Problem to read Numeric data

[TinaNeik]

Hi,

I'm struggling to get my numeric data to read in MVP.

I prepared the numeric format based on the instructions at https://github.com/XiaoleiLiuBio/MVP

It looks like this (Just a portion shown here. The total number of row/SNP is 52157 and total number of column/individual is 68), no header and row names:

NaN | NaN | NaN | NaN | NaN | NaN | NaN 0 | 0 | 0 | 0 | 2 | 0 | NaN 2 | 2 | 2 | 2 | 2 | 2 | 0 2 | 2 | 2 | 2 | 2 | 2 | 2 2 | 0 | 2 | 2 | 2 | 0 | 0 0 | NaN | 0 | 0 | 0 | 2 | 2 NaN | NaN | NaN | NaN | NaN | NaN | NaN 0 | 0 | 0 | 0 | 0 | 0 | 0

I understand that there shouldn't be NaN or missing data in numeric format. I don't know how to overcome this. Could you please help me?

I also got this error:

Error in big.matrix(nrow = numRows, ncol = createCols, type = type, dimnames = list(rowNames, : A big.matrix must have at least one row and one column

I used this code to try to fix:

fileNum=matrix(1:3546676,ncol=68)

But still got the same error.

May I ask what should I do?

Appreciate your help.

Many thanks.

Regards, Tina

[hyacz]

Hi, Tina

First, you should do quality control and impute before you do GWAS.

your numeric file is separated by ' | ', you need to replace it with tab. I always recommend using a text editor (VSCode, NotePad++, or whatever you like) to format your data when dealing with small data.

Then delete the mark with too high the miss rate(>10%) and the mark with the low MAF value(<0.05), and then impute it to make sure there is no missing in your genotype file.

I usually do quality control and imputation from files in Plink format or VCF format. I don't know if there is a tool to do quality control and impute on data in numeric format. There are two functions MVP.Data.impute and MVP.Data.QC in the devel branch, you can find a way to read your file into R, then refer to these two functions.

https://github.com/XiaoleiLiuBio/MVP/blob/370bf6bab733f03b970dc6a33c83850f43fbabd6/R/MVP.Data.r#L501 https://github.com/XiaoleiLiuBio/MVP/blob/370bf6bab733f03b970dc6a33c83850f43fbabd6/R/MVP.Data.r#L552

Haohao

[TinaNeik]

Hi Haohao,

Many thanks for the suggestions, really appreciate it. Yes, I will do the filtering and QC.

My only concern is that my SNP genotype data is in AA, BB and AB format. I don't have the SNP allele information for each individual at each SNP example CC, TT, AG, AT etc.

I'm manually converting the AA=0, BB=2 and AB=1 based on GAPIT user manual, using Excel. The remaining NN has to be imputed but I don't know how to do that so far, still looking at how to solve this problem.

Thank you very much for your help.

Cheers,

Tina Neik PhD candidate (Batley Lab) School of Biological Sciences (M092) The University of Western Australia Crawley WA 6009

Email: ting.neik@research.uwa.edu.au Mobile: 04112 10 334

On Tue, 6 Nov 2018 at 12:03, haohao notifications@github.com wrote:

Hi, Tina

First, you should do quality control and impute before you do GWAS.

your numeric file is separated by ' | ', you need to replace it with tab. I always recommend using a text editor (VSCode, NotePad++, or whatever you like) to format your data when dealing with small data.

Then delete the mark with too high the miss rate(>10%) and the mark with the low MAF value(<0.05), and then impute it to make sure there is no missing in your genotype file.

I usually do quality control and imputation from files in Plink format or VCF format. I don't know if there is a tool to do quality control and impute on data in numeric format. There are two functions MVP.Data.impute and MVP.Data.QC in the devel branch, you can find a way to read your file into R, then refer to these two functions.

https://github.com/XiaoleiLiuBio/MVP/blob/370bf6bab733f03b970dc6a33c83850f43fbabd6/R/MVP.Data.r#L501

https://github.com/XiaoleiLiuBio/MVP/blob/370bf6bab733f03b970dc6a33c83850f43fbabd6/R/MVP.Data.r#L552

Haohao

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[hyacz]

Hi, Tina

Sorry to reply to you so late. I recommend that you convert the original file to a format supported by Plink, such as .ped and .map(see https://www.cog-genomics.org/plink2/formats), so that you can use more software to do data preprocessing.

I show you my qc and impute practice.

1. qc with plink

   plink --file <your_prefix> --geno 0.1 --maf 0.05 --recode vcf --out mydata.qc

2. impute with beagle

   java -Xmx8g -jar beagle.28Sep18.793.jar gt=mydata.qc.vcf out=mydata.qc.imp

   you can download beagle from https://faculty.washington.edu/browning/beagle/beagle.html

3. qc with plink again

   plink --vcf mydata.qc.imp --maf 0.05 --make-bed --out mydata.clean

Then you get the clean data in bfile format, MVP can use it.

If there is anything I have not stated, you can point out that we can discuss it again.

Haohao

[TinaNeik]

Many thanks, Haohao for the suggestions. I will try them out and let you know if it works.

Meanwhile, I also see that some people say we shouldn't be doing filtering before imputation, like here:

"We recommend that you run imputation before stringent filtering " https://groups.google.com/forum/#!topic/tassel/GUjzv78oEoo

"Additionally, you shouldn't be filtering before running find haplotypes. All of the sites are informative, and should be included." https://groups.google.com/forum/#!topic/tassel/7ve-vcv42z0

From the same person though.

May I ask, what are your thoughts on these?

Thank you.

Cheers,

Tina Neik PhD candidate (Batley Lab) School of Biological Sciences (M092) The University of Western Australia Crawley WA 6009

Email: ting.neik@research.uwa.edu.au Mobile: 04112 10 334

On Wed, 7 Nov 2018 at 14:08, haohao notifications@github.com wrote:

Hi, Tina

Sorry to reply to you so late. I recommend that you convert the original file to a format supported by Plink, such as .ped and .map(see https://www.cog-genomics.org/plink2/formats), so that you can use more software to do data preprocessing.

I show you my qc and impute practice.

1. qc with plink

plink --file --geno 0.1 --maf 0.05 --recode vcf --out mydata.qc

1. impute with beagle

java -Xmx8g -jar beagle.28Sep18.793.jar gt=mydata.qc.vcf out=mydata.qc.imp

you can download beagle from https://faculty.washington.edu/browning/beagle/beagle.html

1. qc with plink again

plink --vcf mydata.qc.imp --maf 0.05 --make-bed --out mydata.clean

Then you get the clean data in bfile format, MVP can use it.

If there is anything I have not stated, you can point out that we can discuss it again.

Haohao

— You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub https://github.com/XiaoleiLiuBio/MVP/issues/28#issuecomment-436515070, or mute the thread https://github.com/notifications/unsubscribe-auth/Al8lkOjA6l9o1N0z3r9wg_9DPI33gGyPks5usnj1gaJpZM4YOb4u .

[TinaNeik]

Hi Haohao,

First of all, I can't seem to find a way to convert my SNP data (a portion attached in this email) into .ped and .map format. I think it is because I have AA, BB and AB as my genotype instead of AT, CG etc.

Unfortunately, I don't have access to the original SNP data (AT, CG etc).

I tried to use TASSEL to convert the attached SNP data into .ped and .map format but it cannot recognise it as a genotype table.

Do you think you have a way to solve this problem?

I would appreciate if you could help me here, with this first step.

Thank you very much, in advance.

Cheers,

Tina Neik PhD candidate (Batley Lab) School of Biological Sciences (M092) The University of Western Australia Crawley WA 6009

Email: ting.neik@research.uwa.edu.au Mobile: 04112 10 334

On Wed, 7 Nov 2018 at 14:58, Ting Neik ting.neik@research.uwa.edu.au wrote:

Many thanks, Haohao for the suggestions. I will try them out and let you know if it works.

Meanwhile, I also see that some people say we shouldn't be doing filtering before imputation, like here:

"We recommend that you run imputation before stringent filtering " https://groups.google.com/forum/#!topic/tassel/GUjzv78oEoo

"Additionally, you shouldn't be filtering before running find haplotypes. All of the sites are informative, and should be included." https://groups.google.com/forum/#!topic/tassel/7ve-vcv42z0

From the same person though.

May I ask, what are your thoughts on these?

Thank you.

Cheers,

Tina Neik PhD candidate (Batley Lab) School of Biological Sciences (M092) The University of Western Australia Crawley WA 6009

Email: ting.neik@research.uwa.edu.au Mobile: 04112 10 334

On Wed, 7 Nov 2018 at 14:08, haohao notifications@github.com wrote:

Hi, Tina

Sorry to reply to you so late. I recommend that you convert the original file to a format supported by Plink, such as .ped and .map(see https://www.cog-genomics.org/plink2/formats), so that you can use more software to do data preprocessing.

I show you my qc and impute practice.

1. qc with plink

plink --file --geno 0.1 --maf 0.05 --recode vcf --out mydata.qc

1. impute with beagle

java -Xmx8g -jar beagle.28Sep18.793.jar gt=mydata.qc.vcf out=mydata.qc.imp

you can download beagle from https://faculty.washington.edu/browning/beagle/beagle.html

1. qc with plink again

plink --vcf mydata.qc.imp --maf 0.05 --make-bed --out mydata.clean

Then you get the clean data in bfile format, MVP can use it.

If there is anything I have not stated, you can point out that we can discuss it again.

Haohao

— You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub https://github.com/XiaoleiLiuBio/MVP/issues/28#issuecomment-436515070, or mute the thread https://github.com/notifications/unsubscribe-auth/Al8lkOjA6l9o1N0z3r9wg_9DPI33gGyPks5usnj1gaJpZM4YOb4u .

[hyacz]

Hi, Tina

I think it's hard to find the optimal threshold for quality control, depending on your propensity to analyze. Often you need to try multiple thresholds when analyzing. I usually use a slightly stricter quality control. If I don't detect the signal, I will relax the quality control condition a bit.

1. MAF I usually use --maf 0.01 first, but I noticed that you only have 68 individuals, so I suggest you use strict conditions like --maf 0.05 or --maf 0.1. When a certain type of sample is too small, it cannot represent the population. So the smaller your sample size, the more stringent quality control you need for MAF. And the mark with small MAF value will have low filling accuracy. So I usually filter out these marker before filling.

Bouwman A C, Veerkamp R F. Consequences of splitting whole-genome sequencing effort over multiple breeds on imputation accuracy[J]. BMC Genetics, BioMed Central, 2014, 15(1): 105.

2. missing For the same batch of data, markers with too high a miss rate are usually considered to be of low quality, and are more likely to be filtered out in order to avoid false positives.

In short, I think that the value of quality control conditions depends largely on your data. The more lenient the quality control conditions, the higher the probability of detecting a signal and the higher the false positive.

Haohao

[hyacz]

Can you simply replace 'B' with 'T' to make TASSEL recognize your data? What the original SNP does not affect the analysis of GWAS.

[TinaNeik]

Hi Haohao,

Many thanks for the suggestion.

I tried replacing 'B' with 'T' but still TASSEL cannot recognise my data. Attached is the print screen of the error.

I really don't know what's going on.

Appreciate your input.

Thank you very much in advance.

Cheers,

Tina Neik PhD candidate (Batley Lab) School of Biological Sciences (M092) The University of Western Australia Crawley WA 6009

Email: ting.neik@research.uwa.edu.au Mobile: 04112 10 334

On Thu, 8 Nov 2018 at 02:45, haohao notifications@github.com wrote:

Can you simply replace 'B' with 'T' to make TASSEL recognize your data? What the original SNP does not affect the analysis of GWAS.

— You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub https://github.com/XiaoleiLiuBio/MVP/issues/28#issuecomment-436733948, or mute the thread https://github.com/notifications/unsubscribe-auth/Al8lkINc-KZviqlWpMw2BYOgixyUTYyBks5usypPgaJpZM4YOb4u .

[TinaNeik]

Many thanks, Haohao.

Sorry, I think I didn't frame my question clearly. My question was, do we do QC before imputing the missing SNP?

I read from other discussion that we should first impute the missing SNP, then only do QC.

What are your thoughts?

Thank you.

Cheers,

Tina Neik PhD candidate (Batley Lab) School of Biological Sciences (M092) The University of Western Australia Crawley WA 6009

Email: ting.neik@research.uwa.edu.au Mobile: 04112 10 334

On Thu, 8 Nov 2018 at 02:41, haohao notifications@github.com wrote:

Hi, Tina

I think it's hard to find the optimal threshold for quality control, depending on your propensity to analyze. Often you need to try multiple thresholds when analyzing. I usually use a slightly stricter quality control. If I don't detect the signal, I will relax the quality control condition a bit.

1. MAF I usually use --maf 0.01 first, but I noticed that you only have 68 individuals, so I suggest you use strict conditions like --maf 0.05 or --maf 0.1. When a certain type of sample is too small, it cannot represent the population. So the smaller your sample size, the more stringent quality control you need for MAF. And the mark with small MAF value will have low filling accuracy. So I usually filter out these marker before filling.

[image: image] https://user-images.githubusercontent.com/9551197/48152086-ef0cd900-e2fd-11e8-9fcd-6eaaeeef8ac9.png

Bouwman A C, Veerkamp R F. Consequences of splitting whole-genome sequencing effort over multiple breeds on imputation accuracy[J]. BMC Genetics, BioMed Central, 2014, 15(1): 105.

1. missing For the same batch of data, markers with too high a miss rate are usually considered to be of low quality, and are more likely to be filtered out in order to avoid false positives.

In short, I think that the value of quality control conditions depends largely on your data. The more lenient the quality control conditions, the higher the probability of detecting a signal and the higher the false positive.

Haohao

— You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub https://github.com/XiaoleiLiuBio/MVP/issues/28#issuecomment-436732716, or mute the thread https://github.com/notifications/unsubscribe-auth/Al8lkNhIPhCei7gLIXWiYHVvhhaHIFE_ks5usylzgaJpZM4YOb4u .

[hyacz]

Hi, Tina

Sorry, my bad English make you confused. Regarding whether you should do qc before impute, my answer is "YES, you should to control maf and missing rate before impute."

Because marker with low maf and high missing rate are more error-prone when imputing.

Haohao

[TinaNeik]

Thank you, Haohao.

Do you have any idea how I can overcome the replacement of 'B' with 'T' but still didn't work problem?

Many thanks in advance.

Cheers, Tina

On Thu, 8 Nov 2018 at 13:23, haohao notifications@github.com wrote:

Hi, Tina

Sorry, my bad English make you confused. Regarding whether you should do qc before impute, my answer is "YES, you should to control maf and missing rate before impute."

Because marker with low maf and high missing rate are more error-prone when imputing.

Haohao

— You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub https://github.com/XiaoleiLiuBio/MVP/issues/28#issuecomment-436877069, or mute the thread https://github.com/notifications/unsubscribe-auth/Al8lkGBjmk5WiLtr06w9o2i7NzbVeIADks5us7-4gaJpZM4YOb4u .

[hyacz]

Hi, Tina

If your data is organized into a 0, 1, 2 matrix that can be read directly into R, you can follow the following code to get a set of bfile formatted data accepted by plink.

# get bed file.
> suppressMessages(library(MVP))
> geno <- as.matrix(read.table('data.txt', sep='|'))
> geno
      V1  V2  V3  V4  V5  V6  V7
[1,] NaN NaN NaN NaN NaN NaN NaN
[2,]   0   0   0   0   2   0 NaN
[3,]   2   2   2   2   2   2   0
[4,]   2   2   2   2   2   2   2
[5,]   2   0   2   2   2   0   0
[6,]   0 NaN   0   0   0   2   2
[7,] NaN NaN NaN NaN NaN NaN NaN
[8,]   0   0   0   0   0   0   0
> geno[is.nan(geno)] <- NA
> geno
     V1 V2 V3 V4 V5 V6 V7
[1,] NA NA NA NA NA NA NA
[2,]  0  0  0  0  2  0 NA
[3,]  2  2  2  2  2  2  0
[4,]  2  2  2  2  2  2  2
[5,]  2  0  2  2  2  0  0
[6,]  0 NA  0  0  0  2  2
[7,] NA NA NA NA NA NA NA
[8,]  0  0  0  0  0  0  0
> write_bfile(as.big.matrix(geno)@address, 'mydata')

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
# get fam file.
> pheno <- read.table('pheno.txt', header=T)
> pheno
  ID Pheno1
1  1   51.9
2  2   49.8
3  3   30.1
4  4   45.4
5  5   35.4
6  6   40.2
7  7   49.1
> fam <- cbind(pheno$ID, pheno$ID, 0, 0, 0, pheno$Pheno1)
> write.table(fam, 'mydata.fam', quote = F, row.names = F, col.names = F, sep = '\t')

# get bim file.
> map <- read.table('map.txt', header=T)
> map
  SNP Chr  Pos
1 rs1   1 1001
2 rs2   1 2019
3 rs3   1 3221
4 rs4   1 4400
5 rs5   1 5550
6 rs6   1 6190
7 rs7   1 7990
8 rs8   1 9999
> bim <- cbind(map[, 2], map[, 1], 0, map[, 3], 0, 0)
> write.table(bim, 'mydata.bim', quote = F, row.names = F, col.names = F, sep = '\t')

you will can get plink.bed, plink.fam, plink.bim. now you can use plink --bfile mydata to access your data.

Cheers, Haohao

[TinaNeik]

Thank you very much, Haohao.

I will need some time to test this out and will update you on the progress.

Thank you.

Cheers, Tina

On Thu, 8 Nov 2018 at 17:09, haohao notifications@github.com wrote:

Hi, Tina

If your data is organized into a 0, 1, 2 matrix that can be read directly into R, you can follow the following code to get a set of bfile formatted data accepted by plink.

get bed file.> suppressMessages(library(MVP))> geno <- as.matrix(read.table('data.txt', sep='|'))> geno

  V1  V2  V3  V4  V5  V6  V7

[1,] NaN NaN NaN NaN NaN NaN NaN [2,] 0 0 0 0 2 0 NaN [3,] 2 2 2 2 2 2 0 [4,] 2 2 2 2 2 2 2 [5,] 2 0 2 2 2 0 0 [6,] 0 NaN 0 0 0 2 2 [7,] NaN NaN NaN NaN NaN NaN NaN [8,] 0 0 0 0 0 0 0> geno[is.nan(geno)] <- NA> geno V1 V2 V3 V4 V5 V6 V7 [1,] NA NA NA NA NA NA NA [2,] 0 0 0 0 2 0 NA [3,] 2 2 2 2 2 2 0 [4,] 2 2 2 2 2 2 2 [5,] 2 0 2 2 2 0 0 [6,] 0 NA 0 0 0 2 2 [7,] NA NA NA NA NA NA NA [8,] 0 0 0 0 0 0 0> write_bfile(as.big.matrix(geno)@address, 'plink') 0% 10 20 30 40 50 60 70 80 90 100%|----|----|----|----|----|----|----|----|----|----|***# get fam file.> pheno <- read.table('pheno.txt', header=T)> pheno ID Pheno11 1 51.92 2 49.83 3 30.14 4 45.45 5 35.46 6 40.27 7 49.1> fam <- cbind(pheno$ID, pheno$ID, 0, 0, 0, pheno$Pheno1)> write.table(fam, 'plink.fam', quote = F, row.names = F, col.names = F, sep = '\t')

get bim file.> map <- read.table('map.txt', header=T)> map

SNP Chr Pos1 rs1 1 10012 rs2 1 20193 rs3 1 32214 rs4 1 44005 rs5 1 55506 rs6 1 61907 rs7 1 79908 rs8 1 9999> bim <- cbind(map[, 2], map[, 1], 0, map[, 3], 0, 0)> write.table(bim, paste0(out, '.bim'), quote = F, row.names = F, col.names = F, sep = '\t')

you will can get plink.bed, plink.fam, plink.bim. now you can use plink --bfile plink to access your data.

Cheers, Haohao

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[TinaNeik]

Hi Haohao,

I tried MVP again using my original SNP genotype data in AA, AB and BB format and it works! It can be read in MVP and I think MVP will do the numeric conversion automatically because I see this: [1] "Output numeric genotype..."

First, may I ask why is there an error at the end of the run? What does it mean?

Rerun with Debug Error in filebacked.big.matrix(nrow = nrow, ncol = ncol, type = type, : A big.matrix must have at least one row and one column

Second, why is the number of individuals reduced from 68 to 65?

It became like this in Rlm7mvp.hmp.phe:

Taxa Rlm7 X X.1 X.2 X.3

Darmor 4 NA NA NA NA

AgSpectrum 4 NA NA NA NA

Yudal 4 NA NA NA NA

Surpass400_024DH 4 NA NA NA NA

Argyle 4 NA NA NA NA

Taparoo 4 NA NA NA NA

Skipton 4 NA NA NA NA

Monty_028DH 4 NA NA NA NA

Dunkeld 4 NA NA NA NA

Telfer 4 NA NA NA NA

While it should be like this in the original data Phenotype_Rlm7_68LinesAll.txt:

Taxa Rlm7

Atenzo_1 4

Atenzo_2 4

Atenzo_3 1

Atenzo_4 1

DKExpertise_1 1

DKExpertise_2 1

Andesson_1 1

DKExstorm_1 1

Excel_1 1

Anisse_1 1

Any suggestions will be most appreciated. Thank you very much.

MVP.Data(fileHMP = "~/Dropbox/Rlm7_SNP_68lines_forNumericAll_forTASSEL.hmp.txt", filePhe = "~/Dropbox/Phenotype_Rlm7_68LinesAll.txt", sep.hmp = "\t", sep.phe = "\t", SNP.effect="Add", fileKin = FALSE, filePC = FALSE, out = "Rlm7mvp.hmp") [1] "Preparing data for MVP..." [1] "Preparation for PHENOTYPE data is done!" [1] "Output numeric genotype..." [1] "File: ~/Dropbox/Rlm7_SNP_68lines_forNumericAll_forTASSEL.hmp.txt ; Total markers: 52099 finished!" [1] "Preparation for numeric data is done!" [1] "Output mvp genotype..." [1] "MVP data prepration accomplished successfully!" genotype <- attach.big.matrix("Rlm7mvp.hmp.geno.desc") phenotype <- read.table("Rlm7mvp.hmp.phe",head=TRUE) map <- read.table("Rlm7mvp.hmp.map" , head = TRUE) for(i in 2:ncol(phenotype)){

• imMVP <- MVP(
• phe=phenotype[, c(1, i)],
• geno=genotype,
• map=map,

• K=Kinship,

• CV.GLM=Covariates,

• CV.MLM=Covariates,

• CV.FarmCPU=Covariates,

• nPC.GLM=3,
• nPC.MLM=3,
• nPC.FarmCPU=3,
• perc=1,
• priority="speed",
• ncpus=10,
• vc.method="EMMA",
• maxLoop=10,
• method.bin="FaST-LMM",#"FaST-LMM","EMMA", "static"

• permutation.threshold=TRUE,

• permutation.rep=100,

• threshold=0.05,
• method=c("GLM", "MLM")
• )}

  --------------------------------------------Welcome to

  MVP---------------------------------------------#

  A Memory-efficient, Visualization-enhanced, and

  Parallel-accelerated Tool For GWAS #

  Version: 1.0.1

  #

  Authors: Lilin Yin, Haohao Zhang, Zhiwu Zhang, Xinyun Li, Xiaohui Yuan,

  Shuhong Zhao, and Xiaolei Liu #

  Contact: xiaoleiliu@mail.hzau.edu.cn

  #

  -------------------------------------------------------------------------------------------------------

  [1] "Input data has 63 individuals, 52099 markers" [1] "Principal Component Analysis Start..." means for first 10 snps: [1] 0 0 0 0 0 0 0 0 0 0 [1] "GWAS Start..." [1] "General Linear Model (GLM) Start ..."

[1] "Mixed Linear Model (MLM) Start ..." [1] "Calculating Kinship..." [1] "Variance components..." [1] "Variance Components Estimation is Done!" [1] "Eigen-Decomposition..." [1] "Eigen-Decomposition is Done!"

[1] "Significance Level: 9.59711318835294e-07" [1] "Visualization Start..." [1] "Phenotype distribution Plotting..." [1] "PCA plot2d..." [1] "PCA plot3d..." Writing 'MVP.3D090.png' Loading required namespace: magick Writing 'MVP.3D045.png' [1] "SNP_Density Plotting..." [1] "Circular_Manhattan Plotting Rlm7.GLM..." [1] "Circular_Manhattan Plotting Rlm7.MLM..." [1] "Rectangular_Manhattan Plotting Rlm7.GLM..." [1] "Rectangular_Manhattan Plotting Rlm7.MLM..." [1] "Q_Q Plotting Rlm7.GLM..." [1] "Q_Q Plotting Rlm7.MLM..." [1] "Multracks_Rectangular Plotting Rlm7.GLM..." [1] "Multracks_Rectangular Plotting Rlm7.MLM..." [1] "Multraits_Rectangular Plotting..." [1] "Multracks_QQ Plotting Rlm7.GLM..." [1] "Multracks_QQ Plotting Rlm7.MLM..." [1] "Multraits_QQ Plotting Rlm7.GLM..." [1] "Multraits_QQ Plotting Rlm7.MLM..."

-------------------------------------------MVP

ACCOMPLISHED--------------------------------------------#

--------------------------------------------Welcome to

MVP---------------------------------------------#

A Memory-efficient, Visualization-enhanced, and

Parallel-accelerated Tool For GWAS #

Version: 1.0.1

                         #

Authors: Lilin Yin, Haohao Zhang, Zhiwu Zhang, Xinyun Li, Xiaohui Yuan,

Shuhong Zhao, and Xiaolei Liu #

Contact: xiaoleiliu@mail.hzau.edu.cn

                         #

-------------------------------------------------------------------------------------------------------

Show Traceback

Rerun with Debug Error in filebacked.big.matrix(nrow = nrow, ncol = ncol, type = type, : A big.matrix must have at least one row and one column

Cheers,

Tina Neik PhD candidate (Batley Lab) School of Biological Sciences (M092) The University of Western Australia Crawley WA 6009

Email: ting.neik@research.uwa.edu.au Mobile: 04112 10 334

On Thu, 8 Nov 2018 at 21:24, Ting Neik ting.neik@research.uwa.edu.au wrote:

Thank you very much, Haohao.

I will need some time to test this out and will update you on the progress.

Thank you.

Cheers, Tina

On Thu, 8 Nov 2018 at 17:09, haohao notifications@github.com wrote:

Hi, Tina

If your data is organized into a 0, 1, 2 matrix that can be read directly into R, you can follow the following code to get a set of bfile formatted data accepted by plink.

get bed file.> suppressMessages(library(MVP))> geno <- as.matrix(read.table('data.txt', sep='|'))> geno

  V1  V2  V3  V4  V5  V6  V7

[1,] NaN NaN NaN NaN NaN NaN NaN [2,] 0 0 0 0 2 0 NaN [3,] 2 2 2 2 2 2 0 [4,] 2 2 2 2 2 2 2 [5,] 2 0 2 2 2 0 0 [6,] 0 NaN 0 0 0 2 2 [7,] NaN NaN NaN NaN NaN NaN NaN [8,] 0 0 0 0 0 0 0> geno[is.nan(geno)] <- NA> geno V1 V2 V3 V4 V5 V6 V7 [1,] NA NA NA NA NA NA NA [2,] 0 0 0 0 2 0 NA [3,] 2 2 2 2 2 2 0 [4,] 2 2 2 2 2 2 2 [5,] 2 0 2 2 2 0 0 [6,] 0 NA 0 0 0 2 2 [7,] NA NA NA NA NA NA NA [8,] 0 0 0 0 0 0 0> write_bfile(as.big.matrix(geno)@address, 'plink') 0% 10 20 30 40 50 60 70 80 90 100%|----|----|----|----|----|----|----|----|----|----|***# get fam file.> pheno <- read.table('pheno.txt', header=T)> pheno ID Pheno11 1 51.92 2 49.83 3 30.14 4 45.45 5 35.46 6 40.27 7 49.1> fam <- cbind(pheno$ID, pheno$ID, 0, 0, 0, pheno$Pheno1)> write.table(fam, 'plink.fam', quote = F, row.names = F, col.names = F, sep = '\t')

get bim file.> map <- read.table('map.txt', header=T)> map

SNP Chr Pos1 rs1 1 10012 rs2 1 20193 rs3 1 32214 rs4 1 44005 rs5 1 55506 rs6 1 61907 rs7 1 79908 rs8 1 9999> bim <- cbind(map[, 2], map[, 1], 0, map[, 3], 0, 0)> write.table(bim, paste0(out, '.bim'), quote = F, row.names = F, col.names = F, sep = '\t')

you will can get plink.bed, plink.fam, plink.bim. now you can use plink --bfile plink to access your data.

Cheers, Haohao

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[TinaNeik]

Hi Haohao,

I followed your script but it didn't work for this:

write_bfile(as.big.matrix(geno)@address, 'plink') Error in write_bfile(as.big.matrix(geno)@address, "plink") : could not find function "write_bfile"

Is it write_file? I can't seem to find this function in R. R has write.big.matrix but I don't think it is this function?

Cheers, Tina

On Thu, 8 Nov 2018 at 17:09, haohao notifications@github.com wrote:

Hi, Tina

If your data is organized into a 0, 1, 2 matrix that can be read directly into R, you can follow the following code to get a set of bfile formatted data accepted by plink.

get bed file.> suppressMessages(library(MVP))> geno <- as.matrix(read.table('data.txt', sep='|'))> geno

  V1  V2  V3  V4  V5  V6  V7

[1,] NaN NaN NaN NaN NaN NaN NaN [2,] 0 0 0 0 2 0 NaN [3,] 2 2 2 2 2 2 0 [4,] 2 2 2 2 2 2 2 [5,] 2 0 2 2 2 0 0 [6,] 0 NaN 0 0 0 2 2 [7,] NaN NaN NaN NaN NaN NaN NaN [8,] 0 0 0 0 0 0 0> geno[is.nan(geno)] <- NA> geno V1 V2 V3 V4 V5 V6 V7 [1,] NA NA NA NA NA NA NA [2,] 0 0 0 0 2 0 NA [3,] 2 2 2 2 2 2 0 [4,] 2 2 2 2 2 2 2 [5,] 2 0 2 2 2 0 0 [6,] 0 NA 0 0 0 2 2 [7,] NA NA NA NA NA NA NA [8,] 0 0 0 0 0 0 0> write_bfile(as.big.matrix(geno)@address, 'plink') 0% 10 20 30 40 50 60 70 80 90 100%|----|----|----|----|----|----|----|----|----|----|***# get fam file.> pheno <- read.table('pheno.txt', header=T)> pheno ID Pheno11 1 51.92 2 49.83 3 30.14 4 45.45 5 35.46 6 40.27 7 49.1> fam <- cbind(pheno$ID, pheno$ID, 0, 0, 0, pheno$Pheno1)> write.table(fam, 'plink.fam', quote = F, row.names = F, col.names = F, sep = '\t')

get bim file.> map <- read.table('map.txt', header=T)> map

SNP Chr Pos1 rs1 1 10012 rs2 1 20193 rs3 1 32214 rs4 1 44005 rs5 1 55506 rs6 1 61907 rs7 1 79908 rs8 1 9999> bim <- cbind(map[, 2], map[, 1], 0, map[, 3], 0, 0)> write.table(bim, paste0(out, '.bim'), quote = F, row.names = F, col.names = F, sep = '\t')

you will can get plink.bed, plink.fam, plink.bim. now you can use plink --bfile plink to access your data.

Cheers, Haohao

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[hyacz]

Hi, Tina

Yes, MVP can read data from hapmap format, it should work fine.

First, may I ask why is there an error at the end of the run? What does it mean?

This problem seems to be caused by the separator. You originally had only one phenotype, but after the conversion, 4 were generated. Rlm7 has been successfully executed. The second phenotype is all NA, so the program has an error when generating an empty big.matrix.

why is the number of individuals reduced from 68 to 65

Please check your Rlm7mvp.hmp.phe file. The Rlm7 column may have several missing values and the missing values will be deleted.

Is it write_file?

write_bfile is a function in the MVP development version that you can download from the devel branch in Github.

Cheers, Haohao

[TinaNeik]

Thanks Haohao.

First, I managed to fix the separator problem. Thank you for this tip. Now the phenotype file is correct with only one phenotype that is Rlm7 with 2 missing values. So I would expect 66 individuals. How come It is showing 61 invididuals with a new error:

Error in SetElements.bm(x, i, j, value) : Matrix dimensions do not agree with big.matrix instance set size.

--------------------------------------------Welcome to

MVP---------------------------------------------#

A Memory-efficient, Visualization-enhanced, and

Parallel-accelerated Tool For GWAS #

Version: 1.0.1

                         #

Authors: Lilin Yin, Haohao Zhang, Zhiwu Zhang, Xinyun Li, Xiaohui Yuan,

Shuhong Zhao, and Xiaolei Liu #

Contact: xiaoleiliu@mail.hzau.edu.cn

                         #

-------------------------------------------------------------------------------------------------------

[1] "Input data has 61 individuals, 52099 markers" [1] "Principal Component Analysis Start..." means for first 10 snps: [1] 0 0 0 0 0 0 0 0 0 0 Show Traceback

Rerun with Debug Error in SetElements.bm(x, i, j, value) : Matrix dimensions do not agree with big.matrix instance set size.

Second, how come the .hmp.numeric output is different each time I run MVP?

Please see attached screenshot of Rlm7 and Rlm7A.hmp.numeric files

Thank you again in advance for your help.

Cheers, Tina

On Fri, 9 Nov 2018 at 11:56, haohao notifications@github.com wrote:

Hi, Tina

Yes, MVP can read data from hapmap format, it should work fine.

First, may I ask why is there an error at the end of the run? What does it mean?

This problem seems to be caused by the separator. You originally had only one phenotype, but after the conversion, 4 were generated. Rlm7 has been successfully executed. The second phenotype is all NA, so the program has an error when generating an empty big.matrix.

why is the number of individuals reduced from 68 to 65

Please check your Rlm7mvp.hmp.phe file. The Rlm7 column may have several missing values and the missing values will be deleted.

Is it write_file? write_bfile is a function in the MVP development version that you can download from the devel branch in Github.

Cheers, Haohao

— You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub https://github.com/XiaoleiLiuBio/MVP/issues/28#issuecomment-437242200, or mute the thread https://github.com/notifications/unsubscribe-auth/Al8lkNtW8zb2M_l5X9m7RMIxxTiuFrxxks5utP0GgaJpZM4YOb4u .

[TinaNeik]

Hi Haohao,

I tried to re-run MVP, this time imputing the phenotype data for the two individuals in the phenotype file, so I have complete 68 phenotype data and 68 genotype data with no missing data.

This time there are 63 individuals. May I ask, where did the 5 individuals go?

Also, a new error exists:

Warning messages: 1: In hist.default(phe[, i], plot = FALSE, breaks = Breaks, xlab = "", : arguments 'freq', 'col', 'main', 'xlab', 'ylab', '...' are not made use of 2: In dir.create("PCA plot3D") : 'PCA plot3D' already exists

What does this error mean?

Good thing is that now the .hmp.numeric is consistent with the Rlm7A one (see attached Rlm7B). I think the .hmp.numeric data is influenced by the phenotype data because once I corrected the separator in the phenotype data as I did in my previous email, the .hmp.numeric data also changed as shown in the previous screenshots Rlm7 and Rlm7A. Now Rlm7B is the same with Rlm7A.

--------------------------------------------Welcome to

MVP---------------------------------------------#

A Memory-efficient, Visualization-enhanced, and

Parallel-accelerated Tool For GWAS #

Version: 1.0.1

                         #

Authors: Lilin Yin, Haohao Zhang, Zhiwu Zhang, Xinyun Li, Xiaohui Yuan,

Shuhong Zhao, and Xiaolei Liu #

Contact: xiaoleiliu@mail.hzau.edu.cn

                         #

-------------------------------------------------------------------------------------------------------

[1] "Input data has 63 individuals, 52099 markers" [1] "Principal Component Analysis Start..." means for first 10 snps: [1] 0 0 0 0 0 0 0 0 0 0 [1] "GWAS Start..." [1] "General Linear Model (GLM) Start ..."

[1] "Mixed Linear Model (MLM) Start ..." [1] "Calculating Kinship..." [1] "Variance components..." [1] "Variance Components Estimation is Done!" [1] "Eigen-Decomposition..." [1] "Eigen-Decomposition is Done!"

[1] "Significance Level: 9.59711318835294e-07" [1] "Visualization Start..." [1] "Phenotype distribution Plotting..." [1] "PCA plot2d..." [1] "PCA plot3d..." Writing 'MVP.3D090.png' Writing 'MVP.3D045.png' [1] "SNP_Density Plotting..." [1] "Circular_Manhattan Plotting Rlm7.GLM..." [1] "Circular_Manhattan Plotting Rlm7.MLM..." [1] "Rectangular_Manhattan Plotting Rlm7.GLM..." [1] "Rectangular_Manhattan Plotting Rlm7.MLM..." [1] "Q_Q Plotting Rlm7.GLM..." [1] "Q_Q Plotting Rlm7.MLM..." [1] "Multracks_Rectangular Plotting Rlm7.GLM..." [1] "Multracks_Rectangular Plotting Rlm7.MLM..." [1] "Multraits_Rectangular Plotting..." [1] "Multracks_QQ Plotting Rlm7.GLM..." [1] "Multracks_QQ Plotting Rlm7.MLM..." [1] "Multraits_QQ Plotting Rlm7.GLM..." [1] "Multraits_QQ Plotting Rlm7.MLM..."

-------------------------------------------MVP

ACCOMPLISHED--------------------------------------------# Warning messages: 1: In hist.default(phe[, i], plot = FALSE, breaks = Breaks, xlab = "", : arguments 'freq', 'col', 'main', 'xlab', 'ylab', '...' are not made use of 2: In dir.create("PCA plot3D") : 'PCA plot3D' already exists

Cheers, Tina

On Fri, 9 Nov 2018 at 12:24, Ting Neik ting.neik@research.uwa.edu.au wrote:

Thanks Haohao.

First, I managed to fix the separator problem. Thank you for this tip. Now the phenotype file is correct with only one phenotype that is Rlm7 with 2 missing values. So I would expect 66 individuals. How come It is showing 61 invididuals with a new error:

Error in SetElements.bm(x, i, j, value) : Matrix dimensions do not agree with big.matrix instance set size.

--------------------------------------------Welcome to

MVP---------------------------------------------#

A Memory-efficient, Visualization-enhanced, and

Parallel-accelerated Tool For GWAS #

Version: 1.0.1

                         #

Authors: Lilin Yin, Haohao Zhang, Zhiwu Zhang, Xinyun Li, Xiaohui Yuan,

Shuhong Zhao, and Xiaolei Liu #

Contact: xiaoleiliu@mail.hzau.edu.cn

                           #

-------------------------------------------------------------------------------------------------------

[1] "Input data has 61 individuals, 52099 markers" [1] "Principal Component Analysis Start..." means for first 10 snps: [1] 0 0 0 0 0 0 0 0 0 0 Show Traceback

Rerun with Debug Error in SetElements.bm(x, i, j, value) : Matrix dimensions do not agree with big.matrix instance set size.

Second, how come the .hmp.numeric output is different each time I run MVP?

Please see attached screenshot of Rlm7 and Rlm7A.hmp.numeric files

Thank you again in advance for your help.

Cheers, Tina

On Fri, 9 Nov 2018 at 11:56, haohao notifications@github.com wrote:

Hi, Tina

Yes, MVP can read data from hapmap format, it should work fine.

First, may I ask why is there an error at the end of the run? What does it mean?

This problem seems to be caused by the separator. You originally had only one phenotype, but after the conversion, 4 were generated. Rlm7 has been successfully executed. The second phenotype is all NA, so the program has an error when generating an empty big.matrix.

why is the number of individuals reduced from 68 to 65

Please check your Rlm7mvp.hmp.phe file. The Rlm7 column may have several missing values and the missing values will be deleted.

Is it write_file? write_bfile is a function in the MVP development version that you can download from the devel branch in Github.

Cheers, Haohao

— You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub https://github.com/XiaoleiLiuBio/MVP/issues/28#issuecomment-437242200, or mute the thread https://github.com/notifications/unsubscribe-auth/Al8lkNtW8zb2M_l5X9m7RMIxxTiuFrxxks5utP0GgaJpZM4YOb4u .

[hyacz]

Hi, Tina

where did the 5 individuals go?

Individuals with phenotype or genotype deletion will be filtered, and if your phenotype is ok, check your genotype file.

What does this error mean?

These are just some warnings, not errors. Some parameters are ignored when plotting the phenotype distribution plot, and you have ever run MVP so the folder for the PCA 3D plot already exists.

Haohao

[TinaNeik]

Thank you, Haohao.

I apologise for the all the long questions. I just can't get it. Really appreciate all your help and support and of course, patience. Thank you very much.

My genotype file is a normal .hmp.txt file, has all 68 individuals, with either AA, AB, or BB genotype and missing calls represented as NN. The X.hmp.geno.desc file shows:

new("big.matrix.descriptor", description = list(sharedType = "FileBacked",

filename = "Rlm7B.hmp.geno.bin", dirname = "/Users/tinaneik/",

totalRows = 52099L, totalCols = 63L, rowOffset = c(0, 52099

), colOffset = c(0, 63), nrow = 52099, ncol = 63, rowNames = NULL,

colNames = NULL, type = "char", separated = FALSE))

I check again the genotype data but can't find anything unusual. I just added '#' to the 'rs' in the first column and did the same to the 6th column 'assembly'. And this time, I get a different X.hmp.geno.desc output:

new("big.matrix.descriptor", description = list(sharedType = "FileBacked",

filename = "Rlm7C.hmp.geno.bin", dirname = "/Users/tinaneik/",

totalRows = 52099L, totalCols = 65L, rowOffset = c(0, 52099

), colOffset = c(0, 65), nrow = 52099, ncol = 65, rowNames = NULL,

colNames = NULL, type = "char", separated = FALSE))

May I ask what is happening? What did MVP filter from the totalCols that I can't get full 68 individuals? They look alright to me in the genotype file.

Appreciate your help here. Thank you so much.

Cheers, Tina

On Fri, 9 Nov 2018 at 16:13, haohao notifications@github.com wrote:

Hi, Tina

where did the 5 individuals go?

Individuals with phenotype or genotype deletion will be filtered, and if your phenotype is ok, check your genotype file.

What does this error mean?

These are just some warnings, not errors. Some parameters are ignored when plotting the phenotype distribution plot, and you have ever run MVP so the folder for the PCA 3D plot already exists.

Haohao

— You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub https://github.com/XiaoleiLiuBio/MVP/issues/28#issuecomment-437283024, or mute the thread https://github.com/notifications/unsubscribe-auth/Al8lkMXZab6BE2LjcaGHkYlBUnEl7W5Gks5utTkkgaJpZM4YOb4u .

[hyacz]

Hi, Tina

Sorry to reply to your message so late. We are very happy that you use our software and we will improve the way MVP works to reduce the occurrence of these problems. Please feel free to ask your question.

From your reply, your original file may not be fully supported by MVP. I am not sure what went wrong. Could you please share data files with me? I will check it and tell you why the MVP is not working properly.

Haohao Email: haohaozhang@whut.edu.cn

[TinaNeik]

I think the files were too big. I attached the compressed version here:

https://www.dropbox.com/s/eq1awhj87nfi9s8/Archive.zip?dl=0

Please let me know if you can access them?

Thank you.

Cheers, Tina

On Sun, 11 Nov 2018 at 20:37, Ting Neik ting.neik@research.uwa.edu.au wrote:

Thanks, Haohao.

Here are the files. Many thanks for the help.

Hope we manage to find out the problem.

Hear from you soon, thank you,

Cheers, Tina

On Sun, 11 Nov 2018 at 15:11, haohao notifications@github.com wrote:

Hi, Tina

Sorry to reply to your message so late. We are very happy that you use our software and we will improve the way MVP works to reduce the occurrence of these problems. Please feel free to ask your question.

From your reply, your original file may not be fully supported by MVP. I am not sure what went wrong. Could you please share data files with me? I will check it and tell you why the MVP is not working properly.

Haohao Email: haohaozhang@whut.edu.cn

— You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub https://github.com/XiaoleiLiuBio/MVP/issues/28#issuecomment-437648782, or mute the thread https://github.com/notifications/unsubscribe-auth/Al8lkKllr_3UMpdaZnySQNHl5X5yMWtHks5ut82ngaJpZM4YOb4u .

[hyacz]

Hi, Tina I have received your data, but I have time to check this data this afternoon and I will reply you today.

Haohao

[hyacz]

Hi, Tina

I have checked the data, as you said there is nothing unusual here. And then I rearranged the data with the development version of MVP and got the expected results.(see bellow log) The MVP.Data module was upgraded in the development version, I guess there are some bugs in the previous version. If I find the bug, I will let you know.

This is the result: ForTina.zip You can continue your analysis with the mvp.imp.* files in the archive.

And log:

> suppressMessages(library(MVP))
> MVP.Data(fileHMP='Rlm7_SNP_68lines_forNumericAll_forTASSEL_ForHaohao.hmp.txt',
+ filePhe='Phenotype_Rlm7_68LinesAllA_ForHaohao.txt')
Preparing data for MVP...
Reading file...
inds: 68    markers:52099

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Preparation for GENOTYPE data is done within 1s
Preparation for Genotype File is done!
Preparation for PHENOTYPE data is Done within 0s
Imputing...
Impute Genotype File is done!
[1] "Principal Component Analysis Start..."
Preparation for PC matrix is done!
Calculate KINSHIP using Vanraden method...
Preparation for Kinship matrix is done!
MVP data prepration accomplished successfully!

Haohao

[TinaNeik]

Many thanks, Haohao.

What do I do if I want to run another set of data? Do I uninstall my current MVP package? Where can I get the development version of MVP to re-install? Is this the latest version?

Thank you.

Cheers, Tina

On Mon, 12 Nov 2018 at 15:32, haohao notifications@github.com wrote:

Hi, Tina

I have checked the data, as you said there is nothing unusual here. And then I rearranged the data with the development version of MVP and got the expected results.(see bellow log) The MVP.Data module was upgraded in the development version, I guess there are some bugs in the previous version. If I find the bug, I will let you know.

This is the result: ForTina.zip https://github.com/XiaoleiLiuBio/MVP/files/2570892/ForTina.zip You can continue your analysis with the mvp.imp.* files in the archive.

And log:

suppressMessages(library(MVP)) MVP.Data(fileHMP='Rlm7_SNP_68lines_forNumericAll_forTASSEL_ForHaohao.hmp.txt',

• filePhe='Phenotype_Rlm7_68LinesAllA_ForHaohao.txt') Preparing data for MVP... Reading file... inds: 68 markers:52099

0% 10 20 30 40 50 60 70 80 90 100% |----|----|----|----|----|----|----|----|----|----|

---

Preparation for GENOTYPE data is done within 1s Preparation for Genotype File is done! Preparation for PHENOTYPE data is Done within 0s Imputing... Impute Genotype File is done! [1] "Principal Component Analysis Start..." Preparation for PC matrix is done! Calculate KINSHIP using Vanraden method... Preparation for Kinship matrix is done! MVP data prepration accomplished successfully!

Haohao

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[hyacz]

Hi, Tina

Yes, the latest version of MVP can be downloaded from Github's devel branch. It is unstable and we may have some updates in a few days.

You can install it as follows

1. Download https://github.com/XiaoleiLiuBio/MVP/archive/devel.zip
2. Start R input:

   devtools::install_local("MVP-devel")

Haohao

[TinaNeik]

Thanks, Haohao.

Can you please let me know when it is stable and ready to be downloaded and use?

Thank you.

Cheers,

On Mon, 12 Nov 2018 at 18:26, haohao notifications@github.com wrote:

Hi, Tina

Yes, the latest version of MVP can be downloaded from Github's devel branch. It is unstable and we may have some updates in a few days.

You can install it as follows

1. Download https://github.com/XiaoleiLiuBio/MVP/archive/devel.zip
2. Start R input:

devtools::install_local("MVP-devel")

Haohao

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[hyacz]

Hi, Tina

We've planned a lot of things to do in the MVP 2.0 release, including some new models, more complete fault tolerance mechanisms, and more visualization. It may be released early next year. We will be very grateful for any suggestions.

Cheers, Haohao

[TinaNeik]

Hi Haohao,

I followed your instructions to install "MVP-devel" on my MacBook High Sierra but failed.

Do you think it would be easier to fix the bug in the previous version of MVP so I can still run it, or is it easier to fix the installation of MVP-devel below? I'm stuck.

Appreciate your help.

Thank you.

Cheers, Tina

devtools::install_local("MVP-devel") Installing MVP '/Library/Frameworks/R.framework/Resources/bin/R' --no-site-file --no-environ \ --no-save --no-restore --quiet CMD INSTALL \

'/private/var/folders/qd/1n0yvng123n1sf9cmbzvsf6r0000gn/T/RtmpztBGrb/file46f230031bb/MVP-devel' \

--library='/Library/Frameworks/R.framework/Versions/3.5/Resources/library' \ --install-tests

• installing source package ‘MVP’ ... ** libs clang++ -std=gnu++11 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/Rcpp/include" -I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/RcppProgress/include" -I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/BH/include" -I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/bigmemory/include" -I/usr/local/include -fopenmp -fPIC -Wall -g -O2 -c RcppExports.cpp -o RcppExports.o clang: error: unsupported option '-fopenmp' make: *** [RcppExports.o] Error 1 ERROR: compilation failed for package ‘MVP’
• removing ‘/Library/Frameworks/R.framework/Versions/3.5/Resources/library/MVP’
• restoring previous ‘/Library/Frameworks/R.framework/Versions/3.5/Resources/library/MVP’ Installation failed: Command failed (1)

Cheers,

Tina Neik PhD candidate (Batley Lab) School of Biological Sciences (M092) The University of Western Australia Crawley WA 6009

Email: ting.neik@research.uwa.edu.au Mobile: 04112 10 334

On Mon, 12 Nov 2018 at 18:26, haohao notifications@github.com wrote:

Hi, Tina

Yes, the latest version of MVP can be downloaded from Github's devel branch. It is unstable and we may have some updates in a few days.

You can install it as follows

1. Download https://github.com/XiaoleiLiuBio/MVP/archive/devel.zip
2. Start R input:

devtools::install_local("MVP-devel")

Haohao

— You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub https://github.com/XiaoleiLiuBio/MVP/issues/28#issuecomment-437829562, or mute the thread https://github.com/notifications/unsubscribe-auth/Al8lkNaG7wR4IzdgiZu-p3feucA2B3xNks5uuUzBgaJpZM4YOb4u .

[TinaNeik]

Hi Haohao,

I managed to solve the MVP-devel installation problem by changing the ~/.R/Makevars

I tried running MVP-devel. But even this version gave the same problem as before:

new("big.matrix.descriptor", description = list(sharedType = "FileBacked",

filename = "mvp.hmp.geno.bin", dirname = "/Users/tinaneik/",

totalRows = 52099L, totalCols = 65L, rowOffset = c(0, 52099

), colOffset = c(0, 65), nrow = 52099, ncol = 65, rowNames = NULL,

colNames = NULL, type = "char", separated = FALSE))

It should be 68 individuals.

What should I do?

Cheers, Tina

On Mon, 12 Nov 2018 at 22:32, Ting Neik ting.neik@research.uwa.edu.au wrote:

Hi Haohao,

I followed your instructions to install "MVP-devel" on my MacBook High Sierra but failed.

Do you think it would be easier to fix the bug in the previous version of MVP so I can still run it, or is it easier to fix the installation of MVP-devel below? I'm stuck.

Appreciate your help.

Thank you.

Cheers, Tina

devtools::install_local("MVP-devel") Installing MVP '/Library/Frameworks/R.framework/Resources/bin/R' --no-site-file --no-environ \ --no-save --no-restore --quiet CMD INSTALL \

'/private/var/folders/qd/1n0yvng123n1sf9cmbzvsf6r0000gn/T/RtmpztBGrb/file46f230031bb/MVP-devel' \

--library='/Library/Frameworks/R.framework/Versions/3.5/Resources/library' \ --install-tests

• installing source package ‘MVP’ ... ** libs clang++ -std=gnu++11 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/Rcpp/include" -I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/RcppProgress/include" -I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/BH/include" -I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/bigmemory/include" -I/usr/local/include -fopenmp -fPIC -Wall -g -O2 -c RcppExports.cpp -o RcppExports.o clang: error: unsupported option '-fopenmp' make: *** [RcppExports.o] Error 1 ERROR: compilation failed for package ‘MVP’
• removing ‘/Library/Frameworks/R.framework/Versions/3.5/Resources/library/MVP’
• restoring previous ‘/Library/Frameworks/R.framework/Versions/3.5/Resources/library/MVP’ Installation failed: Command failed (1)

Cheers,

Tina Neik PhD candidate (Batley Lab) School of Biological Sciences (M092) The University of Western Australia Crawley WA 6009

Email: ting.neik@research.uwa.edu.au Mobile: 04112 10 334

On Mon, 12 Nov 2018 at 18:26, haohao notifications@github.com wrote:

Hi, Tina

Yes, the latest version of MVP can be downloaded from Github's devel branch. It is unstable and we may have some updates in a few days.

You can install it as follows

1. Download https://github.com/XiaoleiLiuBio/MVP/archive/devel.zip
2. Start R input:

devtools::install_local("MVP-devel")

Haohao

— You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub https://github.com/XiaoleiLiuBio/MVP/issues/28#issuecomment-437829562, or mute the thread https://github.com/notifications/unsubscribe-auth/Al8lkNaG7wR4IzdgiZu-p3feucA2B3xNks5uuUzBgaJpZM4YOb4u .

[TinaNeik]

Got it. The name of the individuals cannot be in character, must be in S1, S2, S3 etc like what I provided for you.

I managed to get 68L, but another error again. Can you please help me?

Thank you.

imMVP <- MVP(

• phe=phenotype,
• geno=genotype,
• map=map,

• K=Kinship,

• CV.GLM=Covariates,

• CV.MLM=Covariates,

• CV.FarmCPU=Covariates,

• nPC.GLM=5,
• nPC.MLM=3,
• nPC.FarmCPU=3,
• perc=1,
• priority="speed",

• ncpus=10,

• vc.method="EMMA",
• maxLoop=10,
• method.bin="FaST-LMM",#"FaST-LMM","EMMA", "static"

• permutation.threshold=TRUE,

• permutation.rep=100,

• threshold=0.05,
• method=c("GLM", "MLM")
• ) Error in MVP.Version(TRUE) : could not find function "MVP.Version"

Cheers, Tina

On Mon, 12 Nov 2018 at 23:30, Ting Neik ting.neik@research.uwa.edu.au wrote:

Hi Haohao,

I managed to solve the MVP-devel installation problem by changing the ~/.R/Makevars

I tried running MVP-devel. But even this version gave the same problem as before:

new("big.matrix.descriptor", description = list(sharedType = "FileBacked",

filename = "mvp.hmp.geno.bin", dirname = "/Users/tinaneik/",

totalRows = 52099L, totalCols = 65L, rowOffset = c(0, 52099

), colOffset = c(0, 65), nrow = 52099, ncol = 65, rowNames = NULL,

colNames = NULL, type = "char", separated = FALSE))

It should be 68 individuals.

What should I do?

Cheers, Tina

On Mon, 12 Nov 2018 at 22:32, Ting Neik ting.neik@research.uwa.edu.au wrote:

Hi Haohao,

I followed your instructions to install "MVP-devel" on my MacBook High Sierra but failed.

Do you think it would be easier to fix the bug in the previous version of MVP so I can still run it, or is it easier to fix the installation of MVP-devel below? I'm stuck.

Appreciate your help.

Thank you.

Cheers, Tina

devtools::install_local("MVP-devel") Installing MVP '/Library/Frameworks/R.framework/Resources/bin/R' --no-site-file --no-environ \ --no-save --no-restore --quiet CMD INSTALL \

'/private/var/folders/qd/1n0yvng123n1sf9cmbzvsf6r0000gn/T/RtmpztBGrb/file46f230031bb/MVP-devel' \

--library='/Library/Frameworks/R.framework/Versions/3.5/Resources/library' \ --install-tests

• installing source package ‘MVP’ ... ** libs clang++ -std=gnu++11 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/Rcpp/include" -I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/RcppProgress/include" -I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/BH/include" -I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/bigmemory/include" -I/usr/local/include -fopenmp -fPIC -Wall -g -O2 -c RcppExports.cpp -o RcppExports.o clang: error: unsupported option '-fopenmp' make: *** [RcppExports.o] Error 1 ERROR: compilation failed for package ‘MVP’
• removing ‘/Library/Frameworks/R.framework/Versions/3.5/Resources/library/MVP’
• restoring previous ‘/Library/Frameworks/R.framework/Versions/3.5/Resources/library/MVP’ Installation failed: Command failed (1)

Cheers,

Tina Neik PhD candidate (Batley Lab) School of Biological Sciences (M092) The University of Western Australia Crawley WA 6009

Email: ting.neik@research.uwa.edu.au Mobile: 04112 10 334

On Mon, 12 Nov 2018 at 18:26, haohao notifications@github.com wrote:

Hi, Tina

Yes, the latest version of MVP can be downloaded from Github's devel branch. It is unstable and we may have some updates in a few days.

You can install it as follows

1. Download https://github.com/XiaoleiLiuBio/MVP/archive/devel.zip
2. Start R input:

devtools::install_local("MVP-devel")

Haohao

— You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub https://github.com/XiaoleiLiuBio/MVP/issues/28#issuecomment-437829562, or mute the thread https://github.com/notifications/unsubscribe-auth/Al8lkNaG7wR4IzdgiZu-p3feucA2B3xNks5uuUzBgaJpZM4YOb4u .

[TinaNeik]

Hi Haohao,

The installation website for Linux is not working:

https://github.com/XiaoleiLiuBio/MVP/releases/download/v1.0.1/MVP_offline_1.0.1.zip

Cheers, Tina

On Tue, 13 Nov 2018 at 00:51, Ting Neik ting.neik@research.uwa.edu.au wrote:

Got it. The name of the individuals cannot be in character, must be in S1, S2, S3 etc like what I provided for you.

I managed to get 68L, but another error again. Can you please help me?

Thank you.

imMVP <- MVP(

• phe=phenotype,
• geno=genotype,
• map=map,

• K=Kinship,

• CV.GLM=Covariates,

• CV.MLM=Covariates,

• CV.FarmCPU=Covariates,

• nPC.GLM=5,
• nPC.MLM=3,
• nPC.FarmCPU=3,
• perc=1,
• priority="speed",

• ncpus=10,

• vc.method="EMMA",
• maxLoop=10,
• method.bin="FaST-LMM",#"FaST-LMM","EMMA", "static"

• permutation.threshold=TRUE,

• permutation.rep=100,

• threshold=0.05,
• method=c("GLM", "MLM")
• ) Error in MVP.Version(TRUE) : could not find function "MVP.Version"

Cheers, Tina

On Mon, 12 Nov 2018 at 23:30, Ting Neik ting.neik@research.uwa.edu.au wrote:

Hi Haohao,

I managed to solve the MVP-devel installation problem by changing the ~/.R/Makevars

I tried running MVP-devel. But even this version gave the same problem as before:

new("big.matrix.descriptor", description = list(sharedType = "FileBacked",

filename = "mvp.hmp.geno.bin", dirname = "/Users/tinaneik/",

totalRows = 52099L, totalCols = 65L, rowOffset = c(0, 52099

), colOffset = c(0, 65), nrow = 52099, ncol = 65, rowNames = NULL,

colNames = NULL, type = "char", separated = FALSE))

It should be 68 individuals.

What should I do?

Cheers, Tina

On Mon, 12 Nov 2018 at 22:32, Ting Neik ting.neik@research.uwa.edu.au wrote:

Hi Haohao,

I followed your instructions to install "MVP-devel" on my MacBook High Sierra but failed.

Do you think it would be easier to fix the bug in the previous version of MVP so I can still run it, or is it easier to fix the installation of MVP-devel below? I'm stuck.

Appreciate your help.

Thank you.

Cheers, Tina

devtools::install_local("MVP-devel") Installing MVP '/Library/Frameworks/R.framework/Resources/bin/R' --no-site-file --no-environ \ --no-save --no-restore --quiet CMD INSTALL \

'/private/var/folders/qd/1n0yvng123n1sf9cmbzvsf6r0000gn/T/RtmpztBGrb/file46f230031bb/MVP-devel' \

--library='/Library/Frameworks/R.framework/Versions/3.5/Resources/library' \ --install-tests

• installing source package ‘MVP’ ... ** libs clang++ -std=gnu++11 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/Rcpp/include" -I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/RcppProgress/include" -I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/BH/include" -I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/bigmemory/include" -I/usr/local/include -fopenmp -fPIC -Wall -g -O2 -c RcppExports.cpp -o RcppExports.o clang: error: unsupported option '-fopenmp' make: *** [RcppExports.o] Error 1 ERROR: compilation failed for package ‘MVP’
• removing ‘/Library/Frameworks/R.framework/Versions/3.5/Resources/library/MVP’
• restoring previous ‘/Library/Frameworks/R.framework/Versions/3.5/Resources/library/MVP’ Installation failed: Command failed (1)

Cheers,

Tina Neik PhD candidate (Batley Lab) School of Biological Sciences (M092) The University of Western Australia Crawley WA 6009

Email: ting.neik@research.uwa.edu.au Mobile: 04112 10 334

On Mon, 12 Nov 2018 at 18:26, haohao notifications@github.com wrote:

Hi, Tina

Yes, the latest version of MVP can be downloaded from Github's devel branch. It is unstable and we may have some updates in a few days.

You can install it as follows

1. Download https://github.com/XiaoleiLiuBio/MVP/archive/devel.zip
2. Start R input:

devtools::install_local("MVP-devel")

Haohao

— You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub https://github.com/XiaoleiLiuBio/MVP/issues/28#issuecomment-437829562, or mute the thread https://github.com/notifications/unsubscribe-auth/Al8lkNaG7wR4IzdgiZu-p3feucA2B3xNks5uuUzBgaJpZM4YOb4u .

[hyacz]

Hi, Tina

I am very sorry that you have encountered so many problems when using our software.

1. Mac default Clang does not support OpenMP, you need to download Clang from https://cran.r-project.org/bin/macosx/tools/. Or just like you did, disable OpenMP.
2. Can you tell me which individual names are not correctly identified?
3. The MVP.Version function just prints some welcome messages. I first encountered this error, maybe the MVP was not installed correctly.
4. Is 1.0.1 instead of v1.0.1, I will fix it on the README page https://github.com/XiaoleiLiuBio/MVP/releases/download/1.0.1/MVP_offline_1.0.1.zip

Haohao

[TinaNeik]

Thanks Haohao,

Please see my response in blue.

Cheers, Tina

On Tue, 13 Nov 2018 at 12:10, haohao notifications@github.com wrote:

Hi, Tina

I am very sorry that you have encountered so many problems when using our software.

1. Mac default Clang does not support OpenMP, you need to download Clang from https://cran.r-project.org/bin/macosx/tools/. Or just like you did, disable OpenMP.

I just corrected the ~/.R/Makevars. Didn't make any changes to the OpenMP.

I'm using Windows on office desktop, Mac at home. I tried to install MVP 1.0.1 on my desktop but can't do it:

devtools::install_github("hclimente/snpStats") Downloading GitHub repo hclimente/snpStats@master Skipping 2 packages ahead of CRAN: BiocGenerics, zlibbioc √ checking for file 'C:\Users\21660971\AppData\Local\Temp\Rtmp2DJPDT\remotes2470351c14ed\hclimente-snpStats-5d34a9e/DESCRIPTION' ...

• preparing 'snpStats': (1.1s) √ checking DESCRIPTION meta-information
• cleaning src

• installing the package to process help pages (504ms)

• installing source package 'snpStats' ... * libs C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c Runcertain.c -o Runcertain.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c bind.c -o bind.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c count_gt.c -o count_gt.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c covwin.c -o covwin.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c force_hom.c -o force_hom.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c fst.c -o fst.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c glm_test.c -o glm_test.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c glm_test_R.c -o glm_test_R.o glm_test_R.c: In function 'snp_rhs_score': glm_test_R.c:368:7: warning: unused variable 'max_name_length' [-Wunused-variable] int max_name_length = MAX_NAME_LENGTH -1; ^ C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c hash_index.c -o hash_index.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c hphase.c -o hphase.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c imputation.c -o imputation.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c in.c -o in.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c input.c -o input.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c invert.c -o invert.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c ipf.c -o ipf.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c ld.c -o ld.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c misc.c -o misc.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c mla.c -o mla.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c mvphenotype.c -o mvphenotype.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c outdata.c -o outdata.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c plink.c -o plink.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c read_uncertain.c -o read_uncertain.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c readped.c -o readped.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c single_snp_tests.c -o single_snp_tests.o single_snp_tests.c: In function 'score_single': single_snp_tests.c:327:4: warning: 'name_index' may be used uninitialized in this function [-Wmaybe-uninitialized] do_impute(Snps, n, diploid, subset, nsubj, name_index, Rule, gt2ht, ^ C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c snp_summary.c -o snp_summary.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c snpmpy.c -o snpmpy.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c solve_cubic.c -o solve_cubic.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c structure.c -o structure.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c subset.c -o subset.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c switch.c -o switch.o switch.c: In function 'test_switch': switch.c:32:8: warning: variable 'female2' set but not used [-Wunused-but-set-variable] int female2 = NULL; ^ C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c tdt.c -o tdt.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c testBig.c -o testBig.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c uncertain.c -o uncertain.o C:/RBuildTools/3.5/mingw_64/bin/gcc -shared -s -static-libgcc -o snpStats.dll tmp.def Runcertain.o bind.o count_gt.o covwin.o force_hom.o fst.o glm_test.o glm_test_R.o hash_index.o hphase.o imputation.o in.o input.o invert.o ipf.o ld.o misc.o mla.o mvphenotype.o outdata.o plink.o read_uncertain.o readped.o single_snp_tests.o snp_summary.o snpmpy.o solve_cubic.o structure.o subset.o switch.o tdt.o testBig.o uncertain.o -L\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/libs/x64 -lzlib1bioc -LC:/PROGRA~1/R/R-35~1.1/bin/x64 -lR

C:/RBuildTools/3.5/mingw_64/bin/../lib/gcc/x86_64-w64-mingw32/4.9.3/../../../../x86_64-w64-mingw32/bin/ld.exe: cannot find -lzlib1bioc collect2.exe: error: ld returned 1 exit status no DLL was created ERROR: compilation failed for package 'snpStats'

• removing 'C:/Temp/Rtmp8SeoNy/Rinst1d346f563def/snpStats' In R CMD INSTALL

  ERROR: package installation failed Error in run(bin, args = real_cmdargs, stdout_line_callback = real_callback(stdout), : System command error In addition: Warning messages: 1: In untar2(tarfile, files, list, exdir) : skipping pax global extended headers 2: In untar2(tarfile, files, list, exdir) : skipping pax global extended headers

1. Can you tell me which individual names are not correctly identified?

If you replace S1-S68 with the individual names:

Carousy Trilogies MPT Trisivil 92071_IB1832 Asgora NVT Poddy YHUA Bayrus Velkra X1267 X1268 Deakon Anges Polly Serene Archie Tame Schook 528DH Dingy Telton Thumpy Oscar HY99 Heyol T6 Jackel_1 Jackel_2 Jackel_3 Jackel_4 Garond NDB7 Amazon Tair Bold Margin Tomero Wayine P569 PO1548 ATRBanjo Avand_1 Avand_2 Avand_3 Avand_4 Bell_1 Bell_2 Highlands_1 Highlands_2 Highlands_3 Highlands_4 Torn_1 Torn_2 Cambrial_1 Putty_2 Putty_1 Pollen_2 GCV_1 GCV_2 Gale_1 Gale_2 15.23.4.1_1 CT_1 NKR_1 NKR_2 Extreme_1

It would give 65 or lesser individuals instead of 68

But if you give S1-S68 as individual name, then it will read as 68L, but later on give error on "attach.big.matrix" matrix row and column doesn't match.

1. The MVP.Version function just prints some welcome messages. I first encountered this error, maybe the MVP was not installed correctly.

I got stuck at MVP.Version on my macbook using the MVP-devel version. So I uninstalled and use the 1.0.1 version on my Macbook.

Still cannot run because it says the "attach.big.matrix" matrix row and column doesn't match.

1. Is 1.0.1 instead of v1.0.1, I will fix it on the README page https://github.com/XiaoleiLiuBio/MVP/releases/download/1.0.1/MVP_offline_1.0.1.zip

Thanks

Haohao

— You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub https://github.com/XiaoleiLiuBio/MVP/issues/28#issuecomment-438128479, or mute the thread https://github.com/notifications/unsubscribe-auth/Al8lkM_nGyE7QlkjAF7bvhNAvXboH90uks5uukY6gaJpZM4YOb4u .

[TinaNeik]

Hi Haohao,

I installed the MVP 1.0.1 using Linux command and managed to run it successfully on my office desktop.

I used my original file with the individual names (not S1-S68). Still I don't know where the 3 individuals go.

But I'm happy because it finally worked. If you know how did MVP do the filtering of removing individuals, please let me know.

Thank you very much.

MVP.Data(fileHMP="Rlm7_SNP_68lines_forNumericAll_forTASSEL.hmp.txt", filePhe="Phenotype_Rlm7_68LinesAllA.txt", sep.hmp="\t", sep.phe="\t", SNP.effect="Add", out="Rlm7MVP", fileKin=FALSE, filePC=FALSE) [1] "Preparing data for MVP..." [1] "Preparation for PHENOTYPE data is done!" [1] "Output numeric genotype..." [1] "File: Rlm7_SNP_68lines_forNumericAll_forTASSEL.hmp.txt ; Total markers: 52099 finished!" [1] "Preparation for numeric data is done!" [1] "Output mvp genotype..." [1] "MVP data prepration accomplished successfully!" genotype <- attach.big.matrix("Rlm7MVP.geno.desc") phenotype <- read.table("Rlm7MVP.phe", head=TRUE) map <- read.table("Rlm7MVP.map", head=TRUE) imMVP <- MVP(

• phe=phenotype,
• geno=genotype,
• map=map,

• K=Kinship,

• CV.GLM=Covariates,

• CV.MLM=Covariates,

• CV.FarmCPU=Covariates,

• nPC.GLM=5,
• nPC.MLM=3,
• nPC.FarmCPU=3,
• perc=1,
• priority="speed",

• ncpus=10,

• vc.method="EMMA",
• maxLoop=10,
• method.bin="FaST-LMM",#"FaST-LMM","EMMA", "static"

• permutation.threshold=TRUE,

• permutation.rep=100,

• threshold=0.05,
• method=c("GLM", "MLM", "FarmCPU")
• )

  --------------------------------------------Welcome to

  MVP---------------------------------------------#

  A Memory-efficient, Visualization-enhanced, and

  Parallel-accelerated Tool For GWAS #

  Version: 1.0.1

  #

  Authors: Lilin Yin, Haohao Zhang, Zhiwu Zhang, Xinyun Li, Xiaohui Yuan,

  Shuhong Zhao, and Xiaolei Liu #

  Contact: xiaoleiliu@mail.hzau.edu.cn

  #

  -------------------------------------------------------------------------------------------------------

  [1] "Input data has 65 individuals, 52099 markers" [1] "Principal Component Analysis Start ..." means for first 10 snps: [1] 0 0 0 0 0 0 0 0 0 0 [1] "GWAS Start..." [1] "General Linear Model (GLM) Start ..." |---------------------------------------------------------------------->100.00% [1] "Mixed Linear Model (MLM) Start ..." [1] "Calculating Kinship..." [1] "Variance components..." [1] "Variance Components Estimation is Done!" [1] "Eigen-Decomposition..." [1] "Eigen-Decomposition is Done!" |---------------------------------------------------------------------->100.00% [1] "FarmCPU Start ..." [1] "Current loop: 1 out of maximum of 10" [1] "seqQTN" NULL [1] "scanning..." [1] "number of covariates in current loop is:" [1] 3 |---------------------------------------------------------------------->100.00% [1] "Current loop: 2 out of maximum of 10" [1] "Optimizing Pseudo QTNs..." [1] 5.000000e+05 6.000000e+00 8.169581e+01 1.100854e-01 1.485997e-01 [1] 5.000000e+06 6.000000e+00 8.169581e+01 1.100854e-01 1.485997e-01 [1] 5.000000e+07 6.000000e+00 8.153343e+01 1.213692e-01 1.482407e-01 [1] "seqQTN" [1] 8745 43885 6808 5801 35234 25329 [1] "scanning..." [1] "number of covariates in current loop is:" [1] 9 |---------------------------------------------------------------------->100.00% [1] "Current loop: 3 out of maximum of 10" [1] "Optimizing Pseudo QTNs..." [1] 5.000000e+05 6.000000e+00 3.152053e+01 7.503536e-02 6.789480e-02 [1] 5.000000e+06 6.000000e+00 3.152053e+01 7.503536e-02 6.789480e-02 [1] 5.000000e+07 6.000000e+00 3.152053e+01 7.503536e-02 6.789480e-02 [1] "seqQTN" [1] 8745 6808 704 15175 43885 5801 25329 35234 [1] "scanning..." [1] "number of covariates in current loop is:" [1] 11 |---------------------------------------------------------------------->100.00% [1] "Current loop: 4 out of maximum of 10" [1] "Optimizing Pseudo QTNs..." [1] 5.000000e+05 6.000000e+00 -1.993368e+01 1.797932e-01 2.689142e-02 [1] 5.000000e+06 6.000000e+00 -1.993368e+01 1.797932e-01 2.689142e-02 [1] 5.000000e+07 6.000000e+00 8.335373e+00 1.090235e-02 5.399971e-02 [1] "seqQTN" [1] 8745 704 6808 15175 13531 43885 35234 25329 5801 [1] "scanning..." [1] "number of covariates in current loop is:" [1] 12 |---------------------------------------------------------------------->100.00% [1] "Current loop: 5 out of maximum of 10" [1] "Optimizing Pseudo QTNs..." [1] 5.000000e+05 6.000000e+00 -2.022484e+01 1.634129e-01 2.701198e-02 [1] 5.000000e+06 6.000000e+00 -2.022484e+01 1.634129e-01 2.701198e-02 [1] 5.000000e+07 6.000000e+00 -1.020703e+01 3.247892e-04 4.820299e-02 [1] "seqQTN" [1] 8745 6808 704 15175 13531 35234 43885 25329 5801 [1] "scanning..." [1] "number of covariates in current loop is:" [1] 12 |---------------------------------------------------------------------->100.00% [1] "Significance Level: 9.59711318835294e-07" [1] "Visualization Start..." [1] "Phenotype distribution Plotting..." [1] "PCA plot2d..." [1] "SNP_Density Plotting..." [1] "Circular_Manhattan Plotting Rlm7.GLM..." [1] "Circular_Manhattan Plotting Rlm7.MLM..." [1] "Circular_Manhattan Plotting Rlm7.FarmCPU..." [1] "Rectangular_Manhattan Plotting Rlm7.GLM..." [1] "Rectangular_Manhattan Plotting Rlm7.MLM..." [1] "Rectangular_Manhattan Plotting Rlm7.FarmCPU..." [1] "Q_Q Plotting Rlm7.GLM..." [1] "Q_Q Plotting Rlm7.MLM..." [1] "Q_Q Plotting Rlm7.FarmCPU..." [1] "Multracks_Rectangular Plotting Rlm7.GLM..." [1] "Multracks_Rectangular Plotting Rlm7.MLM..." [1] "Multracks_Rectangular Plotting Rlm7.FarmCPU..." [1] "Multraits_Rectangular Plotting..." [1] "Multracks_QQ Plotting Rlm7.GLM..." [1] "Multracks_QQ Plotting Rlm7.MLM..." [1] "Multracks_QQ Plotting Rlm7.FarmCPU..." [1] "Multraits_QQ Plotting Rlm7.GLM..." [1] "Multraits_QQ Plotting Rlm7.MLM..." [1] "Multraits_QQ Plotting Rlm7.FarmCPU..."

  -------------------------------------------MVP

  ACCOMPLISHED--------------------------------------------#

Cheers, Tina

On Tue, 13 Nov 2018 at 12:53, Ting Neik ting.neik@research.uwa.edu.au wrote:

Thanks Haohao,

Please see my response in blue.

Cheers, Tina

On Tue, 13 Nov 2018 at 12:10, haohao notifications@github.com wrote:

Hi, Tina

I am very sorry that you have encountered so many problems when using our software.

1. Mac default Clang does not support OpenMP, you need to download Clang from https://cran.r-project.org/bin/macosx/tools/. Or just like you did, disable OpenMP.

I just corrected the ~/.R/Makevars. Didn't make any changes to the OpenMP.

I'm using Windows on office desktop, Mac at home. I tried to install MVP 1.0.1 on my desktop but can't do it:

devtools::install_github("hclimente/snpStats") Downloading GitHub repo hclimente/snpStats@master Skipping 2 packages ahead of CRAN: BiocGenerics, zlibbioc √ checking for file 'C:\Users\21660971\AppData\Local\Temp\Rtmp2DJPDT\remotes2470351c14ed\hclimente-snpStats-5d34a9e/DESCRIPTION' ...

• preparing 'snpStats': (1.1s) √ checking DESCRIPTION meta-information
• cleaning src

• installing the package to process help pages (504ms)

• installing source package 'snpStats' ... * libs C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c Runcertain.c -o Runcertain.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c bind.c -o bind.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c count_gt.c -o count_gt.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c covwin.c -o covwin.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c force_hom.c -o force_hom.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c fst.c -o fst.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c glm_test.c -o glm_test.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c glm_test_R.c -o glm_test_R.o glm_test_R.c: In function 'snp_rhs_score': glm_test_R.c:368:7: warning: unused variable 'max_name_length' [-Wunused-variable] int max_name_length = MAX_NAME_LENGTH -1; ^ C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c hash_index.c -o hash_index.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c hphase.c -o hphase.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c imputation.c -o imputation.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c in.c -o in.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c input.c -o input.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c invert.c -o invert.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c ipf.c -o ipf.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c ld.c -o ld.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c misc.c -o misc.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c mla.c -o mla.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c mvphenotype.c -o mvphenotype.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c outdata.c -o outdata.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c plink.c -o plink.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c read_uncertain.c -o read_uncertain.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c readped.c -o readped.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c single_snp_tests.c -o single_snp_tests.o single_snp_tests.c: In function 'score_single': single_snp_tests.c:327:4: warning: 'name_index' may be used uninitialized in this function [-Wmaybe-uninitialized] do_impute(Snps, n, diploid, subset, nsubj, name_index, Rule, gt2ht, ^ C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c snp_summary.c -o snp_summary.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c snpmpy.c -o snpmpy.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c solve_cubic.c -o solve_cubic.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c structure.c -o structure.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c subset.c -o subset.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c switch.c -o switch.o switch.c: In function 'test_switch': switch.c:32:8: warning: variable 'female2' set but not used [-Wunused-but-set-variable] int female2 = NULL; ^ C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c tdt.c -o tdt.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c testBig.c -o testBig.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c uncertain.c -o uncertain.o C:/RBuildTools/3.5/mingw_64/bin/gcc -shared -s -static-libgcc -o snpStats.dll tmp.def Runcertain.o bind.o count_gt.o covwin.o force_hom.o fst.o glm_test.o glm_test_R.o hash_index.o hphase.o imputation.o in.o input.o invert.o ipf.o ld.o misc.o mla.o mvphenotype.o outdata.o plink.o read_uncertain.o readped.o single_snp_tests.o snp_summary.o snpmpy.o solve_cubic.o structure.o subset.o switch.o tdt.o testBig.o uncertain.o -L\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/libs/x64 -lzlib1bioc -LC:/PROGRA~1/R/R-35~1.1/bin/x64 -lR

C:/RBuildTools/3.5/mingw_64/bin/../lib/gcc/x86_64-w64-mingw32/4.9.3/../../../../x86_64-w64-mingw32/bin/ld.exe: cannot find -lzlib1bioc collect2.exe: error: ld returned 1 exit status no DLL was created ERROR: compilation failed for package 'snpStats'

• removing 'C:/Temp/Rtmp8SeoNy/Rinst1d346f563def/snpStats' In R CMD INSTALL

  ERROR: package installation failed Error in run(bin, args = real_cmdargs, stdout_line_callback = real_callback(stdout), : System command error In addition: Warning messages: 1: In untar2(tarfile, files, list, exdir) : skipping pax global extended headers 2: In untar2(tarfile, files, list, exdir) : skipping pax global extended headers

1. Can you tell me which individual names are not correctly identified?

If you replace S1-S68 with the individual names:

Carousy Trilogies MPT Trisivil 92071_IB1832 Asgora NVT Poddy YHUA Bayrus Velkra X1267 X1268 Deakon Anges Polly Serene Archie Tame Schook 528DH Dingy Telton Thumpy Oscar HY99 Heyol T6 Jackel_1 Jackel_2 Jackel_3 Jackel_4 Garond NDB7 Amazon Tair Bold Margin Tomero Wayine P569 PO1548 ATRBanjo Avand_1 Avand_2 Avand_3 Avand_4 Bell_1 Bell_2 Highlands_1 Highlands_2 Highlands_3 Highlands_4 Torn_1 Torn_2 Cambrial_1 Putty_2 Putty_1 Pollen_2 GCV_1 GCV_2 Gale_1 Gale_2 15.23.4.1_1 CT_1 NKR_1 NKR_2 Extreme_1

It would give 65 or lesser individuals instead of 68

But if you give S1-S68 as individual name, then it will read as 68L, but later on give error on "attach.big.matrix" matrix row and column doesn't match.

1. The MVP.Version function just prints some welcome messages. I first encountered this error, maybe the MVP was not installed correctly.

I got stuck at MVP.Version on my macbook using the MVP-devel version. So I uninstalled and use the 1.0.1 version on my Macbook.

Still cannot run because it says the "attach.big.matrix" matrix row and column doesn't match.

1. Is 1.0.1 instead of v1.0.1, I will fix it on the README page https://github.com/XiaoleiLiuBio/MVP/releases/download/1.0.1/MVP_offline_1.0.1.zip

Thanks

Haohao

— You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub https://github.com/XiaoleiLiuBio/MVP/issues/28#issuecomment-438128479, or mute the thread https://github.com/notifications/unsubscribe-auth/Al8lkM_nGyE7QlkjAF7bvhNAvXboH90uks5uukY6gaJpZM4YOb4u .

[TinaNeik]

Hi Haohao,

I can't get past this step because of the same error. This is for a new dataset for 176 individuals.

--------------------------------------------Welcome to

MVP--------------------- ------------------------#

A Memory-efficient, Visualization-enhanced, and

Parallel-accelerated Tool For GWAS #

Version: 1.0.1

                                                          #

Authors: Lilin Yin, Haohao Zhang, Zhiwu Zhang, Xinyun Li, Xiaohui Yuan,

Shuhon g Zhao, and Xiaolei Liu #

Contact: xiaoleiliu@mail.hzau.edu.cn

                                                          #

-------------------------------------------------------------------------------

                            ------------------------#

[1] "Input data has 173 individuals, 52099 markers" [1] "Principal Component Analysis Start ..." means for first 10 snps: [1] 0 0 0 0 0 0 0 0 0 0 Error in SetElements.bm(x, i, j, value) : Matrix dimensions do not agree with big.matrix instance set size.

Cheers, Tina

On Tue, 13 Nov 2018 at 15:04, Ting Neik ting.neik@research.uwa.edu.au wrote:

Hi Haohao,

I installed the MVP 1.0.1 using Linux command and managed to run it successfully on my office desktop.

I used my original file with the individual names (not S1-S68). Still I don't know where the 3 individuals go.

But I'm happy because it finally worked. If you know how did MVP do the filtering of removing individuals, please let me know.

Thank you very much.

MVP.Data(fileHMP="Rlm7_SNP_68lines_forNumericAll_forTASSEL.hmp.txt", filePhe="Phenotype_Rlm7_68LinesAllA.txt", sep.hmp="\t", sep.phe="\t", SNP.effect="Add", out="Rlm7MVP", fileKin=FALSE, filePC=FALSE) [1] "Preparing data for MVP..." [1] "Preparation for PHENOTYPE data is done!" [1] "Output numeric genotype..." [1] "File: Rlm7_SNP_68lines_forNumericAll_forTASSEL.hmp.txt ; Total markers: 52099 finished!" [1] "Preparation for numeric data is done!" [1] "Output mvp genotype..." [1] "MVP data prepration accomplished successfully!" genotype <- attach.big.matrix("Rlm7MVP.geno.desc") phenotype <- read.table("Rlm7MVP.phe", head=TRUE) map <- read.table("Rlm7MVP.map", head=TRUE) imMVP <- MVP(

• phe=phenotype,
• geno=genotype,
• map=map,

• K=Kinship,

• CV.GLM=Covariates,

• CV.MLM=Covariates,

• CV.FarmCPU=Covariates,

• nPC.GLM=5,
• nPC.MLM=3,
• nPC.FarmCPU=3,
• perc=1,
• priority="speed",

• ncpus=10,

• vc.method="EMMA",
• maxLoop=10,
• method.bin="FaST-LMM",#"FaST-LMM","EMMA", "static"

• permutation.threshold=TRUE,

• permutation.rep=100,

• threshold=0.05,
• method=c("GLM", "MLM", "FarmCPU")
• )

  --------------------------------------------Welcome to

  MVP---------------------------------------------#

  A Memory-efficient, Visualization-enhanced, and

  Parallel-accelerated Tool For GWAS #

  Version: 1.0.1

  #

  Authors: Lilin Yin, Haohao Zhang, Zhiwu Zhang, Xinyun Li, Xiaohui Yuan,

  Shuhong Zhao, and Xiaolei Liu #

  Contact: xiaoleiliu@mail.hzau.edu.cn

  #

-------------------------------------------------------------------------------------------------------

[1] "Input data has 65 individuals, 52099 markers" [1] "Principal Component Analysis Start ..." means for first 10 snps: [1] 0 0 0 0 0 0 0 0 0 0 [1] "GWAS Start..." [1] "General Linear Model (GLM) Start ..."

|---------------------------------------------------------------------->100.00% [1] "Mixed Linear Model (MLM) Start ..." [1] "Calculating Kinship..." [1] "Variance components..." [1] "Variance Components Estimation is Done!" [1] "Eigen-Decomposition..." [1] "Eigen-Decomposition is Done!"

|---------------------------------------------------------------------->100.00% [1] "FarmCPU Start ..." [1] "Current loop: 1 out of maximum of 10" [1] "seqQTN" NULL [1] "scanning..." [1] "number of covariates in current loop is:" [1] 3

|---------------------------------------------------------------------->100.00% [1] "Current loop: 2 out of maximum of 10" [1] "Optimizing Pseudo QTNs..." [1] 5.000000e+05 6.000000e+00 8.169581e+01 1.100854e-01 1.485997e-01 [1] 5.000000e+06 6.000000e+00 8.169581e+01 1.100854e-01 1.485997e-01 [1] 5.000000e+07 6.000000e+00 8.153343e+01 1.213692e-01 1.482407e-01 [1] "seqQTN" [1] 8745 43885 6808 5801 35234 25329 [1] "scanning..." [1] "number of covariates in current loop is:" [1] 9

|---------------------------------------------------------------------->100.00% [1] "Current loop: 3 out of maximum of 10" [1] "Optimizing Pseudo QTNs..." [1] 5.000000e+05 6.000000e+00 3.152053e+01 7.503536e-02 6.789480e-02 [1] 5.000000e+06 6.000000e+00 3.152053e+01 7.503536e-02 6.789480e-02 [1] 5.000000e+07 6.000000e+00 3.152053e+01 7.503536e-02 6.789480e-02 [1] "seqQTN" [1] 8745 6808 704 15175 43885 5801 25329 35234 [1] "scanning..." [1] "number of covariates in current loop is:" [1] 11

|---------------------------------------------------------------------->100.00% [1] "Current loop: 4 out of maximum of 10" [1] "Optimizing Pseudo QTNs..." [1] 5.000000e+05 6.000000e+00 -1.993368e+01 1.797932e-01 2.689142e-02 [1] 5.000000e+06 6.000000e+00 -1.993368e+01 1.797932e-01 2.689142e-02 [1] 5.000000e+07 6.000000e+00 8.335373e+00 1.090235e-02 5.399971e-02 [1] "seqQTN" [1] 8745 704 6808 15175 13531 43885 35234 25329 5801 [1] "scanning..." [1] "number of covariates in current loop is:" [1] 12

|---------------------------------------------------------------------->100.00% [1] "Current loop: 5 out of maximum of 10" [1] "Optimizing Pseudo QTNs..." [1] 5.000000e+05 6.000000e+00 -2.022484e+01 1.634129e-01 2.701198e-02 [1] 5.000000e+06 6.000000e+00 -2.022484e+01 1.634129e-01 2.701198e-02 [1] 5.000000e+07 6.000000e+00 -1.020703e+01 3.247892e-04 4.820299e-02 [1] "seqQTN" [1] 8745 6808 704 15175 13531 35234 43885 25329 5801 [1] "scanning..." [1] "number of covariates in current loop is:" [1] 12

|---------------------------------------------------------------------->100.00% [1] "Significance Level: 9.59711318835294e-07" [1] "Visualization Start..." [1] "Phenotype distribution Plotting..." [1] "PCA plot2d..." [1] "SNP_Density Plotting..." [1] "Circular_Manhattan Plotting Rlm7.GLM..." [1] "Circular_Manhattan Plotting Rlm7.MLM..." [1] "Circular_Manhattan Plotting Rlm7.FarmCPU..." [1] "Rectangular_Manhattan Plotting Rlm7.GLM..." [1] "Rectangular_Manhattan Plotting Rlm7.MLM..." [1] "Rectangular_Manhattan Plotting Rlm7.FarmCPU..." [1] "Q_Q Plotting Rlm7.GLM..." [1] "Q_Q Plotting Rlm7.MLM..." [1] "Q_Q Plotting Rlm7.FarmCPU..." [1] "Multracks_Rectangular Plotting Rlm7.GLM..." [1] "Multracks_Rectangular Plotting Rlm7.MLM..." [1] "Multracks_Rectangular Plotting Rlm7.FarmCPU..." [1] "Multraits_Rectangular Plotting..." [1] "Multracks_QQ Plotting Rlm7.GLM..." [1] "Multracks_QQ Plotting Rlm7.MLM..." [1] "Multracks_QQ Plotting Rlm7.FarmCPU..." [1] "Multraits_QQ Plotting Rlm7.GLM..." [1] "Multraits_QQ Plotting Rlm7.MLM..." [1] "Multraits_QQ Plotting Rlm7.FarmCPU..."

-------------------------------------------MVP

ACCOMPLISHED--------------------------------------------#

Cheers, Tina

On Tue, 13 Nov 2018 at 12:53, Ting Neik ting.neik@research.uwa.edu.au wrote:

Thanks Haohao,

Please see my response in blue.

Cheers, Tina

On Tue, 13 Nov 2018 at 12:10, haohao notifications@github.com wrote:

Hi, Tina

I am very sorry that you have encountered so many problems when using our software.

1. Mac default Clang does not support OpenMP, you need to download Clang from https://cran.r-project.org/bin/macosx/tools/. Or just like you did, disable OpenMP.

I just corrected the ~/.R/Makevars. Didn't make any changes to the OpenMP.

I'm using Windows on office desktop, Mac at home. I tried to install MVP 1.0.1 on my desktop but can't do it:

devtools::install_github("hclimente/snpStats") Downloading GitHub repo hclimente/snpStats@master Skipping 2 packages ahead of CRAN: BiocGenerics, zlibbioc √ checking for file 'C:\Users\21660971\AppData\Local\Temp\Rtmp2DJPDT\remotes2470351c14ed\hclimente-snpStats-5d34a9e/DESCRIPTION' ...

• preparing 'snpStats': (1.1s) √ checking DESCRIPTION meta-information
• cleaning src

• installing the package to process help pages (504ms)

• installing source package 'snpStats' ... * libs C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c Runcertain.c -o Runcertain.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c bind.c -o bind.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c count_gt.c -o count_gt.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c covwin.c -o covwin.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c force_hom.c -o force_hom.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c fst.c -o fst.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c glm_test.c -o glm_test.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c glm_test_R.c -o glm_test_R.o glm_test_R.c: In function 'snp_rhs_score': glm_test_R.c:368:7: warning: unused variable 'max_name_length' [-Wunused-variable] int max_name_length = MAX_NAME_LENGTH -1; ^ C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c hash_index.c -o hash_index.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c hphase.c -o hphase.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c imputation.c -o imputation.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c in.c -o in.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c input.c -o input.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c invert.c -o invert.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c ipf.c -o ipf.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c ld.c -o ld.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c misc.c -o misc.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c mla.c -o mla.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c mvphenotype.c -o mvphenotype.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c outdata.c -o outdata.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c plink.c -o plink.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c read_uncertain.c -o read_uncertain.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c readped.c -o readped.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c single_snp_tests.c -o single_snp_tests.o single_snp_tests.c: In function 'score_single': single_snp_tests.c:327:4: warning: 'name_index' may be used uninitialized in this function [-Wmaybe-uninitialized] do_impute(Snps, n, diploid, subset, nsubj, name_index, Rule, gt2ht, ^ C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c snp_summary.c -o snp_summary.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c snpmpy.c -o snpmpy.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c solve_cubic.c -o solve_cubic.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c structure.c -o structure.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c subset.c -o subset.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c switch.c -o switch.o switch.c: In function 'test_switch': switch.c:32:8: warning: variable 'female2' set but not used [-Wunused-but-set-variable] int female2 = NULL; ^ C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c tdt.c -o tdt.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c testBig.c -o testBig.o C:/RBuildTools/3.5/mingw_64/bin/gcc -I"\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/include" -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -O2 -Wall -std=gnu99 -mtune=generic -c uncertain.c -o uncertain.o C:/RBuildTools/3.5/mingw_64/bin/gcc -shared -s -static-libgcc -o snpStats.dll tmp.def Runcertain.o bind.o count_gt.o covwin.o force_hom.o fst.o glm_test.o glm_test_R.o hash_index.o hphase.o imputation.o in.o input.o invert.o ipf.o ld.o misc.o mla.o mvphenotype.o outdata.o plink.o read_uncertain.o readped.o single_snp_tests.o snp_summary.o snpmpy.o solve_cubic.o structure.o subset.o switch.o tdt.o testBig.o uncertain.o -L\ uniwa.uwa.edu.au/userhome/students1/21660971/My Documents/R/win-library/3.5/zlibbioc/libs/x64 -lzlib1bioc -LC:/PROGRA~1/R/R-35~1.1/bin/x64 -lR

C:/RBuildTools/3.5/mingw_64/bin/../lib/gcc/x86_64-w64-mingw32/4.9.3/../../../../x86_64-w64-mingw32/bin/ld.exe: cannot find -lzlib1bioc collect2.exe: error: ld returned 1 exit status no DLL was created ERROR: compilation failed for package 'snpStats'

• removing 'C:/Temp/Rtmp8SeoNy/Rinst1d346f563def/snpStats' In R CMD INSTALL

  ERROR: package installation failed Error in run(bin, args = real_cmdargs, stdout_line_callback = real_callback(stdout), : System command error In addition: Warning messages: 1: In untar2(tarfile, files, list, exdir) : skipping pax global extended headers 2: In untar2(tarfile, files, list, exdir) : skipping pax global extended headers

1. Can you tell me which individual names are not correctly identified?

If you replace S1-S68 with the individual names:

Carousy Trilogies MPT Trisivil 92071_IB1832 Asgora NVT Poddy YHUA Bayrus Velkra X1267 X1268 Deakon Anges Polly Serene Archie Tame Schook 528DH Dingy Telton Thumpy Oscar HY99 Heyol T6 Jackel_1 Jackel_2 Jackel_3 Jackel_4 Garond NDB7 Amazon Tair Bold Margin Tomero Wayine P569 PO1548 ATRBanjo Avand_1 Avand_2 Avand_3 Avand_4 Bell_1 Bell_2 Highlands_1 Highlands_2 Highlands_3 Highlands_4 Torn_1 Torn_2 Cambrial_1 Putty_2 Putty_1 Pollen_2 GCV_1 GCV_2 Gale_1 Gale_2 15.23.4.1_1 CT_1 NKR_1 NKR_2 Extreme_1

It would give 65 or lesser individuals instead of 68

But if you give S1-S68 as individual name, then it will read as 68L, but later on give error on "attach.big.matrix" matrix row and column doesn't match.

1. The MVP.Version function just prints some welcome messages. I first encountered this error, maybe the MVP was not installed correctly.

I got stuck at MVP.Version on my macbook using the MVP-devel version. So I uninstalled and use the 1.0.1 version on my Macbook.

Still cannot run because it says the "attach.big.matrix" matrix row and column doesn't match.

1. Is 1.0.1 instead of v1.0.1, I will fix it on the README page https://github.com/XiaoleiLiuBio/MVP/releases/download/1.0.1/MVP_offline_1.0.1.zip

Thanks

Haohao

— You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub https://github.com/XiaoleiLiuBio/MVP/issues/28#issuecomment-438128479, or mute the thread https://github.com/notifications/unsubscribe-auth/Al8lkM_nGyE7QlkjAF7bvhNAvXboH90uks5uukY6gaJpZM4YOb4u .

[hyacz]

Hi, Tina This seems to be because some individuals have been mistakenly identified. The log show that only 173 individuals have been identified. Have you replaced the individual with S1, S2, S3...?

Haohao

[TinaNeik]

Thanks Haohao.

I replaced the individual names to S1, S2, S3 etc and get it to run completely, now I know the problem of the error

"Error in SetElements.bm(x, i, j, value) : Matrix dimensions do not agree with big.matrix instance set size."

is because naming of individuals have to be in S1, S2, S3 format. But I still don't understand why it is 173 individuals, not 176, just like before missing 3 individuals.

Do you know what is the reason?

Appreciate your help.

Thank you.

--------------------------------------------Welcome to

MVP---------------------------------------------#

A Memory-efficient, Visualization-enhanced, and

Parallel-accelerated Tool For GWAS #

Version: 1.0.1

                         #

Authors: Lilin Yin, Haohao Zhang, Zhiwu Zhang, Xinyun Li, Xiaohui Yuan,

Shuhong Zhao, and Xiaolei Liu #

Contact: xiaoleiliu@mail.hzau.edu.cn

                         #

-------------------------------------------------------------------------------------------------------

[1] "Input data has 173 individuals, 52099 markers" [1] "Principal Component Analysis Start..." means for first 10 snps: [1] 0 0 0 0 0 0 0 0 0 0 [1] "GWAS Start..." [1] "General Linear Model (GLM) Start ..."

[1] "Mixed Linear Model (MLM) Start ..." [1] "Calculating Kinship..." [1] "Variance components..." [1] "Variance Components Estimation is Done!" [1] "Eigen-Decomposition..." [1] "Eigen-Decomposition is Done!"

[1] "FarmCPU Start ..." [1] "Current loop: 1 out of maximum of 10" [1] "seqQTN" NULL [1] "scanning..." [1] "number of covariates in current loop is:" [1] 3 |---------------------------------------------------------------------->100.00% [1] "Current loop: 2 out of maximum of 10" [1] "Optimizing Pseudo QTNs..." [1] 5.000000e+05 9.000000e+00 3.480488e+02 8.435696e-02 3.780617e-01 [1] 5.000000e+06 9.000000e+00 3.480488e+02 8.435696e-02 3.780617e-01 [1] 5.000000e+07 9.000000e+00 3.714787e+02 7.304528e-02 4.418982e-01 [1] "seqQTN" [1] 13719 6805 26349 8745 12938 42787 9954 20568 7037 [1] "scanning..." [1] "number of covariates in current loop is:" [1] 12 |---------------------------------------------------------------------->100.00% [1] "Current loop: 3 out of maximum of 10" [1] "Optimizing Pseudo QTNs..." [1] 5.000000e+05 9.000000e+00 2.220364e+02 8.107669e-02 1.804395e-01 [1] 5.000000e+06 9.000000e+00 2.137317e+02 7.631238e-02 1.698363e-01 [1] 5.000000e+07 9.000000e+00 2.258661e+02 7.450450e-02 1.832515e-01 [1] "seqQTN" [1] 25867 24506 24459 34889 44391 9954 13719 42787 7037 8745 20568 6805 12938 26349 [1] "scanning..." [1] "number of covariates in current loop is:" [1] 17 |---------------------------------------------------------------------->100.00% [1] "Current loop: 4 out of maximum of 10" [1] "Optimizing Pseudo QTNs..." [1] 5.000000e+05 9.000000e+00 1.565068e+02 4.534805e-02 1.232688e-01 [1] 5.000000e+06 9.000000e+00 1.565068e+02 4.534805e-02 1.232688e-01 [1] 5.000000e+07 9.000000e+00 1.418569e+02 1.328677e-01 1.087829e-01 [1] "seqQTN" [1] 34889 42787 8745 7037 9954 3926 49355 31345 12826 44391 24459 20568 26349 6805 [15] 13719 12938 24506 [1] "scanning..." [1] "number of covariates in current loop is:" [1] 20 |---------------------------------------------------------------------->100.00% [1] "Current loop: 5 out of maximum of 10" [1] "Optimizing Pseudo QTNs..." [1] 5.000000e+05 9.000000e+00 9.824032e+01 2.221593e-02 9.009003e-02 [1] 5.000000e+06 9.000000e+00 9.824032e+01 2.221593e-02 9.009003e-02 [1] 5.000000e+07 9.000000e+00 9.706491e+01 1.120900e-02 9.153457e-02 [1] "seqQTN" [1] 34889 42787 7037 49355 8745 12826 44391 24459 3686 3926 9954 31345 6805 13719 [15] 26349 12938 24506 [1] "scanning..." [1] "number of covariates in current loop is:" [1] 20 |---------------------------------------------------------------------->100.00% [1] "Current loop: 6 out of maximum of 10" [1] "Optimizing Pseudo QTNs..." [1] 5.000000e+05 9.000000e+00 7.204580e+01 5.812775e-04 8.626922e-02 [1] 5.000000e+06 9.000000e+00 7.204580e+01 5.812775e-04 8.626922e-02 [1] 5.000000e+07 9.000000e+00 6.110482e+01 7.504327e-03 7.484952e-02 [1] "seqQTN" [1] 34889 42787 12826 8745 44391 3686 7037 49355 9954 3926 51954 24459 31345 13719 [15] 26349 6805 12938 24506 [1] "scanning..." [1] "number of covariates in current loop is:" [1] 21 |---------------------------------------------------------------------->100.00% [1] "Current loop: 7 out of maximum of 10" [1] "Optimizing Pseudo QTNs..." [1] 5.000000e+05 9.000000e+00 3.804992e+01 4.775745e-04 7.087834e-02 [1] 5.000000e+06 9.000000e+00 3.804992e+01 4.775745e-04 7.087834e-02 [1] 5.000000e+07 9.000000e+00 3.834452e+01 4.775745e-04 7.087834e-02 [1] "seqQTN" [1] 34889 42787 44391 49355 3686 7037 12826 8745 9954 51954 3926 24459 13719 31345 [15] 6805 26349 12938 24506 [1] "scanning..." [1] "number of covariates in current loop is:" [1] 21 |---------------------------------------------------------------------->100.00% [1] "Significance Level: 9.59711318835294e-07" [1] "Visualization Start..." [1] "Phenotype distribution Plotting..." [1] "PCA plot2d..." [1] "PCA plot3d..." Writing 'MVP.3D090.png' Loading required namespace: magick Writing 'MVP.3D045.png' [1] "SNP_Density Plotting..." [1] "Circular_Manhattan Plotting Rlm7.GLM..." [1] "Circular_Manhattan Plotting Rlm7.MLM..." [1] "Circular_Manhattan Plotting Rlm7.FarmCPU..." [1] "Rectangular_Manhattan Plotting Rlm7.GLM..." [1] "Rectangular_Manhattan Plotting Rlm7.MLM..." [1] "Rectangular_Manhattan Plotting Rlm7.FarmCPU..." [1] "Q_Q Plotting Rlm7.GLM..." [1] "Q_Q Plotting Rlm7.MLM..." [1] "Q_Q Plotting Rlm7.FarmCPU..." [1] "Multracks_Rectangular Plotting Rlm7.GLM..." [1] "Multracks_Rectangular Plotting Rlm7.MLM..." [1] "Multracks_Rectangular Plotting Rlm7.FarmCPU..." [1] "Multraits_Rectangular Plotting..." [1] "Multracks_QQ Plotting Rlm7.GLM..." [1] "Multracks_QQ Plotting Rlm7.MLM..." [1] "Multracks_QQ Plotting Rlm7.FarmCPU..." [1] "Multraits_QQ Plotting Rlm7.GLM..." [1] "Multraits_QQ Plotting Rlm7.MLM..." [1] "Multraits_QQ Plotting Rlm7.FarmCPU..."

-------------------------------------------MVP

ACCOMPLISHED--------------------------------------------# There were 50 or more warnings (use warnings() to see the first 50)

Cheers, Tina

On Wed, 14 Nov 2018 at 21:00, haohao notifications@github.com wrote:

Hi, Tina This seems to be because some individuals have been mistakenly identified. The log show that only 173 individuals have been identified. Have you replaced the individual with S1, S2, S3...?

Haohao

— You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub https://github.com/XiaoleiLiuBio/MVP/issues/28#issuecomment-438653786, or mute the thread https://github.com/notifications/unsubscribe-auth/Al8lkLk_FqeKI6v6JNKvaY6mrutXzUOBks5uvBQDgaJpZM4YOb4u .

[TinaNeik]

Here is the .geno.desc

It says ncol=176?

new("big.matrix.descriptor", description = list(sharedType = "FileBacked",

filename = "Rlm7MVP176.geno.bin", dirname = "/Users/tinaneik/",

totalRows = 52099L, totalCols = 176L, rowOffset = c(0, 52099

), colOffset = c(0, 176), nrow = 52099, ncol = 176, rowNames = NULL,

colNames = NULL, type = "char", separated = FALSE))

Cheers, Tina

On Wed, 14 Nov 2018 at 21:41, Ting Neik ting.neik@research.uwa.edu.au wrote:

Thanks Haohao.

I replaced the individual names to S1, S2, S3 etc and get it to run completely, now I know the problem of the error

"Error in SetElements.bm(x, i, j, value) : Matrix dimensions do not agree with big.matrix instance set size."

is because naming of individuals have to be in S1, S2, S3 format. But I still don't understand why it is 173 individuals, not 176, just like before missing 3 individuals.

Do you know what is the reason?

Appreciate your help.

Thank you.

--------------------------------------------Welcome to

MVP---------------------------------------------#

A Memory-efficient, Visualization-enhanced, and

Parallel-accelerated Tool For GWAS #

Version: 1.0.1

                         #

Authors: Lilin Yin, Haohao Zhang, Zhiwu Zhang, Xinyun Li, Xiaohui Yuan,

Shuhong Zhao, and Xiaolei Liu #

Contact: xiaoleiliu@mail.hzau.edu.cn

                           #

-------------------------------------------------------------------------------------------------------

[1] "Input data has 173 individuals, 52099 markers" [1] "Principal Component Analysis Start..." means for first 10 snps: [1] 0 0 0 0 0 0 0 0 0 0 [1] "GWAS Start..." [1] "General Linear Model (GLM) Start ..."

[1] "Mixed Linear Model (MLM) Start ..." [1] "Calculating Kinship..." [1] "Variance components..." [1] "Variance Components Estimation is Done!" [1] "Eigen-Decomposition..." [1] "Eigen-Decomposition is Done!"

[1] "FarmCPU Start ..." [1] "Current loop: 1 out of maximum of 10" [1] "seqQTN" NULL [1] "scanning..." [1] "number of covariates in current loop is:" [1] 3

|---------------------------------------------------------------------->100.00% [1] "Current loop: 2 out of maximum of 10" [1] "Optimizing Pseudo QTNs..." [1] 5.000000e+05 9.000000e+00 3.480488e+02 8.435696e-02 3.780617e-01 [1] 5.000000e+06 9.000000e+00 3.480488e+02 8.435696e-02 3.780617e-01 [1] 5.000000e+07 9.000000e+00 3.714787e+02 7.304528e-02 4.418982e-01 [1] "seqQTN" [1] 13719 6805 26349 8745 12938 42787 9954 20568 7037 [1] "scanning..." [1] "number of covariates in current loop is:" [1] 12

|---------------------------------------------------------------------->100.00% [1] "Current loop: 3 out of maximum of 10" [1] "Optimizing Pseudo QTNs..." [1] 5.000000e+05 9.000000e+00 2.220364e+02 8.107669e-02 1.804395e-01 [1] 5.000000e+06 9.000000e+00 2.137317e+02 7.631238e-02 1.698363e-01 [1] 5.000000e+07 9.000000e+00 2.258661e+02 7.450450e-02 1.832515e-01 [1] "seqQTN" [1] 25867 24506 24459 34889 44391 9954 13719 42787 7037 8745 20568 6805 12938 26349 [1] "scanning..." [1] "number of covariates in current loop is:" [1] 17

|---------------------------------------------------------------------->100.00% [1] "Current loop: 4 out of maximum of 10" [1] "Optimizing Pseudo QTNs..." [1] 5.000000e+05 9.000000e+00 1.565068e+02 4.534805e-02 1.232688e-01 [1] 5.000000e+06 9.000000e+00 1.565068e+02 4.534805e-02 1.232688e-01 [1] 5.000000e+07 9.000000e+00 1.418569e+02 1.328677e-01 1.087829e-01 [1] "seqQTN" [1] 34889 42787 8745 7037 9954 3926 49355 31345 12826 44391 24459 20568 26349 6805 [15] 13719 12938 24506 [1] "scanning..." [1] "number of covariates in current loop is:" [1] 20

|---------------------------------------------------------------------->100.00% [1] "Current loop: 5 out of maximum of 10" [1] "Optimizing Pseudo QTNs..." [1] 5.000000e+05 9.000000e+00 9.824032e+01 2.221593e-02 9.009003e-02 [1] 5.000000e+06 9.000000e+00 9.824032e+01 2.221593e-02 9.009003e-02 [1] 5.000000e+07 9.000000e+00 9.706491e+01 1.120900e-02 9.153457e-02 [1] "seqQTN" [1] 34889 42787 7037 49355 8745 12826 44391 24459 3686 3926 9954 31345 6805 13719 [15] 26349 12938 24506 [1] "scanning..." [1] "number of covariates in current loop is:" [1] 20

|---------------------------------------------------------------------->100.00% [1] "Current loop: 6 out of maximum of 10" [1] "Optimizing Pseudo QTNs..." [1] 5.000000e+05 9.000000e+00 7.204580e+01 5.812775e-04 8.626922e-02 [1] 5.000000e+06 9.000000e+00 7.204580e+01 5.812775e-04 8.626922e-02 [1] 5.000000e+07 9.000000e+00 6.110482e+01 7.504327e-03 7.484952e-02 [1] "seqQTN" [1] 34889 42787 12826 8745 44391 3686 7037 49355 9954 3926 51954 24459 31345 13719 [15] 26349 6805 12938 24506 [1] "scanning..." [1] "number of covariates in current loop is:" [1] 21

|---------------------------------------------------------------------->100.00% [1] "Current loop: 7 out of maximum of 10" [1] "Optimizing Pseudo QTNs..." [1] 5.000000e+05 9.000000e+00 3.804992e+01 4.775745e-04 7.087834e-02 [1] 5.000000e+06 9.000000e+00 3.804992e+01 4.775745e-04 7.087834e-02 [1] 5.000000e+07 9.000000e+00 3.834452e+01 4.775745e-04 7.087834e-02 [1] "seqQTN" [1] 34889 42787 44391 49355 3686 7037 12826 8745 9954 51954 3926 24459 13719 31345 [15] 6805 26349 12938 24506 [1] "scanning..." [1] "number of covariates in current loop is:" [1] 21

|---------------------------------------------------------------------->100.00% [1] "Significance Level: 9.59711318835294e-07" [1] "Visualization Start..." [1] "Phenotype distribution Plotting..." [1] "PCA plot2d..." [1] "PCA plot3d..." Writing 'MVP.3D090.png' Loading required namespace: magick Writing 'MVP.3D045.png' [1] "SNP_Density Plotting..." [1] "Circular_Manhattan Plotting Rlm7.GLM..." [1] "Circular_Manhattan Plotting Rlm7.MLM..." [1] "Circular_Manhattan Plotting Rlm7.FarmCPU..." [1] "Rectangular_Manhattan Plotting Rlm7.GLM..." [1] "Rectangular_Manhattan Plotting Rlm7.MLM..." [1] "Rectangular_Manhattan Plotting Rlm7.FarmCPU..." [1] "Q_Q Plotting Rlm7.GLM..." [1] "Q_Q Plotting Rlm7.MLM..." [1] "Q_Q Plotting Rlm7.FarmCPU..." [1] "Multracks_Rectangular Plotting Rlm7.GLM..." [1] "Multracks_Rectangular Plotting Rlm7.MLM..." [1] "Multracks_Rectangular Plotting Rlm7.FarmCPU..." [1] "Multraits_Rectangular Plotting..." [1] "Multracks_QQ Plotting Rlm7.GLM..." [1] "Multracks_QQ Plotting Rlm7.MLM..." [1] "Multracks_QQ Plotting Rlm7.FarmCPU..." [1] "Multraits_QQ Plotting Rlm7.GLM..." [1] "Multraits_QQ Plotting Rlm7.MLM..." [1] "Multraits_QQ Plotting Rlm7.FarmCPU..."

-------------------------------------------MVP

ACCOMPLISHED--------------------------------------------# There were 50 or more warnings (use warnings() to see the first 50)

Cheers, Tina

On Wed, 14 Nov 2018 at 21:00, haohao notifications@github.com wrote:

Hi, Tina This seems to be because some individuals have been mistakenly identified. The log show that only 173 individuals have been identified. Have you replaced the individual with S1, S2, S3...?

Haohao

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[hyacz]

Hi, Tina I tried to replace S1, S2, S3... as Carousy, Trilogies, MPT... as you said yesterday. But without encountering errors, Could you please share the original name data with me? It will be very helpful in identifying this issue.

Haohao

[TinaNeik]

Thanks Haohao.

The 68 individual names were the same as given. Do you mean you can run all 68 without any errors?

I can get it to read the individual name but can't fix the 3 missing individuals.

"If you replace S1-S68 with the individual names:

Carousy Trilogies MPT Trisivil 92071_IB1832 Asgora NVT Poddy YHUA Bayrus Velkra X1267 X1268 Deakon Anges Polly Serene Archie Tame Schook 528DH Dingy Telton Thumpy Oscar HY99 Heyol T6 Jackel_1 Jackel_2 Jackel_3 Jackel_4 Garond NDB7 Amazon Tair Bold Margin Tomero Wayine P569 PO1548 ATRBanjo Avand_1 Avand_2 Avand_3 Avand_4 Bell_1 Bell_2 Highlands_1 Highlands_2 Highlands_3 Highlands_4 Torn_1 Torn_2 Cambrial_1 Putty_2 Putty_1 Pollen_2 GCV_1 GCV_2 Gale_1 Gale_2 15.23.4.1_1 CT_1 NKR_1 NKR_2 Extreme_1

It would give 65 or lesser individuals instead of 68"

For my new dataset 176 individuals, I can't get it to read the individual name but when I changed to S1-176, it can read but missing 3 individuals again.

Cheers, Tina

On Wed, 14 Nov 2018 at 23:10, haohao notifications@github.com wrote:

Hi, Tina I tried to replace S1, S2, S3... as Carousy, Trilogies, MPT... as you said yesterday. But without encountering errors, Could you please share the original name data with me? It will be very helpful in identifying this issue.

Haohao

— You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub https://github.com/XiaoleiLiuBio/MVP/issues/28#issuecomment-438695405, or mute the thread https://github.com/notifications/unsubscribe-auth/Al8lkNgbOrke77R2Ro9AaWV7ZoNRe7Ptks5uvDJhgaJpZM4YOb4u .

[hyacz]

Hi, Tina

Yes, I replaced them and worked fine.

> suppressMessages(library(MVP))
> MVP.Data(fileHMP='Rlm7_SNP_68lines_forNumericAll_forTASSEL_ForHaohao.hmp.txt', filePhe='Phenotype_Rlm7_68LinesAllA_ForHaohao.txt')
Preparing data for MVP...
Reading file...
inds: 68    markers:52099

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Preparation for GENOTYPE data is done within 1s
Preparation for Genotype File is done!
Preparation for PHENOTYPE data is Done within 0s
Imputing...
Impute Genotype File is done!
[1] "Principal Component Analysis Start..."
Preparation for PC matrix is done!
Calculate KINSHIP using Vanraden method...
Preparation for Kinship matrix is done!
MVP data prepration accomplished successfully!
> geno <- attach.big.matrix('mvp.imp.geno.desc')
> pheno <- read.table('mvp.phe', header=T)
> map <- read.table('mvp.map', header=T)
> imMVP <- MVP(
+     phe=pheno,
+     geno=geno,
+     map=map,
+     nPC.MLM=3,
+     perc=1,
+     priority="speed",
+     vc.method="EMMA",
+     threshold=0.05,
+     method=c("MLM")
+ )
====================== Welcome to MVP ======================
       A Memory-efficient, Visualization-enhanced, and
             Parallel-accelerated Tool For GWAS
                    __  __  __   __  ___
                   |  \/  | \ \ / / | _ \
                   | |\/| |  \ V /  |  _/
                   |_|  |_|   \_/   |_|     Version: 1.1.0
  Authors: Lilin Yin, Haohao Zhang, and Xiaolei Liu
  Contact: xiaoleiliu@mail.hzau.edu.cn
============================================================
[1] "Input data has 68 individuals, 52099 markers"
[1] "Principal Component Analysis Start..."
[1] "GWAS Start..."
[1] "Mixed Linear Model (MLM) Start ..."
[1] "Calculating Kinship..."
[1] "Variance components..."
[1] "Variance Components Estimation is Done!"
[1] "Eigen-Decomposition..."
[1] "Eigen-Decomposition is Done!"
|-------------------------------------------------->100.00%
[1] "Significance Level: 9.59711318835294e-07"
[1] "Visualization Start..."
[1] "Phenotype distribution Plotting..."
[1] "PCA plot2d..."
[1] "The 3D PCA map has been temporarily disabled, and we will improve this feature in subsequent versions."
[1] "SNP_Density Plotting..."
[1] "Circular_Manhattan Plotting Rlm7.MLM..."
[1] "Rectangular_Manhattan Plotting Rlm7.MLM..."
[1] "Q_Q Plotting Rlm7.MLM..."