Genetic Map Files (for RFMix)

[cwarden45]

Hi Alicia,

First, I thought it might be nice to point out that I was referred here from another site (for the chromosome painting portion of your scripts):

https://github.com/slowkoni/rfmix/issues/9

Second, I noticed that you provide a link to download the genetic map files used by RFMix. I'm guessing those are within the .tgz files (which I am working on downloading).

Just out of curiosity, did you know of a direct link to those genetic mapping files? I was having some difficulty finding them within a subfolder of the FTP, but that doesn't mean they aren't there somewhere.

I would guess that you already know about it, but I did find this HapMap file (from a Biostars discussion):

https://ftp.hapmap.org/hapmap/recombination/2011-01_phaseII_B37/

However, I'm guessing what I can download from 1000 Genomes will cover more sites (but something like that smaller file is what I am looking for).

Best Wishes, Charles

[cwarden45]

It is definitely an "unoffiical" solution, but I have uploaded the 1000 Genomes genetic map files on Google Drive:

https://drive.google.com/open?id=1z1a961djGVH3zWaAkFithHLqHx8ZEnkt

So, this may save some time for other users.

Also, I will be tidy, and close this ticket :)

[cwarden45]

That said, if you follow the tutorial (and not try to go directly to running SHAPEIT + rfmix on your samples), I see that you do need other files from the link (which I'm not providing separately in the above link).

[shikhaMeda]

Hi Charles, This is my first time working with genetic map files and I saw the files for different chr on your drive. It would be great help if you can please give me an idea on how to get genetic map files for a particular population from 1000 genome project?

[cwarden45]

Hi @shikhaMeda - I don't do this on a regular basis, so I would have to refresh my memory about the specifics (and I may or may not be able to provide enough guidance to do that, exactly).

In other words, sometimes, you may need to use code to manually create a smaller reference file.

However, I think sometimes selecting specific subsets can be handled by some programs.

For example, when running STITCH, I think these are the relevant commands from the following programs:

run_STITCH-REF286.R:

        STITCH(
            bamlist = bam_list,
            sampleNames_file = name_list,
            outputdir = output_folder,
            method = "diploid",
            regenerateInput = TRUE,
            regionStart = STITCH_start,
            regionEnd = STITCH_end,
            buffer = buffer,
            niterations = 1,
            chr = chr,
            reference_populations = c("CEU", "GBR", "ACB"),#this is a reference set of 286 samples
    #       reference_populations = c("CEU"),#this is a reference set of 99 samples     
            reference_haplotype_file = human_reference_haplotype_file,
            reference_sample_file = human_reference_sample_file,
            reference_legend_file = human_reference_legend_file,
            posfile = human_posfile,
            shuffleHaplotypeIterations = NA,
            refillIterations = NA,
            K = human_K, tempdir = temp_folder, nCores = 1, nGen = human_nGen)

run_STITCH-REF99-DOWN2.R:

        STITCH(
            bamlist = bam_list,
            sampleNames_file = name_list,
            outputdir = output_folder,
            method = "diploid",
            regenerateInput = TRUE,
            regionStart = STITCH_start,
            regionEnd = STITCH_end,
            buffer = buffer,
            niterations = 1,
            chr = chr,
            downsampleFraction = 0.5,
    #       reference_populations = c("CEU", "GBR", "ACB"),#this is a reference set of 286 samples
            reference_populations = c("CEU"),#this is a reference set of 99 samples     
            reference_haplotype_file = human_reference_haplotype_file,
            reference_sample_file = human_reference_sample_file,
            reference_legend_file = human_reference_legend_file,
            posfile = human_posfile,
            shuffleHaplotypeIterations = NA,
            refillIterations = NA,
            K = human_K, tempdir = temp_folder, nCores = 1, nGen = human_nGen)

I think you could change the reference sets for GLIMPSE, but the computational requirements were lower and I don't think I did that so much.

So, I am sorry that this is not precisely an answer to your question, but I hope that might help.

I don't think that I can guarantee being able to help you, but do you have a specific point in Alicia's scripts that you are having difficulty with? If she can't provide assistance, then I can see if that was something I also had a hard time with (and whether I found a solution, when I have a chance to sort through what I did).

[shikhaMeda]

Thanks for the help Charles, I think I'll try and use something like STITCH and see if it helps me get a subset.

---

From: Charles Warden @.> Sent: 02 April 2021 23:54 To: armartin/ancestry_pipeline @.> Cc: shikhaMeda @.>; Mention @.> Subject: Re: [armartin/ancestry_pipeline] Genetic Map Files (for RFMix) (#11)

Hi @shikhaMedahttps://github.com/shikhaMeda - I don't do this on a regular basis, so I would have to refresh my memory about the specifics (and I may or may not be able to provide enough guidance to do that, exactly).

In other words, sometimes, you may need to use code to manually create a smaller reference file.

However, I think sometimes selecting specific subsets can be handled by some programs.

For example, when running STITCHhttps://github.com/cwarden45/DTC_Scripts/tree/master/Nebula/Gencove/STITCH, I think these are the relevant commands from the following programs:

run_STITCH-REF286.Rhttps://github.com/cwarden45/DTC_Scripts/blob/master/Color/lcWGS_Genotype_Estimations/run_STITCH-REF286.R:

            STITCH(
                    bamlist = bam_list,
                    sampleNames_file = name_list,
                    outputdir = output_folder,
                    method = "diploid",
                    regenerateInput = TRUE,
                    regionStart = STITCH_start,
                    regionEnd = STITCH_end,
                    buffer = buffer,
                    niterations = 1,
                    chr = chr,
                    reference_populations = c("CEU", "GBR", "ACB"),#this is a reference set of 286 samples
    #               reference_populations = c("CEU"),#this is a reference set of 99 samples
                    reference_haplotype_file = human_reference_haplotype_file,
                    reference_sample_file = human_reference_sample_file,
                    reference_legend_file = human_reference_legend_file,
                    posfile = human_posfile,
                    shuffleHaplotypeIterations = NA,
                    refillIterations = NA,
                    K = human_K, tempdir = temp_folder, nCores = 1, nGen = human_nGen)

run_STITCH-REF99-DOWN2.Rhttps://github.com/cwarden45/DTC_Scripts/blob/master/Color/lcWGS_Genotype_Estimations/run_STITCH-REF99-DOWN2.R:

            STITCH(
                    bamlist = bam_list,
                    sampleNames_file = name_list,
                    outputdir = output_folder,
                    method = "diploid",
                    regenerateInput = TRUE,
                    regionStart = STITCH_start,
                    regionEnd = STITCH_end,
                    buffer = buffer,
                    niterations = 1,
                    chr = chr,
                    downsampleFraction = 0.5,
    #               reference_populations = c("CEU", "GBR", "ACB"),#this is a reference set of 286 samples
                    reference_populations = c("CEU"),#this is a reference set of 99 samples
                    reference_haplotype_file = human_reference_haplotype_file,
                    reference_sample_file = human_reference_sample_file,
                    reference_legend_file = human_reference_legend_file,
                    posfile = human_posfile,
                    shuffleHaplotypeIterations = NA,
                    refillIterations = NA,
                    K = human_K, tempdir = temp_folder, nCores = 1, nGen = human_nGen)

I think you could change the reference sets for GLIMPSEhttps://github.com/cwarden45/DTC_Scripts/tree/master/Nebula/Gencove/GLIMPSE, but the computational requirements were lower and I don't think I did that so much.

So, I am sorry that this is not precisely an answer to your question, but I hope that might help.

I don't think that I can guarantee being able to help you, but do you have a specific point in Alica's scripts that you are having difficulty with? If she can't provide assistance, then I can see if that was something I also had a hard time with (and whether I found a solution, when I have a chance to sort through what I did).

— You are receiving this because you were mentioned. Reply to this email directly, view it on GitHubhttps://github.com/armartin/ancestry_pipeline/issues/11#issuecomment-812653014, or unsubscribehttps://github.com/notifications/unsubscribe-auth/ASAMUF6Q47SQIC3V2E2EE23TGYDVXANCNFSM4HC3K2OQ.

[cwarden45]

You are welcome, @shikhaMeda.

However, to be clear, STITCH is used for imputations in lcWGS data. So, I am trying to say that your downstream program may have built-in ways to filter for certain samples (with commonly used file formats), if your eventual goal was to use something like STITCH.

I am not suggesting that you use STITCH to subset reference samples to use for a different program. I think that might not work, unless temporary files are saved (in a format that is usable for other programs).

[cwarden45]

Also, I see that Alicia has left a comment at the top of the README:

"While I hope these scripts continue to be helpful, I am no longer maintaining or developing them, so consider code as is."

So, she may not respond to your question. However, I think specifying a point in the scripts on this repository may be the best chance of getting assistance. If different than any other issues, you could try submitting a new issue (to see if the general public can respond).

[cwarden45]

If focusing on Alicia's scripts, did you see this set of demo files to download?

https://www.dropbox.com/sh/zbwka9u09f73gwo/AABc6FNl9fVBPjby8VQWzyeXa?dl=0

I initially overlooked that, but I think it was a helpful starting point.

[shikhaMeda]

I wasn't really focussing on Alicia's script. I came across her repository because I saw your comment in which you mentioned that you have genetic map files for all the chromosomes from 1000 genome vcf on your drive. From there I downloaded the genetic map file for chromosome 22 and I wanted to know that if the variant positions in the first column of that file is for all the populations?

Actually, I am working on an assignment where I have to take the GIH population from 1000 genome vcf for chr 22 and with this data I need to create another .PED and .MAP files containing the snp's and centimorgan value for the specific population. In this regard I found the files you uploaded for all the chromosomes very helpful and wanted to use that file but just wanted to know if the variant information was for all the populations.

P.S. I have attached the file that I downloaded from your drive, which might support my question.

---

From: Charles Warden @.> Sent: 03 April 2021 01:04 To: armartin/ancestry_pipeline @.> Cc: shikhaMeda @.>; Mention @.> Subject: Re: [armartin/ancestry_pipeline] Genetic Map Files (for RFMix) (#11)

If focusing on Alicia's scripts, did you see this set of demo files to download?

https://www.dropbox.com/sh/zbwka9u09f73gwo/AABc6FNl9fVBPjby8VQWzyeXa?dl=0

I initially overlooked that, but I think it was a helpful starting point.

— You are receiving this because you were mentioned. Reply to this email directly, view it on GitHubhttps://github.com/armartin/ancestry_pipeline/issues/11#issuecomment-812679300, or unsubscribehttps://github.com/notifications/unsubscribe-auth/ASAMUF64ZDRPCG3Z3VBMXYLTGYL4LANCNFSM4HC3K2OQ.

position COMBINED_rate(cM/Mb) Genetic_Map(cM) 16051347 9.6640973708 0 16052618 9.7078062447 0.0123386217370137 16053624 9.7138922111 0.0221107973013803 16053659 9.716343506 0.0224508693240903 16053758 9.707808785 0.0234119423938053 16054713 9.5801157234 0.0325609529096523 16054960 9.5805112114 0.0349273391788681 16055070 6.2364537599 0.0356133490924571 16055171 6.2367174178 0.0362432575516549 16055207 6.2427493746 0.0364679965291405 16059734 3.9826855685 0.05449761409774 16061016 3.9611463686 0.0595758037422852 16061342 3.9611463686 0.0608671374584488 16069374 3.8490681814 0.0917828530914536 16072292 3.8361315759 0.102976685029930 16079493 3.8361315759 0.130600668507986 16099437 3.8395844392 0.207177340563390 16100059 3.843712102 0.209568129490834 16102325 3.8442446297 0.218279187821735 16103902 3.841044501 0.224336514999812 16110036 3.8401104866 0.247891752724616 16112336 3.8380568509 0.256719283481686 16136814 3.9599609771 0.35365120827914 16140628 7.1357779607 0.38086706542125 16173179 7.1344157513 0.613099432541816 16227431 7.7588863353 1.03403453400451 16231423 7.758128546 1.06500498316014 16252600 8.2936027089 1.24063860772652 16267521 8.2928451412 1.36437615007836 16317416 8.4678641827 1.78688023347418 16344414 8.6295808956 2.01986165849359 16344572 8.6363169331 2.02122619656902 16345582 8.6430472952 2.02995567433717 16345954 8.6447281629 2.03317151321377 16346071 8.4467069144 2.03415977792275 16347194 6.0762272273 2.04098338109901 16351604 5.7065774019 2.06614938744139 16351852 3.0363696001 2.06690240710222 16364825 2.51826389 2.09957184454719 16365495 0.7453698944 2.10007124237643 16407120 0.1364496279 2.10575095813777 16473585 0.1364498034 2.11482009432075 16489239 0.5395889252 2.12326681935583 16494095 0.5395050468 2.12588665586309 16494187 0.6572459529 2.12594712249076 16494473 0.6507599658 2.12613323984098 16494682 0.5121806841 2.12624028560396 16504399 0.1252975627 2.12745780202071 16587208 0.2506375924 2.14821285040976 16598987 0.3840983843 2.15273714527843 16609899 0.4167777364 2.15728502393803 16616145 0.4426071878 2.16004954843303 16619031 0.5187491818 2.16154665857170 16629152 0.6087594852 2.16770791332141 16630874 0.727659584 2.16896094312506 16631921 0.743472805 2.16973935915190 16632034 0.7567552782 2.16982487249833 16633820 0.884654203 2.17140486490489 16644918 0.8914971326 2.18129870008249 16649282 0 2.18129870008249 16662015 0 2.18129870008249 16682084 0 2.18129870008249 16851640 0.1743574975 2.21086205992860 16855274 0.1709176265 2.21148317458330 16855303 0.1686080445 2.21148806421659 16855618 0.1663343705 2.21154045954330 16855788 0.1634974361 2.21156825410743 16858253 0.156429495 2.21195385281261 16858287 0.148899463 2.21195891539435 16858684 0.1431069079 2.21201572883679 16858968 0.1371288974 2.21205467344365 16862245 0.1392390631 2.21251095985343 16863070 3.1924738134 2.21514475074948 16865262 3.8744446603 2.22363753344486 16870393 2.1256389599 2.23454418694811 16871440 1.9987504029 2.23663687861994 16877135 1.6764223769 2.24618410405639 16878039 1.5829421403 2.24761508375122 16878423 0.5042536399 2.24780871714894 16888577 0.5084612614 2.25297163279720 16888900 0.5131954798 2.25313739493717 16890125 0.515818962 2.25376927316562 16890296 0.5167148867 2.25385763141125 16890307 0.5155704317 2.25386330268600 16890816 0.5165318432 2.25412621739418 16893449 0.5213411448 2.25549890862844 16894090 0.5257647004 2.2558359238014 16894264 0.5319052866 2.25592847532127 16896164 0.5697869814 2.25701107058593 16896210 0.6026675092 2.25703879329135 16896762 0.6232498887 2.25738282722991 16896882 0.6414545526 2.25745980177622 16900134 0.7170777478 2.25979173861207 16902425 0.7717842956 2.26155989643329 16905893 0.9686409902 2.26491914338730 16911574 1.1363593526 2.27137480086942 16911828 1.2931047088 2.27170324946546 16915998 3.4817183041 2.28622201479356 16918335 2.4853280269 2.29203022639242 16918847 1.8358252336 2.29297016891203 16918867 0.1015480367 2.29297219987276 16938376 0.1023668 2.29496927377396 16952978 0.1390407883 2.29699954736472 16953120 0.1657732943 2.29702308717251 16957305 0.1797871953 2.29777549658484 16963545 0.2051593742 2.29905569107984 16966809 0.2204967666 2.29977539252603 16968466 0.2231185904 2.30014510003032 16970900 0.2245622352 2.30069168451080 16973226 0.2243019332 2.30121341080742 16983606 0.2248701343 2.30354756280145 16989290 0.2245855851 2.30482410726716 16990420 0.224293197 2.30507755857977 16990526 0.2240260167 2.30510130533754 17030710 0.2246539392 2.31412879923036 17030792 0.2786655263 2.31415164980351 17032698 0.6070030743 2.31530859766313 17049825 0.8443605285 2.32976996043475 17049907 1.0712608751 2.32985780382651 17050088 1.2558781707 2.33008511777540 17054027 1.2552257061 2.33502945183173 17054720 1.5047962923 2.33607227566229 17056022 1.750157323 2.33835098049684 17057138 1.9236703048 2.34049779655700 17057597 2.1824341234 2.34149953381964 17063359 2.8259244942 2.35778251075522 17065079 3.5216768573 2.36383979494977 17066732 8.7820126371 2.3783564618389 17067504 9.0356564462 2.38533198861537 17068748 8.9638318801 2.39648299547421 17070109 8.8367412262 2.40850980028307 17071043 5.1555179762 2.41332505407284 17072483 3.4039677429 2.41822676762262 17073066 2.2180989431 2.41951991930644 17075353 2.2165153306 2.42458908986753 17080378 2.3150514862 2.43622222358568 17084609 0.7596942153 2.43943648981061 17084762 0.5035869597 2.43951353861545 17087656 0.5226090365 2.44102596916708 17087705 0.5434079102 2.44105259615468 17089639 9.8245307064 2.46005323854086 17090474 9.8245307064 2.4682567216807 17094749 0.6398794642 2.47099220639016 17096707 0.6413819925 2.47224803233147 17096864 0.6414900352 2.472348746267 17097416 0.6450497566 2.47270481373264 17099107 0.6588489394 2.47381892728917 17100497 0.6076896623 2.47466361591976 17100911 0.4651352904 2.47485618192999 17101987 0.3998927372 2.47528646651522 17102604 0.3507484075 2.47550287828264 17103078 0.3095472552 2.47564960368161 17103717 0.2975758724 2.47583975466407 17105666 0.2957014838 2.47641607685600 17106294 0.2907673477 2.47659867875035 17107266 0.2901423325 2.47688069709754 17107330 0.220109057 2.47689478407719 17108619 0.2191700439 2.47717729426378 17109150 0.1360304804 2.47724952644887 17112280 0.1012518219 2.47756644465142 17112342 0.0675620166 2.47757063349645 17116233 0.0675620166 2.47783351730304 17121886 0.0671753236 2.47821325940735 17123478 0.0671682378 2.47832019124193 17130409 0.0672330624 2.47878618359742 17136658 0.0672413535 2.47920637481544 17139227 0.0672413535 2.47937911785258 17140023 0.0672494454 2.47943264841112 17149033 0.0708338168 2.48007086110049 17149300 0.0756381681 2.48009105649137 17152611 0.2238920369 2.48083236302555 17154984 1.1729311501 2.48361572864474 17154992 1.9419766862 2.48363126445823 17155383 2.0356151615 2.48442718998637 17161286 2.458896034 2.49894205327507 17164372 1.9654828634 2.50500753339153 17164478 1.5021667295 2.50516676306485 17164773 1.2594389362 2.50553829755103 17169522 1.1262541332 2.5108868784296 17171377 1.1172629293 2.51295940116345 17173913 1.114417685 2.51578556441261 17175288 1.1079992276 2.51730906335056 17177075 1.1046473202 2.51928306811176 17177470 1.1068277264 2.51972026506369 17177794 1.1065699156 2.52007879371634 17177906 1.1044914931 2.52020249676357 17178015 1.1074785168 2.5203232119219 17178213 1.3079756955 2.52058219110961 17178230 1.617751185 2.52060969287975 17178332 2.3737770602 2.52085181813989 17178586 3.688967248 2.52178881582089 17180510 5.2201510904 2.53183238651882 17183103 5.2201107962 2.54536813381336 17187276 5.2201436305 2.56715179318344 17187577 5.2199044454 2.56872298442150 17187689 5.1980442478 2.56930516537726 17187719 5.1769477092 2.56946047380853 17188058 5.1621749571 2.57121045111899 17189132 5.1617052225 2.57675412252796 17189194 5.1615343663 2.57707413765867 17189718 5.1615343663 2.57977878166661 17190111 5.1598049838 2.58180658502524 17190730 5.1556209654 2.58499791440282 17192143 4.9730971103 2.59202490061968 17195268 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[cwarden45]

Oh, OK. Thank you for the explanation, @shikhaMeda.

For maximal reproducibity, I think you probably should reference the larger file here:

https://ftp.hapmap.org/hapmap/recombination/2011-01_phaseII_B37/

However, Alicia's scripts (and I think other scripts) only require a subset of that large file. The original file is larger to download, but you do need to understand that is where the files are coming from (and how they were created).

For example, I did not create any of those files myself. That is important to make clear, but I don't think I can (or should) say too much more on the discussion about running the ancestry scripts from Alicia (if that is not what you are trying to run).

I hope that helps!

[shikhaMeda]

I understand now where the files came from. Also, I unzipped the file (1000GP_Phase3.tgz) and got those files. I just have to work on getting subsets as per the specific populations now. Many Thanks for all the help today, I understand the data much better now.

---

From: Charles Warden @.> Sent: 03 April 2021 01:29 To: armartin/ancestry_pipeline @.> Cc: shikhaMeda @.>; Mention @.> Subject: Re: [armartin/ancestry_pipeline] Genetic Map Files (for RFMix) (#11)

Oh, OK.

For maximal reproducibity, I think you probably should reference the larger file here:

https://ftp.hapmap.org/hapmap/recombination/2011-01_phaseII_B37/

However, Alicia's scripts (and I think other scripts) only require a subset of that file. The original file is larger to download, but you do need to understand that is where the files are coming from (and how they were created).

For example, I did not create any of those files myself. That is important to make clear, but I don't think I can or should say too much more on the discussion about running the ancestry scripts from Alicia.

I hope that helps!

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