Open YangBen0528 opened 1 day ago
Hi: Hello, I encountered an issue when using SbaysRC. I used my GWAS population to generate LD references and executed GWAS using GEMMA to obtain summary data. When I directly validated the summary data in another population, the accuracy obtained was higher than that after weighting with SbaysRC. I checked the sample size and found that the input was fine, and the hsq was abnormally high. Do you have any good solutions? Kind regards, Ben Here are my running results： results.par : Item Mean SD hsq 10.9749770259857 0.647711110114177 nnz 38271.05 6232.44043423856 SigmaSq 0.0916490917280316 0.00397660001837749 ssq 12.0987713527679 0.730961086395931 Var_e 1.14513167917728 0.012551835755401 Var_g 10.9749770259857 0.647711110114177 NumSnp2 34956.39 6581.25949573295 NumSnp3 2867.8525 425.929755736977 NumSnp4 503.837 68.1571062073742 NumSnp5 6.171 3.47228849517099 Vg2 0.31094222369045 0.0569356120350165 Vg3 0.26620443828404 0.0403269561739471 Vg4 0.382677061542869 0.0417649074674193 Vg5 0.0401764618214802 0.0147711218402458 results.AnnoPerSnpHsqEnrichment: Annotation Enrich SD Intercept 0.999999405 7.09572027568613e-07 0D 1.62374721 0.273466559762005 2D 1.19737231 0.231896666704888 3D 0.662701275 0.28818911471323 4D 0.39227584 0.128171865330445 DISTAL_INTERGENIC 2.39601355 0.647321077269452 PROMOTER 0.818551765 0.375984843242972 UTR3 0.941579205 0.207029780081261 UTR5 1.2879516 0.301139731925682 UTR5ANDUTR3 1.6418935003495 3.39991875310119 CONSERVED 0.840545235 0.195210662819869 DOWNSTREAM 0.62083254 0.156141525386743 EXONIC 1.05253687 0.129051768683637 EXONICANDSPLICING 0.70950658578475 1.20848303442641 INTERGENIC 1.3562447 0.103694404393015 INTRONIC 0.845571405 0.0431090441637778 LDSCOREBIN1 4.8343861 0.300501953770503 LDSCOREBIN10 0.259431295 0.0752454864365521 LDSCOREBIN2 0.77595166 0.104165385363006 LDSCOREBIN3 0.453729245 0.10096503548077 LDSCOREBIN4 0.266086295 0.0562499215408764 LDSCOREBIN5 0.41839753 0.0763445392488204 LDSCOREBIN6 0.28466801 0.0634371015570518 LDSCOREBIN7 0.277108475 0.0553868346396177 LDSCOREBIN8 0.32281826 0.0606983842629527 LDSCOREBIN9 0.216645485 0.0629947873364742 MAFBIN1 1.68425805 0.102587283673735 MAFBIN10 0.81393426 0.123093628272624 MAFBIN2 0.470655565 0.0635990581251626 MAFBIN3 0.496783885 0.0850046066913762 MAFBIN4 0.50866567 0.0932770519788249 MAFBIN5 0.798497715 0.13044591868245 MAFBIN6 1.226637535 0.154449133053389 MAFBIN7 0.303684365 0.0676445361497613 MAFBIN8 0.754109075 0.144122495796815 MAFBIN9 0.31974142 0.102524876561169 NCRNA_EXONIC 2.03253118 2.37514519233337 NCRNA_INTRONIC 3.88049702 4.52444160849681 NCRNA_SPLICING 0.734148927 5.60517124584006 NONSYNONYMOUS 1.74297545 0.224060256676429 SPLICING 5.687267005375 4.35200434915957 STOPGAIN 1.522582385 1.66076015274521 STOPLOSS 2.32567010984 5.60984223727103 SYNONYMOUS 0.61402326 0.147451021778317 UNKNOWN 1.7710674765 2.73266836059198 UPSTREAM 1.083180705 0.284721810446438 UPSTREAMANDDOWNSTREAM 0.3837986645 0.263986089119654 maf.value 0.67009058 0.0618082904389738 pi.value 0.595036045 0.072455272038991
Hi: Hello, I encountered an issue when using SbaysRC. I used my GWAS population to generate LD references and executed GWAS using GEMMA to obtain summary data. When I directly validated the summary data in another population, the accuracy obtained was higher than that after weighting with SbaysRC. I checked the sample size and found that the input was fine, and the hsq was abnormally high. Do you have any good solutions? Kind regards, Ben Here are my running results： results.par : Item Mean SD hsq 10.9749770259857 0.647711110114177 nnz 38271.05 6232.44043423856 SigmaSq 0.0916490917280316 0.00397660001837749 ssq 12.0987713527679 0.730961086395931 Var_e 1.14513167917728 0.012551835755401 Var_g 10.9749770259857 0.647711110114177 NumSnp2 34956.39 6581.25949573295 NumSnp3 2867.8525 425.929755736977 NumSnp4 503.837 68.1571062073742 NumSnp5 6.171 3.47228849517099 Vg2 0.31094222369045 0.0569356120350165 Vg3 0.26620443828404 0.0403269561739471 Vg4 0.382677061542869 0.0417649074674193 Vg5 0.0401764618214802 0.0147711218402458 results.AnnoPerSnpHsqEnrichment: Annotation Enrich SD Intercept 0.999999405 7.09572027568613e-07 0D 1.62374721 0.273466559762005 2D 1.19737231 0.231896666704888 3D 0.662701275 0.28818911471323 4D 0.39227584 0.128171865330445 DISTAL_INTERGENIC 2.39601355 0.647321077269452 PROMOTER 0.818551765 0.375984843242972 UTR3 0.941579205 0.207029780081261 UTR5 1.2879516 0.301139731925682 UTR5ANDUTR3 1.6418935003495 3.39991875310119 CONSERVED 0.840545235 0.195210662819869 DOWNSTREAM 0.62083254 0.156141525386743 EXONIC 1.05253687 0.129051768683637 EXONICANDSPLICING 0.70950658578475 1.20848303442641 INTERGENIC 1.3562447 0.103694404393015 INTRONIC 0.845571405 0.0431090441637778 LDSCOREBIN1 4.8343861 0.300501953770503 LDSCOREBIN10 0.259431295 0.0752454864365521 LDSCOREBIN2 0.77595166 0.104165385363006 LDSCOREBIN3 0.453729245 0.10096503548077 LDSCOREBIN4 0.266086295 0.0562499215408764 LDSCOREBIN5 0.41839753 0.0763445392488204 LDSCOREBIN6 0.28466801 0.0634371015570518 LDSCOREBIN7 0.277108475 0.0553868346396177 LDSCOREBIN8 0.32281826 0.0606983842629527 LDSCOREBIN9 0.216645485 0.0629947873364742 MAFBIN1 1.68425805 0.102587283673735 MAFBIN10 0.81393426 0.123093628272624 MAFBIN2 0.470655565 0.0635990581251626 MAFBIN3 0.496783885 0.0850046066913762 MAFBIN4 0.50866567 0.0932770519788249 MAFBIN5 0.798497715 0.13044591868245 MAFBIN6 1.226637535 0.154449133053389 MAFBIN7 0.303684365 0.0676445361497613 MAFBIN8 0.754109075 0.144122495796815 MAFBIN9 0.31974142 0.102524876561169 NCRNA_EXONIC 2.03253118 2.37514519233337 NCRNA_INTRONIC 3.88049702 4.52444160849681 NCRNA_SPLICING 0.734148927 5.60517124584006 NONSYNONYMOUS 1.74297545 0.224060256676429 SPLICING 5.687267005375 4.35200434915957 STOPGAIN 1.522582385 1.66076015274521 STOPLOSS 2.32567010984 5.60984223727103 SYNONYMOUS 0.61402326 0.147451021778317 UNKNOWN 1.7710674765 2.73266836059198 UPSTREAM 1.083180705 0.284721810446438 UPSTREAMANDDOWNSTREAM 0.3837986645 0.263986089119654 maf.value 0.67009058 0.0618082904389738 pi.value 0.595036045 0.072455272038991