Power of Inclusion: enhancing polygenic prediction with admixed individuals

Tanigawa and Kellis. Am J Hum Genet. (2023).


Phenotype: Red blood cell (erythrocyte) distribution width


Erythrocyte dist. width iPGS coefficients

Our FAQ page shows the description of the file format and how you may use iPGS coefficients in your research.


iPGS prediction in the held-out test set individuals

We compared the polygenic prediction from our iPGS model and the phenotype values using the held-out test set individuals in UK Biobank. Note the difference in the number of individuals in the five population groups.

/static/data/tanigawakellis2023/per_trait/INI30070/INI30070.WB.PGS_vs_phe.png
/static/data/tanigawakellis2023/per_trait/INI30070/INI30070.NBW.PGS_vs_phe.png
/static/data/tanigawakellis2023/per_trait/INI30070/INI30070.SA.PGS_vs_phe.png
/static/data/tanigawakellis2023/per_trait/INI30070/INI30070.Afr.PGS_vs_phe.png
/static/data/tanigawakellis2023/per_trait/INI30070/INI30070.others.PGS_vs_phe.png

Predictive performance

Population Model Metric Predictive Performance 95% CI P-value
Population Model Metric Predictive Performance 95% CI P-value
white BritishCovariate-only modelR20.013[0.011, 0.014]1.4x10-186
white BritishGenotype-only modelR20.079[0.076, 0.083]<1.0x10-300
white BritishFull model (covariates and genotypes)R20.092[0.088, 0.096]<1.0x10-300
Non-British whiteCovariate-only modelR20.021[0.011, 0.032]6.8x10-15
Non-British whiteGenotype-only modelR20.087[0.068, 0.107]7.1x10-58
Non-British whiteFull model (covariates and genotypes)R20.106[0.085, 0.127]1.6x10-70
South AsianCovariate-only modelR20.015[0.003, 0.027]4.1x10-06
South AsianGenotype-only modelR20.052[0.030, 0.075]1.6x10-18
South AsianFull model (covariates and genotypes)R20.066[0.042, 0.091]3.7x10-23
AfricanCovariate-only modelR20.008[-0.002, 0.018]2.2x10-03
AfricanGenotype-only modelR20.018[0.003, 0.033]4.5x10-06
AfricanFull model (covariates and genotypes)R20.026[0.009, 0.044]2.9x10-08
OthersCovariate-only modelR20.027[0.020, 0.034]5.9x10-48
OthersGenotype-only modelR20.067[0.057, 0.078]1.4x10-119
OthersFull model (covariates and genotypes)R20.088[0.076, 0.100]1.6x10-156

The predictive performance (R2), its 95% confidence interval (CI), and statistical significance (P-value) are shown for each population in UK Biobank in the held-out test set. The "model" column indicates whether the predictive performance is from the covariate-terms alone (covariate-only model), PGS terms alone (Genotype-only model), or the full model containing both PGS and covariate terms. We used the following sets of covariates in our analysis: age, sex, age2, age*sex, Townsend deprivation index, and genotype PCs (PC1-PC18). Please refer to our publication for a more detailed description of the methods.


Coefficients (BETA) of PGS models

/static/data/tanigawakellis2023/per_trait/INI30070/INI30070.BETAs.png

We show the coefficients (BETA) of PGS models. Our iPGS model selected 12557 variants with non-zero coefficients. The genetic variants with the large absolute values of coefficients are annotated in the plot. There is no guarantee that our iPGS model selects causal variants. We use the GRCh37/hg19 reference genome.

The top 100 genetic variants with the largest absolute value of coefficients

CHROM POS Variant Variant ID Effect allele Consequence Gene symbol Beta
CHROM POS Variant Variant ID Effect allele Consequence Gene symbol Beta
X153763492X:153763492:T:Crs1050829CPAVsG6PD-0.115
6259182256:25918225:T:Crs80215559CIntronicSLC17A2-0.096
6260911796:26091179:C:Grs1799945GPAVsHFE-0.091
223746959022:37469590:C:Trs387907018TPAVsTMPRSS6-0.075
111626109811:16261098:C:Ars17539593AIntronicSOX60.063
136919971:3691997:AGTCAGCCTAGGGGCTGT:Ars566629828APTVsSMIM10.061
159151226715:91512267:G:Trs2290202TIntronicPRC1, PRC1-AS10.058
142349427714:23494277:A:Grs8013143GIntronicPSMB50.055
61354190186:135419018:T:Crs9399137CIntronicHBS1L-0.046
511049385:1104938:C:Trs35188965TIntronicSLC12A7-0.044
172718763617:27187636:G:Trs9279TUTRERAL10.043
51274337985:127433798:A:Grs1112956GIntronicSLC12A20.040
194541564019:45415640:G:Ars445925AOthersAPOC10.037
12480394511:248039451:C:Trs3811444TPAVsTRIM58-0.037
184384203218:43842032:G:Ars11082518AIntronicC18orf250.034
102485777810:24857778:G:Trs12569671TOthers0.034
91361376579:136137657:C:Trs8176693TIntronicABO-0.033
71002402967:100240296:A:Grs2075672GIntronicTFR2-0.032
287502662:8750266:A:Grs3856447GIntronicAC011747.60.032
125464997812:54649978:C:Trs79880068TIntronicCBX5-0.032
7330651027:33065102:T:Crs17170180CIntronicAVL9, NT5C3A0.031
51273575265:127357526:C:Trs17764730TOthersCTC-228N24.3-0.030
184383370118:43833701:T:TCTGrs34068795TCTGPAVsC18orf250.029
125468588012:54685880:C:Trs35979828TIntronicRP11-968A15.8-0.029
184385827118:43858271:C:Trs7506126TOthers-0.029
11585771091:158577109:A:Crs857685CPAVsOR10Z10.028
191300238419:13002384:T:Crs3745647CPAVsGCDH0.027
12036524441:203652444:A:Grs1419114GPCVsATP2B40.026
147466664114:74666641:A:Grs887595GUTRLIN520.026
1257536381:25753638:C:Trs926438TIntronicRHCE0.026
116157138211:61571382:G:Ars174549AUTRFADS1-0.025
51505670175:150567017:A:Grs61745454GPAVsCCDC69-0.024
8415436758:41543675:G:Ars34664882APAVsANK1-0.023
104595376710:45953767:A:Grs7908745GPAVsMARCH80.022
31957960493:195796049:G:Trs4927866TIntronicTFRC0.022
177612186417:76121864:A:Grs2748427GPAVsTMC60.021
104611189510:46111895:G:Ars74436700APAVsZFAND40.021
1123036811:230368:C:Trs536715TIntronicSIRT30.021
12480392941:248039294:G:Ars1339847APAVsTRIM580.021
223750984422:37509844:T:Crs228928COthersTMPRSS60.020
203567308220:35673082:T:Crs6124577CIntronicRBL1-0.020
1118563781:11856378:G:Ars1801133APAVsMTHFR0.020
146526746914:65267469:T:Crs230703CPAVsSPTB0.020
91319038819:131903881:C:Trs3124512TIntronicPPP2R4-0.020
6419251596:41925159:G:Ars9349205AIntronicCCND30.020
61095866786:109586678:G:Ars932222AIntronicC6orf183-0.019
159151206715:91512067:G:Ars2290203AIntronicPRC1, PRC1-AS10.019
173103649317:31036493:A:Grs9912761GIntronicMYO1D-0.018
2607254512:60725451:G:Crs7606173CIntronicBCL11A, AC009970.1-0.018
6301284426:30128442:C:Trs12212092TPAVsTRIM100.018
1110045660411:100456604:C:Trs11224302TOthers-0.018
12302956911:230295691:G:Ars4846914AIntronicGALNT20.018
191130355419:11303554:A:Grs17616661GPAVsKANK2-0.018
19440975619:4409756:A:Grs2230636GPAVsCHAF1A0.018
8198197248:19819724:C:Grs328GPTVsLPL0.018
194542294619:45422946:A:Grs4420638GOthersAPOC1-0.018
174406102317:44061023:G:Ars62063786APAVsMAPT0.018
510675355:1067535:A:Grs3789199GIntronicSLC12A7-0.017
174230464417:42304644:G:Ars7222349AOthersRP5-882C2.2, SHC1P2-0.017
163140457116:31404571:T:Crs8050500COthersITGAD-0.017
8416304058:41630405:G:Ars4737009AIntronicANK10.017
71002186317:100218631:C:Trs41295942TPAVsTFR2-0.017
81264817478:126481747:A:Grs2980875GIntronicRP11-136O12.20.017
510192835:1019283:C:Ars77792413AIntronicNKD2-0.017
61354271446:135427144:C:Ars9376092AOthersHBS1L-0.017
203110538920:31105389:C:Trs6119879TIntronicC20orf1120.017
1111664891711:116648917:G:Crs964184CUTRZNF2590.017
12709019312:7090193:A:Grs1984564GPAVsLPCAT3-0.017
510905345:1090534:G:Ars6864667AIntronicSLC12A70.017
1311337193813:113371938:T:Crs282568COthersATP11A-0.016
1238474641:23847464:C:Ars2075995APAVsE2F2-0.016
137116891:3711689:T:Crs6667255CIntronicLRRC47-0.016
511166775:1116677:G:Ars13165027AOthersSLC12A7-0.016
8262333258:26233325:G:Ars1865305AOthersPPP2R2A, SDAD1P10.016
194937731919:49377319:A:Grs610308GPAVsPPP1R15A-0.016
223088852722:30888527:C:Trs17738540TPAVsSEC14L40.016
195814471519:58144715:A:Grs9749449GPTVsZNF211-0.016
213512629721:35126297:G:Ars2834257AIntronicITSN1, AP000304.120.016
510750515:1075051:A:Grs11133613GIntronicSLC12A7-0.016
7178136777:17813677:C:Trs2723520TOthers-0.015
1621264916:212649:C:Trs3785309TIntronicHBM-0.015
145875642114:58756421:A:Grs7149735GIntronicRP11-349A22.5, C14orf370.015
31422108013:142210801:G:Ars7630115AIntronicATR0.015
1212116351812:121163518:C:Ars2239760AOthersRP11-173P15.5, ACADS0.015
510369625:1036962:C:Ars34413936APTVsNKD2-0.015
8987421598:98742159:C:Trs2449515TOthersMTDH-0.014
948568779:4856877:G:Ars10758658AIntronicRCL10.014
173131095217:31310952:T:Crs3919457CIntronicSPACA3-0.014
8986914708:98691470:A:Grs2438224GIntronicMTDH-0.014
225096220822:50962208:T:Grs12148GPCVsSCO2-0.014
191068164119:10681641:A:Grs4804509GOthersKRI1, CDKN2D-0.014
174410947417:44109474:G:Ars7220988APAVsKANSL1-0.014
51533633345:153363334:G:Ars390299AOthers0.014
5720560995:72056099:T:Grs10942402GIntronicCTC-347C20.10.014
41450564874:145056487:C:Trs13103731TIntronicGYPA, GYPB-0.014
2277309402:27730940:T:Crs1260326CPAVsGCKR0.014
1311347982013:113479820:A:Grs368865GPAVsATP11A0.014
191725215119:17252151:T:Crs35365035CIntronicMYO9B0.014
61096164206:109616420:T:Crs9374080CIntronicCCDC162P-0.014
125246643912:52466439:T:Crs7979353CIntronicC12orf440.014

There is no guarantee that our iPGS model selects causal variants. We show the top 100 variants with the largest effect size (BETA). To see 12557 variants included in our iPGS model, please download the iPGS coefficients by clicking the download button. We use the GRCh37/hg19 reference genome.


Follow-up analysis

There are several ways to use the resource in your research. First, you may use our iPGS coefficients and compute individual-level polygenic scores for your cohort. Second, you may also investigate the genetic variants with non-zero coefficients and their annotated genes to learn more about biology by taking advantage of the sparsity of our iPGS models. For your convenience, here we suggest several resources as an example of follow-up analysis. We do not intend to cover all the relevant follow-up analyses.

Using iPGS coefficients

By clicking the download button above, you may download the iPGS coefficients. Our FAQ page shows the description of file format and how you may use iPGS coefficients in your research.

HaploReg

HaploReg is a tool for exploring annotations of the non-coding genome at variants on haplotype blocks. The button above submits the top 100 genetic variants with the largest absolute value of coefficients as a query to HaploReg using the default parameters in HaploReg v4.2 (LD threshold r2 >= 1, ChromHMM 15-state model, SiPhy-omega, and GENCODE genes). HaploReg's ability to browse haplotypes is useful here as there is no guarantee that our iPGS model selects causal variants. The 'top 100 variant' cutoff is an arbitrary threshold; we aim to demonstrate how one may investigate the selected variants. Please check Ward and Kellis. Nucleic Acids Res. 2012 and Ward and Kellis. Nucleic Acids Res. 2016 for more information on HaploReg.

GREAT

GREAT: Genomic Regions Enrichment of Annotations Tool evaluates enrichment of pathway and ontology terms. The ability of GREAT to map non-coding genetic variants to their downstream target genes would be suitable for investigating pathway and ontology enrichment of genetic variants selected in our sparse iPGS model. The button above submits the top 1000 genetic variants with the largest absolute value of coefficients as a query to GREAT using the default parameters in GREAT v4.0.4. The 'top 1000 variant' cutoff is an arbitrary threshold; we aim to demonstrate how one may investigate the selected variants. Please check McLean et al. Nat Biotechnol. 2010 and Tanigawa*, Dyer*, and Bejerano. PLoS Comput Biol. 2022 for more information on GREAT.


References