Power of Inclusion: enhancing polygenic prediction with admixed individuals

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


Phenotype: Platelet distribution width


Platelet 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.

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/static/data/tanigawakellis2023/per_trait/INI30110/INI30110.NBW.PGS_vs_phe.png
/static/data/tanigawakellis2023/per_trait/INI30110/INI30110.SA.PGS_vs_phe.png
/static/data/tanigawakellis2023/per_trait/INI30110/INI30110.Afr.PGS_vs_phe.png
/static/data/tanigawakellis2023/per_trait/INI30110/INI30110.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 modelR20.022[0.019, 0.024]1.9x10-308
white BritishGenotype-only modelR20.209[0.203, 0.214]<1.0x10-300
white BritishFull model (covariates and genotypes)R20.231[0.226, 0.237]<1.0x10-300
Non-British whiteCovariate-only modelR20.010[0.003, 0.017]1.3x10-07
Non-British whiteGenotype-only modelR20.199[0.173, 0.225]1.8x10-137
Non-British whiteFull model (covariates and genotypes)R20.208[0.182, 0.234]1.8x10-144
South AsianCovariate-only modelR20.013[0.002, 0.025]1.2x10-05
South AsianGenotype-only modelR20.166[0.132, 0.201]1.8x10-58
South AsianFull model (covariates and genotypes)R20.184[0.148, 0.219]4.9x10-65
AfricanCovariate-only modelR20.032[0.013, 0.052]8.2x10-10
AfricanGenotype-only modelR20.066[0.039, 0.093]7.8x10-19
AfricanFull model (covariates and genotypes)R20.090[0.060, 0.121]1.7x10-25
OthersCovariate-only modelR20.045[0.037, 0.054]3.4x10-80
OthersGenotype-only modelR20.198[0.182, 0.214]<1.0x10-300
OthersFull model (covariates and genotypes)R20.224[0.208, 0.240]<1.0x10-300

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/INI30110/INI30110.BETAs.png

We show the coefficients (BETA) of PGS models. Our iPGS model selected 21899 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
205759880820:57598808:G:Ars41303899APAVsTUBB10.667
205759797020:57597970:A:Crs463312CPAVsTUBB10.279
11719497501:171949750:T:Crs10914144CIntronicDNM30.104
191618555919:16185559:G:Ars8109288AIntronicTPM40.079
12477197691:247719769:G:Ars56043070APTVsGCSAML0.062
2314648292:31464829:A:Grs647316GIntronicEHD3-0.054
205759930320:57599303:C:Trs35565630TPAVsTUBB1-0.051
173398399117:33983991:A:Grs10491116GIntronicAP2B1-0.043
1212236558312:122365583:C:Trs7961894TIntronicWDR660.040
71063729037:106372903:G:Ars342296AIntronicCTB-111H14.10.039
11718920851:171892085:G:Ars2093184AIntronicDNM3-0.039
173387528417:33875284:G:Ars9907259APAVsSLFN14-0.035
173388567217:33885672:C:Trs4516277TOthersSLFN14-0.035
12480392941:248039294:G:Ars1339847APAVsTRIM580.033
890301608:9030160:G:Ars3748136AOthersRP11-10A14.40.033
195553659519:55536595:G:Ars1613662APAVsGP60.033
173388030517:33880305:T:Crs79007502CPAVsSLFN14-0.032
124964524012:49645240:T:Crs11168936CIntronicTUBA1C, RP11-977B10.20.032
2686155462:68615546:A:Crs34338164CPAVsPLEK0.030
1212224711412:122247114:G:Ars77408535AIntronicSETD1B0.028
468914554:6891455:T:Grs11731274GOthers-0.028
19680327019:6803270:C:Ars8109794AIntronicVAV10.027
124959256212:49592562:A:Grs7139233GIntronicTUBA1C0.027
81065815288:106581528:A:Trs6993770TIntronicZFPM20.027
11151305081:115130508:A:Grs61753528GPAVsDENND2C-0.026
91133122319:113312231:G:Crs61751937CPAVsSVEP10.025
8198135298:19813529:A:Grs268GPAVsLPL0.025
22257375462:225737546:T:Crs7607230CIntronicDOCK100.024
223832859722:38328597:A:Grs34834842GPAVsMICALL10.024
125471230812:54712308:G:Ars10876550AIntronicCOPZ1, RP11-968A15.8-0.023
168541583816:85415838:T:Crs4783187COthers0.023
156517432015:65174320:G:Ars1719260AIntronicAC069368.3-0.023
11151428701:115142870:G:Ars149711393APAVsDENND2C-0.022
22201174582:220117458:G:Ars1024131AIntronicTUBA4A0.022
156334199615:63341996:T:Crs11071720CIntronicTPM1-0.022
31244665203:124466520:C:Trs10934685TOthersRP11-71H17.90.022
61107014516:110701451:A:Grs7748253GOthers-0.022
205760199520:57601995:A:Grs151352GUTRATP5E0.021
17487562817:4875628:A:Grs16942615GPAVsCAMTA2-0.020
1311401895313:114018953:T:Crs11164132COthersGRTP1-0.020
11611822081:161182208:C:Grs11576415GPAVsNDUFS2-0.020
9990910099:99091009:C:Trs10990535TIntronicSLC35D2-0.020
191843371419:18433714:G:Ars76378167AIntronicLSM40.020
194571568019:45715680:T:Crs11083767CIntronicMARK4, AC006126.30.019
134724391013:47243910:A:Grs9526216GIntronicLRCH10.019
125702100612:57021006:T:Crs2950387CIntronicBAZ2A-0.018
22415109032:241510903:G:Ars78909033AIntronicRNPEPL1-0.018
186753164218:67531642:T:Crs763361CPAVsCD2260.018
182072097318:20720973:G:Trs11082304TIntronicCABLES10.018
1410356678514:103566785:C:Trs2297067TPAVsEXOC3L4-0.018
1250441111:25044111:C:Trs4601530TOthers0.018
41031887094:103188709:C:Trs13107325TPAVsSLC39A8-0.018
11181556201:118155620:G:Ars3767812AIntronicFAM46C-0.017
125705529112:57055291:C:Trs2950390TOthersPTGES3-0.017
8217694328:21769432:A:Grs56094005GPAVsDOK20.017
81449960298:144996029:A:Grs7833924GPAVsPLEC-0.017
X67919944X:67919944:G:Ars56008802AIntronicSTARD80.017
12323111612:3231116:G:Ars588513AIntronicTSPAN9-0.017
5759969095:75996909:G:Ars34592828APAVsIQGAP20.017
31228398763:122839876:G:Ars3792366AIntronicPDIA50.016
1111664891711:116648917:G:Crs964184CUTRZNF259-0.016
16905247316:9052473:C:Ars4076904AIntronicUSP70.016
3470458463:47045846:C:Trs2305637TPAVsNBEAL20.016
173388171817:33881718:G:Ars321613APAVsSLFN140.016
124967085412:49670854:G:Ars10875942AOthersRP11-161H23.5-0.016
173405215717:34052157:G:Ars2240903AUTRAP2B10.016
61580926386:158092638:T:Crs688181CIntronicZDHHC140.015
31243907223:124390722:G:Ars35653635APAVsKALRN-0.015
22239744312:223974431:A:Grs978918GIntronicKCNE4-0.015
224337570122:43375701:G:Ars5759070AIntronicPACSIN20.015
20860459320:8604593:G:Ars6077396AIntronicPLCB10.015
41433267144:143326714:T:Grs7696969GIntronicINPP4B-0.015
X39679127X:39679127:G:Ars5963693AOthers-0.015
11568690471:156869047:G:Trs12566888TIntronicPEAR1-0.014
1210188012812:101880128:A:Grs61748064GPAVsSPIC0.014
12072698581:207269858:T:Crs3813948CPAVsC4BPB0.014
21438757252:143875725:T:Crs16858573CIntronicARHGAP150.014
193566050819:35660508:G:Ars12110APAVsFXYD50.014
117294534111:72945341:C:Trs2511241TPAVsP2RY2-0.014
174244234417:42442344:T:Crs708382COthersFAM171A20.014
91006847579:100684757:A:Grs3183927GPAVsC9orf156-0.014
11976956211:9769562:C:Grs415895GPAVsSWAP700.013
168541164116:85411641:G:Ars8056420AOthers-0.013
124962649012:49626490:T:Crs10735819CIntronicTUBA1C-0.013
511049385:1104938:C:Trs35188965TIntronicSLC12A7-0.013
92731609:273160:T:Crs540909CIntronicDOCK80.013
71000561667:100056166:C:Trs6955362TIntronicC7orf61-0.013
171018303117:10183031:A:Grs34793094GOthers0.013
1311402816113:114028161:C:Trs78016312TOthers-0.013
1653326416:533264:G:Ars6600221AIntronicRAB11FIP3-0.013
173394610717:33946107:A:Grs225245GIntronicAP2B10.012
1352574161:35257416:A:Grs4653091GIntronicSMIM120.012
71356325217:135632521:T:Crs999251CIntronicMTPN-0.012
152296923215:22969232:G:Ars7170637APAVsCYFIP10.012
195814471519:58144715:A:Grs9749449GPTVsZNF2110.012
61530190996:153019099:GAGAT:Grs3841162GPTVsMYCT10.012
159924813215:99248132:G:Trs4966015TIntronicIGF1R0.012
51338486605:133848660:C:Trs4559047TOthersRN7SL541P-0.012
122943567512:29435675:A:Grs1006409GIntronicRP11-996F15.2, FAR20.012
6316755016:31675501:G:Ars9267547APAVsMEGT1, LY6G6F0.012

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 21899 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