Power of Inclusion: enhancing polygenic prediction with admixed individuals

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


Phenotype: Arm fat mass (right)


Arm fat mass R 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/INI23120/INI23120.NBW.PGS_vs_phe.png
/static/data/tanigawakellis2023/per_trait/INI23120/INI23120.SA.PGS_vs_phe.png
/static/data/tanigawakellis2023/per_trait/INI23120/INI23120.Afr.PGS_vs_phe.png
/static/data/tanigawakellis2023/per_trait/INI23120/INI23120.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 modelR20.064[0.060, 0.068]<1.0x10-300
white BritishGenotype-only modelR20.086[0.082, 0.090]<1.0x10-300
white BritishFull model (covariates and genotypes)R20.149[0.144, 0.154]<1.0x10-300
Non-British whiteCovariate-only modelR20.041[0.027, 0.055]1.4x10-27
Non-British whiteGenotype-only modelR20.082[0.063, 0.101]1.4x10-54
Non-British whiteFull model (covariates and genotypes)R20.124[0.101, 0.146]1.7x10-83
South AsianCovariate-only modelR20.150[0.116, 0.183]4.0x10-53
South AsianGenotype-only modelR20.074[0.048, 0.099]4.9x10-26
South AsianFull model (covariates and genotypes)R20.198[0.162, 0.234]1.0x10-71
AfricanCovariate-only modelR20.190[0.151, 0.230]2.6x10-55
AfricanGenotype-only modelR20.008[-0.002, 0.019]1.6x10-03
AfricanFull model (covariates and genotypes)R20.134[0.098, 0.170]2.7x10-38
OthersCovariate-only modelR20.078[0.066, 0.089]1.6x10-139
OthersGenotype-only modelR20.080[0.068, 0.091]2.1x10-143
OthersFull model (covariates and genotypes)R20.146[0.131, 0.160]4.6x10-270

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/INI23120/INI23120.BETAs.png

We show the coefficients (BETA) of PGS models. Our iPGS model selected 29790 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
165380095416:53800954:T:Crs1421085CIntronicFTO0.038
24171672:417167:T:Crs62106258COthersAC105393.2-0.032
185803927618:58039276:C:Trs2229616TPAVsMC4R-0.028
11778894801:177889480:A:Grs543874GOthersSEC16B0.021
194618139219:46181392:G:Crs1800437CPAVsGIPR-0.019
185785258718:57852587:T:Crs476828COthers0.018
112767991611:27679916:C:Trs6265TPAVsBDNF-0.015
4451798834:45179883:C:Trs12641981TOthers0.013
3123931253:12393125:C:Grs1801282GPAVsPPARG0.012
1111707697211:117076972:C:Ars45574931APAVsPCSK7-0.012
161994436316:19944363:A:Grs11639988GOthers-0.012
194756900319:47569003:G:Ars3810291AUTRZC3H40.012
2251415382:25141538:A:Grs11676272GPAVsADCY30.011
26357212:635721:T:Crs6755502COthers0.011
4254088384:25408838:G:Ars34811474APAVsANAPC4-0.011
3499249403:49924940:T:Crs1062633CPAVsMST1R0.011
125024746812:50247468:G:Ars7138803AOthers0.010
31413266023:141326602:T:Crs295322CPAVsRASA20.010
6403719186:40371918:C:Trs1579557TIntronicLRFN20.010
31858223533:185822353:T:Grs10513801GIntronicETV5-0.010
1212308744212:123087442:A:Grs7307735GPAVsKNTC10.010
176583874317:65838743:T:Grs8074078GIntronicBPTF0.010
165375688516:53756885:A:Grs76488452GIntronicFTO0.010
6315958826:31595882:C:Ars1046080APAVsPRRC2A0.009
142968532814:29685328:G:Ars974471AOthers0.009
159257128315:92571283:G:Trs12101386TIntronicSLCO3A1-0.009
16401346716:4013467:C:Trs2531995TUTRADCY90.009
8734390708:73439070:A:Grs1431659GOthers-0.009
6131892756:13189275:T:Crs9367368CIntronicPHACTR1-0.009
1212441349112:124413491:G:Ars9971695AUTRDNAH10OS0.009
162037081016:20370810:C:Trs9652588TPAVsPDILT-0.009
1786236261:78623626:C:Trs17391694TOthers0.009
3517550653:51755065:T:Crs4687770COthersGRM2-0.009
6348270856:34827085:A:Trs9469913TPAVsUHRF1BP10.009
191882030819:18820308:C:Ars757318AIntronicCRTC1-0.009
5750036785:75003678:T:Crs2307111CPAVsPOC5-0.009
1211716097612:117160976:T:Crs10507274CPAVsC12orf49-0.009
182112044418:21120444:T:Crs1805082CPAVsNPC1-0.009
41031887094:103188709:C:Trs13107325TPAVsSLC39A80.009
6508658206:50865820:C:Trs943005TOthersRP4-753D5.30.008
102183010410:21830104:A:Grs11012732GIntronicMLLT100.008
115463041:1546304:C:Trs11492279TOthersMIB2-0.008
26228272:622827:T:Crs2867125COthers0.008
41027093084:102709308:T:Crs11097755CIntronicBANK10.008
51458953945:145895394:G:Ars114285050APTVsGPR151-0.008
81382152288:138215228:G:Ars16906845AOthers-0.008
8772282228:77228222:A:Grs1405348GOthers0.008
184260609118:42606091:C:Trs9967367TIntronicSETBP1-0.008
12018692571:201869257:G:Ars2820312APAVsLMOD10.008
174712342317:47123423:T:Crs4643373CIntronicIGF2BP1-0.008
41002393194:100239319:T:Crs1229984CPAVsADH1B0.008
191978952819:19789528:A:Grs2304130GPAVsZNF101-0.008
X117904229X:117904229:T:Crs2248846CPTVsIL13RA10.008
1983488851:98348885:G:Ars1801265APAVsDPYD-0.008
1410324647014:103246470:A:Grs3803286GIntronicTRAF3-0.008
22368176292:236817629:C:Trs4663215TIntronicAGAP1-0.008
5957288985:95728898:C:Grs6235GPAVsPCSK10.008
7766081437:76608143:C:Trs2245368TOthersDTX2P1-0.008
147994264714:79942647:G:Ars7156625AIntronicNRXN30.008
19406119019:4061190:C:Trs10414261TIntronicZBTB7A-0.008
134075977313:40759773:T:Crs10507483CIntronicLINC003320.008
174625234617:46252346:T:Crs208015CIntronicSKAP1-0.008
11102107801:110210780:C:Grs530021GPAVsGSTM2-0.008
107396424310:73964243:G:Crs11000217CPTVsASCC10.008
6508209406:50820940:T:Crs2635727COthersRPS17P50.008
21422935112:142293511:A:Grs13008033GIntronicLRP1B-0.008
11903031491:190303149:C:Trs648091TIntronicRP11-547I7.1, BRINP3-0.008
6348246366:34824636:A:Grs11755393GPAVsUHRF1BP10.007
109977240410:99772404:G:Ars563296AIntronicCRTAC10.007
157513009315:75130093:T:Crs12898397CPAVsULK30.007
163108862516:31088625:A:Grs749670GPAVsZNF646-0.007
106183198410:61831984:G:Trs11599164TPAVsANK30.007
185783976918:57839769:C:Ars571312AOthers0.007
201581949520:15819495:A:Grs8123881GIntronicMACROD20.007
1321654951:32165495:G:Trs2228552TPAVsCOL16A10.007
205415375920:54153759:A:Grs2244665GOthers0.007
17182430517:1824305:C:Ars4790292AOthers-0.007
7774235607:77423560:ATCCAGACTGGAATG:Ars866484129APTVsTMEM600.007
213263855021:32638550:C:Trs2070418TPAVsTIAM1-0.007
191941309219:19413092:C:Trs17751061TPAVsSUGP1-0.007
12019345781:201934578:G:Ars4648APAVsTIMM17A0.007
16282152516:2821525:G:Ars4036APAVsTCEB20.007
165429950316:54299503:C:Trs10521300TIntronicRP11-324D17.10.007
31839761033:183976103:C:Trs11546878TPAVsECE2-0.007
21629040132:162904013:T:Crs116302758CPTVsDPP4-0.007
162479374116:24793741:C:Trs2343606TIntronicTNRC6A-0.007
177861172417:78611724:A:Grs12939549GIntronicRPTOR-0.007
6437570826:43757082:T:Ars4711750AOthersVEGFA-0.007
6484151526:48415152:G:Ars9395354AOthers0.007
107685456410:76854564:C:Trs3088142TPAVsDUSP130.007
167126456116:71264561:A:Grs3743953GPTVsHYDIN-0.007
142593098814:25930988:C:Ars8015400AOthers0.006
61533816226:153381622:A:Crs2185027CIntronicRGS170.006
135863152513:58631525:A:Grs9569808GOthers-0.006
11949611721:194961172:A:Grs7349184GOthers-0.006
135348459113:53484591:G:Ars4884286AOthers-0.006
728018037:2801803:C:Trs798489TPTVsGNA12-0.006
156808281615:68082816:G:Ars2241420AIntronicMAP2K5-0.006
3124269363:12426936:G:Trs10510419TIntronicPPARG-0.006
71135037037:113503703:G:Ars10247621AOthers-0.006

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 29790 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.

Single-cell RNA-seq

For anthropometric traits, it may be relevant to investigate the single-cell expression profiling data in adipose-muscle tissues. Please check Single Cell Metab Browser from Yang*, Vamvini*, Nigro* et al. Cell Metab. 2022 as an example of such resources.


References