Power of Inclusion: enhancing polygenic prediction with admixed individuals

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


Phenotype: Body fat percentage


Body fat % 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/INI23099/INI23099.WB.PGS_vs_phe.png
/static/data/tanigawakellis2023/per_trait/INI23099/INI23099.NBW.PGS_vs_phe.png
/static/data/tanigawakellis2023/per_trait/INI23099/INI23099.SA.PGS_vs_phe.png
/static/data/tanigawakellis2023/per_trait/INI23099/INI23099.Afr.PGS_vs_phe.png
/static/data/tanigawakellis2023/per_trait/INI23099/INI23099.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 modelR20.460[0.454, 0.465]<1.0x10-300
white BritishGenotype-only modelR20.056[0.053, 0.059]<1.0x10-300
white BritishFull model (covariates and genotypes)R20.517[0.512, 0.523]<1.0x10-300
Non-British whiteCovariate-only modelR20.424[0.397, 0.451]<1.0x10-300
Non-British whiteGenotype-only modelR20.060[0.044, 0.077]3.0x10-40
Non-British whiteFull model (covariates and genotypes)R20.491[0.465, 0.517]<1.0x10-300
South AsianCovariate-only modelR20.572[0.540, 0.605]5.5x10-270
South AsianGenotype-only modelR20.046[0.025, 0.067]1.4x10-16
South AsianFull model (covariates and genotypes)R20.604[0.573, 0.635]5.8x10-294
AfricanCovariate-only modelR20.610[0.575, 0.644]9.3x10-240
AfricanGenotype-only modelR20.010[-0.001, 0.021]7.4x10-04
AfricanFull model (covariates and genotypes)R20.594[0.559, 0.630]3.4x10-230
OthersCovariate-only modelR20.431[0.414, 0.447]<1.0x10-300
OthersGenotype-only modelR20.059[0.049, 0.069]7.9x10-106
OthersFull model (covariates and genotypes)R20.476[0.460, 0.492]<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/INI23099/INI23099.BETAs.png

We show the coefficients (BETA) of PGS models. Our iPGS model selected 34374 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:Crs1421085CIntronicFTO0.270
3123931253:12393125:C:Grs1801282GPAVsPPARG0.237
24171672:417167:T:Crs62106258COthersAC105393.2-0.218
185803927618:58039276:C:Trs2229616TPAVsMC4R-0.206
194618139219:46181392:G:Crs1800437CPAVsGIPR-0.187
11778894801:177889480:A:Grs543874GOthersSEC16B0.184
51458953945:145895394:G:Ars114285050APTVsGPR151-0.166
109603959710:96039597:G:Crs2274224CPAVsPLCE1-0.149
147994516214:79945162:A:Grs10146997GIntronicNRXN30.141
1210861863012:108618630:C:Trs3764002TPAVsWSCD2-0.141
1212441349112:124413491:G:Ars9971695AUTRDNAH10OS0.129
5828151705:82815170:A:Grs61749613GPAVsVCAN-0.126
2251415382:25141538:A:Grs11676272GPAVsADCY30.126
4451798834:45179883:C:Trs12641981TOthers0.123
21655286242:165528624:G:Trs1128249TIntronicCOBLL10.113
158941524715:89415247:C:Grs3817428GPAVsACAN-0.113
6315958826:31595882:C:Ars1046080APAVsPRRC2A0.112
142968532814:29685328:G:Ars974471AOthers0.111
176583874317:65838743:T:Grs8074078GIntronicBPTF0.108
41031887094:103188709:C:Trs13107325TPAVsSLC39A80.104
5557963195:55796319:C:Trs40271TOthersAC022431.1-0.104
1111707697211:117076972:C:Ars45574931APAVsPCSK7-0.103
12333999612:3339996:G:Ars3782809AIntronicTSPAN9-0.102
174712342317:47123423:T:Crs4643373CIntronicIGF2BP1-0.102
1510069295315:100692953:G:Ars72755233APAVsADAMTS17-0.101
5877300275:87730027:A:Grs7444298GOthersCTC-498M16.4-0.100
11499064131:149906413:T:Crs11205303CPAVsMTMR110.100
X67652748X:67652748:C:Trs41303733TPAVsOPHN10.099
4254088384:25408838:G:Ars34811474APAVsANAPC4-0.098
154085698915:40856989:C:Trs3803354TPTVsC15orf57-0.098
11551727251:155172725:T:Crs35154152CPAVsTHBS3-0.096
167126456116:71264561:A:Grs3743953GPTVsHYDIN-0.095
5879595385:87959538:T:Grs13174131GIntronicLINC004610.095
194756900319:47569003:G:Ars3810291AUTRZC3H40.094
114752994711:47529947:C:Ars7124681AIntronicCELF10.093
16401346716:4013467:C:Trs2531995TUTRADCY90.092
149311112014:93111120:C:Trs11624512TOthersRIN3-0.092
1210427371812:104273718:C:Trs17180749TIntronicRP11-642P15.10.092
194542294619:45422946:A:Grs4420638GOthersAPOC1-0.092
1010243304610:102433046:C:Trs11190644TOthers0.091
159257128315:92571283:G:Trs12101386TIntronicSLCO3A1-0.091
5880215275:88021527:T:Grs34320GIntronicMEF2C-0.090
19406119019:4061190:C:Trs10414261TIntronicZBTB7A-0.089
125024746812:50247468:G:Ars7138803AOthers0.089
112767991611:27679916:C:Trs6265TPAVsBDNF-0.088
109977240410:99772404:G:Ars563296AIntronicCRTAC10.088
185783976918:57839769:C:Ars571312AOthers0.087
8734390708:73439070:A:Grs1431659GOthers-0.087
11102107801:110210780:C:Grs530021GPAVsGSTM2-0.087
8772282228:77228222:A:Grs1405348GOthers0.085
3124269363:12426936:G:Trs10510419TIntronicPPARG-0.085
174625234617:46252346:T:Crs208015CIntronicSKAP1-0.085
7143279667:14327966:A:Grs7785249GIntronicDGKB0.084
106183129010:61831290:T:Crs28932171CPAVsANK30.083
1212081550412:120815504:A:Grs16950101GOthers-0.083
2468954852:46895485:T:Crs17035489COthers-0.082
41002393194:100239319:T:Crs1229984CPAVsADH1B0.082
8308543798:30854379:T:Crs17648656CIntronicPURG-0.082
91341050489:134105048:A:Grs28665311GPTVsNUP214-0.081
204636563620:46365636:C:Trs56218501TPAVsSULF2-0.081
125442181012:54421810:T:Crs10876529CIntronicRP11-834C11.14, RP11-834C11.12, HOXC4, HOXC60.081
8177633658:17763365:G:Ars426372APTVsRP11-156K13.3-0.081
21008300402:100830040:T:Crs4303732CIntronicLINC01104-0.081
114365653511:43656535:G:Ars2862961AIntronicHSD17B12-0.080
4787768834:78776883:A:Grs6817305GOthers0.080
7774235607:77423560:ATCCAGACTGGAATG:Ars866484129APTVsTMEM600.079
6121248556:12124855:G:Ars2228213APAVsHIVEP1-0.078
184260609118:42606091:C:Trs9967367TIntronicSETBP1-0.078
114785725311:47857253:T:Crs3816605CPAVsNUP160-0.077
115463041:1546304:C:Trs11492279TOthersMIB2-0.076
7990817307:99081730:A:Grs6962772GPAVsZNF789-0.076
17182430517:1824305:C:Ars4790292AOthers-0.076
4735450364:73545036:T:Crs2366305COthers0.076
22420217422:242021742:G:Trs2108485TPAVsSNED10.076
1211716097612:117160976:T:Crs10507274CPAVsC12orf49-0.075
71505246817:150524681:T:Crs10952289CIntronicAOC1-0.075
2244390482:24439048:A:Grs3731625GPAVsITSN2-0.075
6348246366:34824636:A:Grs11755393GPAVsUHRF1BP10.075
6403637346:40363734:C:Trs930249TIntronicLRFN2-0.074
49830604:983060:T:Crs3796622CPAVsSLC26A1-0.074
1210554617212:105546172:G:Ars1663564APAVsKIAA1033-0.074
X8913826X:8913826:T:Crs5934505COthers-0.074
5750036785:75003678:T:Crs2307111CPAVsPOC5-0.074
224887569922:48875699:C:Trs9615905TOthers0.073
162888324116:28883241:A:Grs7498665GPAVsSH2B10.073
193030568419:30305684:G:Ars3218036AIntronicCCNE10.073
185502733318:55027333:CG:Crs112792874CPTVsST8SIA3-0.073
19353558219:3535582:T:Crs8109960CUTRFZR10.073
213263855021:32638550:C:Trs2070418TPAVsTIAM1-0.073
11551750891:155175089:C:Trs72704117TPAVsTHBS3-0.072
11126377611:1263776:C:Trs2943510TPAVsMUC5B-0.072
146236046414:62360464:A:Grs217671GIntronicCTD-2277K2.10.072
61533717686:153371768:A:Crs7747583CIntronicRGS170.072
17184451917:1844519:G:Ars4239060AIntronicRTN4RL1-0.071
168998614416:89986144:C:Trs1805008TPAVsMC1R, TUBB3-0.071
135408403213:54084032:G:Ars4883723AOthers0.071
191978952819:19789528:A:Grs2304130GPAVsZNF101-0.071
3517550653:51755065:T:Crs4687770COthersGRM2-0.071
91293705769:129370576:G:Ars2275241AOthersRP11-123K19.10.071
1410324647014:103246470:A:Grs3803286GIntronicTRAF3-0.071

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 34374 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