Hypometric genetics: Improved power in genetic discovery by incorporating quality control flags

Tanigawa and Kellis. Am J Hum Genet. (2024).


Phenotype: BLQ: Free Chol. in XL VLDL


BLQ: Free Chol. in XL VLDL iPGS coefficients

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


Predictive performance of iPGS models

We evaluated the predictive performance of the inclusive polygenic score models using the held-out test set individuals.

Population Model PGS trait type Metric Predictive Performance 95% CI P-value
Population Model PGS trait type Metric Predictive Performance 95% CI P-value
white BritishCovariate-only modelBLQ (binarized at BLQ threshold)AUROC0.654[0.646, 0.661]<1.0x10-300
white BritishGenotype-only modelTruncated (excl. BLQ measurements)AUROC0.640[0.633, 0.648]1.3x10-273
white BritishGenotype-only modelOriginal (incl. BLQ measurements)AUROC0.659[0.651, 0.666]<1.0x10-300
white BritishGenotype-only modelBLQ (binarized at BLQ threshold)AUROC0.639[0.632, 0.647]3.0x10-271
white BritishFull model (covariates and genotypes)Truncated (excl. BLQ measurements)AUROC0.655[0.648, 0.663]<1.0x10-300
white BritishFull model (covariates and genotypes)Original (incl. BLQ measurements)AUROC0.656[0.648, 0.663]<1.0x10-300
white BritishFull model (covariates and genotypes)BLQ (binarized at BLQ threshold)AUROC0.708[0.701, 0.715]<1.0x10-300
Non-British whiteCovariate-only modelBLQ (binarized at BLQ threshold)AUROC0.693[0.658, 0.727]1.4x10-25
Non-British whiteGenotype-only modelTruncated (excl. BLQ measurements)AUROC0.630[0.596, 0.664]1.0x10-12
Non-British whiteGenotype-only modelOriginal (incl. BLQ measurements)AUROC0.649[0.616, 0.682]1.5x10-15
Non-British whiteGenotype-only modelBLQ (binarized at BLQ threshold)AUROC0.638[0.604, 0.671]5.0x10-14
Non-British whiteFull model (covariates and genotypes)Truncated (excl. BLQ measurements)AUROC0.694[0.660, 0.728]7.3x10-26
Non-British whiteFull model (covariates and genotypes)Original (incl. BLQ measurements)AUROC0.694[0.660, 0.728]6.1x10-26
Non-British whiteFull model (covariates and genotypes)BLQ (binarized at BLQ threshold)AUROC0.739[0.707, 0.771]2.6x10-35
South AsianCovariate-only modelBLQ (binarized at BLQ threshold)AUROC0.636[0.576, 0.696]4.2x10-05
South AsianGenotype-only modelTruncated (excl. BLQ measurements)AUROC0.637[0.575, 0.699]3.6x10-05
South AsianGenotype-only modelOriginal (incl. BLQ measurements)AUROC0.641[0.578, 0.704]2.0x10-05
South AsianGenotype-only modelBLQ (binarized at BLQ threshold)AUROC0.623[0.560, 0.685]6.1x10-05
South AsianFull model (covariates and genotypes)Truncated (excl. BLQ measurements)AUROC0.637[0.577, 0.697]3.5x10-05
South AsianFull model (covariates and genotypes)Original (incl. BLQ measurements)AUROC0.637[0.577, 0.697]3.4x10-05
South AsianFull model (covariates and genotypes)BLQ (binarized at BLQ threshold)AUROC0.669[0.606, 0.733]3.8x10-08
AfricanCovariate-only modelBLQ (binarized at BLQ threshold)AUROC0.625[0.579, 0.671]3.6x10-07
AfricanGenotype-only modelTruncated (excl. BLQ measurements)AUROC0.583[0.536, 0.630]5.7x10-04
AfricanGenotype-only modelOriginal (incl. BLQ measurements)AUROC0.602[0.555, 0.648]1.3x10-05
AfricanGenotype-only modelBLQ (binarized at BLQ threshold)AUROC0.604[0.557, 0.650]7.8x10-06
AfricanFull model (covariates and genotypes)Truncated (excl. BLQ measurements)AUROC0.625[0.578, 0.671]3.9x10-07
AfricanFull model (covariates and genotypes)Original (incl. BLQ measurements)AUROC0.625[0.579, 0.671]3.7x10-07
AfricanFull model (covariates and genotypes)BLQ (binarized at BLQ threshold)AUROC0.658[0.613, 0.703]1.5x10-10
OthersCovariate-only modelBLQ (binarized at BLQ threshold)AUROC0.682[0.662, 0.702]7.2x10-61
OthersGenotype-only modelTruncated (excl. BLQ measurements)AUROC0.597[0.576, 0.618]1.2x10-18
OthersGenotype-only modelOriginal (incl. BLQ measurements)AUROC0.621[0.600, 0.643]8.1x10-28
OthersGenotype-only modelBLQ (binarized at BLQ threshold)AUROC0.610[0.589, 0.632]1.7x10-23
OthersFull model (covariates and genotypes)Truncated (excl. BLQ measurements)AUROC0.682[0.662, 0.702]6.5x10-61
OthersFull model (covariates and genotypes)Original (incl. BLQ measurements)AUROC0.682[0.662, 0.702]4.5x10-61
OthersFull model (covariates and genotypes)BLQ (binarized at BLQ threshold)AUROC0.706[0.686, 0.726]1.8x10-73

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/tanigawakellis2024/per_trait/BIN_FC10023793/pgscoeffs.png

We show the coefficients (BETA) of PGS models. Our iPGS model selected 4794 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 Effect Weight
CHROM POS Variant Variant ID Effect Allele Consequence Gene symbol Effect Weight
61609611376:160961137:T:Crs3798220CPAVsLPA0.304068505695605
8198135298:19813529:A:Grs268GPAVsLPL-0.293487883432282
61610101186:161010118:A:Grs10455872GIntronicLPA0.239009206160982
1111664891711:116648917:G:Crs964184CUTRZPR10.238110076747914
8198197248:19819724:C:Grs328GPTVsLPL0.231253896240237
19842932319:8429323:G:Ars116843064APAVsANGPTL40.190214078206726
165699332416:56993324:C:Ars3764261AOthersCETP0.130477369725592
2277309402:27730940:T:Crs1260326CPAVsGCKR0.124525359784278
1111669229311:116692293:C:Ars12721043APAVsAPOA40.121485834798394
1111704240811:117042408:C:Trs186808413TPAVsPAFAH1B20.119423052759835
2212315242:21231524:G:Ars676210APAVsAPOB0.106110138806
8198194398:19819439:A:Grs326GIntronicLPL0.097957322611695
1111666240711:116662407:G:Crs3135506CPAVsAPOA5-0.0928960911696813
194541445119:45414451:T:Crs439401COthersAPOC1-0.089484989205105
191937954919:19379549:C:Trs58542926TPAVsTM6SF20.0858636707564839
174192612617:41926126:C:Trs72836561TPAVsCD300LG-0.0797278079300586
8198057088:19805708:G:Ars1801177APAVsLPL-0.0704756785987128
7730203377:73020337:C:Grs3812316GPAVsMLXIPL0.0698224293993268
165701509116:57015091:G:Crs5880CPAVsCETP-0.0672768515780697
194542294619:45422946:A:Grs4420638GOthersAPOC1-0.0663629690046143
165700659016:57006590:C:Trs7499892TIntronicCETP-0.0566739029194808
1111666370711:116663707:G:Ars662799AOthersAPOA50.0545200405116691
154382071715:43820717:C:Trs55707100TPAVsMAP1A-0.0524701029092828
81264817478:126481747:A:Grs2980875GIntronic0.0504512036927032
81265073898:126507389:C:Ars2954038AIntronic0.0491133577380793
51563902975:156390297:T:Crs6882076COthersTIMD4-0.0482446434179915
21655286242:165528624:G:Trs1128249TIntronicCOBLL10.0480598090633543
1212442730612:124427306:T:Ars11057401APAVsCCDC920.0475004789501105
2212252812:21225281:C:Trs1042034TPAVsAPOB-0.0458630795147921
125784371112:57843711:G:Ars2229357APAVsINHBC0.0458547317652943
5558618945:55861894:G:Ars9687846AIntronic-0.0451264664455539
1111665756111:116657561:C:Trs3741298TIntronicZPR10.0417065883508509
167214417416:72144174:T:Crs9302635CIntronicDHX380.039648118738783
194539526619:45395266:G:Ars157580AIntronicTOMM40-0.0396360790636719
71304455747:130445574:G:Ars17789506AOthers0.0388411278534973
61607663216:160766321:C:Trs540713TOthersSLC22A3-0.0380679943305195
61609603596:160960359:T:Crs6919346CIntronicLPA0.036386477209353
2203742862:20374286:G:Ars6531216AOthersRN7SL140P, RNU6-961P-0.0357286542885074
116157976011:61579760:T:Crs174555CIntronicFADS1, FADS2-0.0356040175752875
1629075951:62907595:A:Grs656297GIntronicUSP10.0348719387542133
2212883212:21288321:A:Grs562338GOthers-0.0347852761197481
7730328357:73032835:T:Crs7785479CIntronicMLXIPL0.0344777690593213
19861558919:8615589:A:Grs4804311GPAVsMYO1F0.034331562856229
5746565395:74656539:T:Crs12916CUTRHMGCR-0.0334087192779685
1630270241:63027024:C:Trs4329540TIntronicDOCK7-0.0332114998670581
61610181746:161018174:C:Trs7770628TIntronicLPA-0.0325810992454584
191120230619:11202306:G:Trs6511720TIntronicLDLR0.0323490419242926
1555056471:55505647:G:Trs11591147TPAVsPCSK90.03192990052993
12302976591:230297659:C:Trs2281719TIntronicGALNT20.0319130590217749
81264779788:126477978:G:Crs2001945COthers0.0318100866638982
61611070186:161107018:G:Ars9457997AOthers0.0311314661944333
174193137517:41931375:A:Grs12453522GPAVsCD300LG-0.0302975830242003
5558608665:55860866:G:Trs3936510TIntronic-0.0300804646918814
1272399201:27239920:C:Grs6659176GPAVsNR0B2-0.0298665603590817
21655485692:165548569:G:Ars10490694AIntronicCOBLL10.0293689015866059
1630455061:63045506:A:Crs1748201CIntronicDOCK7-0.0276275176142746
22271163652:227116365:A:Grs2972143GOthers-0.0275652266166778
106397798010:63977980:C:Trs61850830TPAVsRTKN20.0273719655965445
4880522194:88052219:T:Crs342467CPAVsAFF1-0.0270311603528344
632424101HLA-DRB3*0301HLA-DRB3*0301+PAVsHLA-DRB3-0.0269512607939937
7446006957:44600695:G:Ars217386AOthersDDX560.0267798685808202
61274647546:127464754:C:Trs2503320TIntronicRSPO3-0.0264531417801787
434460914:3446091:G:Trs3748034TPAVsHGFAC-0.0264055774598517
8199414488:19941448:C:Trs6989064TIntronic0.0263379584851544
182112044418:21120444:T:Crs1805082CPAVsNPC10.0261913122903671
11098183061:109818306:G:Trs629301TUTRCELSR2-0.0258052727310357
165701700216:57017002:T:Grs9923854GIntronicCETP0.0256175406644463
109481905310:94819053:C:Trs8211TUTREXOC6-0.0253524634025668
204455401520:44554015:T:Crs6065906COthers-0.0250233800245668
12303018111:230301811:T:Grs11122450GIntronicGALNT20.0248354428694083
61605608456:160560845:A:Grs628031GPAVsSLC22A10.0245975745285529
161514864616:15148646:C:Ars11075253AIntronicNTAN1, PDXDC10.02452352513009
4879967454:87996745:G:Ars17605615AIntronicAFF1-0.0239260005312312
1212457561512:124575615:C:Trs825482TIntronicRFLNA-0.0238369643349503
1111726788411:117267884:A:Grs573455GPAVsCEP164-0.0236793512083855
3523596783:52359678:T:Crs6796333CIntronicDNAH10.0235260226839101
185773627018:57736270:G:Ars1942867AOthers-0.0234255380031142
1111695364511:116953645:G:Ars1871756AIntronicSIK30.022862297646689
8106431648:10643164:C:Trs9657541TIntronicPINX1, SOX7-0.0228028237973844
1111663994111:116639941:A:Grs1263149GIntronicBUD130.0225826332969259
194540883619:45408836:T:Grs405509GOthersAPOE0.0225595096498805
22196688132:219668813:A:Grs6436089GIntronicCYP27A10.0225004865939688
204453465120:44534651:G:Ars6065904AIntronicPLTP-0.0224649902344052
116159236211:61592362:A:Grs174566GIntronicFADS1, FADS2-0.0224131083466052
61611379906:161137990:G:Ars783147AIntronicPLG-0.0223151134416164
223854561922:38545619:A:Grs6001027GIntronicPLA2G60.0221952819948204
2212639002:21263900:G:Ars1367117APAVsAPOB-0.0220307387690116
51187692985:118769298:A:Crs154632COthers0.0219122898919492
81166119028:116611902:T:Crs2737206CIntronicTRPS10.021738374730342
5557701545:55770154:G:Ars284032AIntronic-0.0216143087768533
133101290413:31012904:C:Trs1928496TOthers-0.0214270336021213
22423956742:242395674:G:Ars4675812AIntronicFARP20.021425872166762
51568114435:156811443:G:Ars10063413APTVs-0.0214196839846049
194194423719:41944237:T:Crs2231940CPAVsDMAC20.0214010712682169
8324533588:32453358:G:Ars3924999APAVsNRG10.0213785778837416
16125244116:1252441:T:Crs4984636CPAVsCACNA1H-0.0212971259310976
51565317365:156531736:C:Ars1036199APAVsHAVCR2-0.0212450988115743
8182724388:18272438:C:Trs4921914TOthers0.0209709746843418
2649286032:64928603:T:Crs12471768CIntronicSERTAD2-0.0209275978781459
19720593719:7205937:A:Crs12609995CIntronicINSR0.0208846079739327

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 4794 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.


References