Power of Inclusion: enhancing polygenic prediction with admixed individuals

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

Phenotype: Platelet crit

Link to the source data field in UK Biobank
Estimated h² in white British population in UKB: 0.187 (95% CI:[0.170, 0.205]).
Link to PGS catalog (Study ID: PGP000502).
GWAS Catalog for mapped trait(s): platelet crit (EFO_0007985)

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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Predictive performance

Population	Model	Metric	Predictive Performance	95% CI	P-value
Population	Model	Metric	Predictive Performance	95% CI	P-value

white British	Covariate-only model	R²	0.089	[0.085, 0.093]	<1.0x10^-300
white British	Genotype-only model	R²	0.163	[0.158, 0.168]	<1.0x10^-300
white British	Full model (covariates and genotypes)	R²	0.252	[0.246, 0.257]	<1.0x10^-300
Non-British white	Covariate-only model	R²	0.075	[0.057, 0.094]	1.1x10^-49
Non-British white	Genotype-only model	R²	0.177	[0.152, 0.202]	7.1x10^-121
Non-British white	Full model (covariates and genotypes)	R²	0.248	[0.221, 0.276]	1.9x10^-176
South Asian	Covariate-only model	R²	0.139	[0.106, 0.171]	2.1x10^-48
South Asian	Genotype-only model	R²	0.120	[0.089, 0.151]	1.2x10^-41
South Asian	Full model (covariates and genotypes)	R²	0.248	[0.210, 0.286]	1.8x10^-90
African	Covariate-only model	R²	0.123	[0.089, 0.158]	9.0x10^-35
African	Genotype-only model	R²	0.044	[0.021, 0.066]	6.3x10^-13
African	Full model (covariates and genotypes)	R²	0.152	[0.115, 0.189]	2.8x10^-43
Others	Covariate-only model	R²	0.095	[0.083, 0.107]	1.4x10^-169
Others	Genotype-only model	R²	0.138	[0.124, 0.152]	1.7x10^-252
Others	Full model (covariates and genotypes)	R²	0.229	[0.213, 0.245]	<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/INI30090/INI30090.BETAs.png

We show the coefficients (BETA) of PGS models. Our iPGS model selected 27034 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

12	111885310	12:111885310:G:A	rs72650673	A	PAVs	SH2B3	0.022
17	16852187	17:16852187:A:G	rs34557412	G	PAVs	TNFRSF13B	-0.007
20	57597970	20:57597970:A:C	rs463312	C	PAVs	TUBB1	-0.007
12	111884608	12:111884608:T:C	rs3184504	C	PAVs	SH2B3	-0.007
19	19765499	19:19765499:C:T	rs45522544	T	PAVs	ATP13A1	0.004
9	5126343	9:5126343:G:A	rs41316003	A	PAVs	JAK2	0.004
17	64210580	17:64210580:A:C	rs1801689	C	PAVs	APOH	0.004
22	50656109	22:50656109:A:C	rs55955211	C	Others	SELO, TUBGCP6	0.004
9	4763176	9:4763176:T:C	rs385893	C	Others		0.004
15	43820717	15:43820717:C:T	rs55707100	T	PAVs	MAP1A	0.004
1	247719769	1:247719769:G:A	rs56043070	A	PTVs	GCSAML	-0.003
6	33540209	6:33540209:A:G	rs210134	G	Others	BAK1	0.003
18	60920854	18:60920854:C:T	rs17758695	T	Intronic	BCL2	-0.003
17	55465771	17:55465771:C:T	rs17834140	T	Intronic	MSI2	-0.003
19	16185559	19:16185559:G:A	rs8109288	A	Intronic	TPM4	-0.003
12	122365583	12:122365583:C:T	rs7961894	T	Intronic	WDR66	0.003
9	4766022	9:4766022:C:T	rs10815074	T	Others	RP11-307I14.4	-0.003
20	30294682	20:30294682:T:C	rs80054178	C	Intronic	BCL2L1, RP11-243J16.7	0.003
6	135418635	6:135418635:C:T	rs7775698	T	Intronic	HBS1L	0.003
3	184090266	3:184090266:C:T	rs6141	T	UTR	THPO	0.003
9	4767341	9:4767341:T:C	rs420470	C	Others	RP11-307I14.4	0.003
12	111882485	12:111882485:C:A	rs2238154	A	Intronic	SH2B3	0.002
12	122247114	12:122247114:G:A	rs77408535	A	Intronic	SETD1B	0.002
14	101170436	14:101170436:G:A	rs12888043	A	Others		-0.002
12	54649978	12:54649978:C:T	rs79880068	T	Intronic	CBX5	0.002
2	160690656	2:160690656:G:A	rs78446341	A	PAVs	LY75, LY75-CD302	0.002
8	106581528	8:106581528:A:T	rs6993770	T	Intronic	ZFPM2	-0.002
3	184091383	3:184091383:T:TGGAA	rs55827759	TGGAA	PAVs	THPO	-0.002
11	119075323	11:119075323:G:A	rs4938637	A	Others	CBL	0.002
9	21986847	9:21986847:T:A	rs3731211	A	Intronic	RP11-145E5.5, CDKN2A	0.002
3	184093040	3:184093040:G:A	rs34623301	A	Intronic	EIF2B5, THPO	0.002
9	4840380	9:4840380:A:G	rs10974808	G	Intronic	RCL1	0.002
13	110489152	13:110489152:C:T	rs11618989	T	Others		-0.002
18	42041063	18:42041063:A:C	rs12606438	C	Intronic	CTC-782O7.1	0.002
11	119088883	11:119088883:G:A	rs1893032	A	Intronic	CBL	0.002
12	109490296	12:109490296:G:T	rs12426673	T	Others	USP30-AS1	-0.002
1	25583610	1:25583610:C:G	rs72660908	G	Intronic	C1orf63	-0.002
2	160604514	2:160604514:C:T	rs76774368	T	PAVs	MARCH7	0.002
1	12042261	1:12042261:A:G	rs2236055	G	Intronic	MFN2	-0.002
9	4759930	9:4759930:C:T	rs12343429	T	Others		0.002
2	227291415	2:227291415:A:C	rs11686139	C	Others		0.002
1	43805240	1:43805240:A:G	rs16830693	G	PAVs	MPL	0.002
7	123408842	7:123408842:G:A	rs9886090	A	Others		0.002
10	96039597	10:96039597:G:C	rs2274224	C	PAVs	PLCE1	-0.002
5	1282319	5:1282319:C:A	rs7726159	A	Intronic	TERT	0.002
1	88104648	1:88104648:A:G	rs1826164	G	Others		-0.002
9	4856234	9:4856234:G:A	rs1887430	A	Intronic	RCL1	0.002
7	44870933	7:44870933:T:C	rs10267576	C	Intronic	H2AFV	-0.001
9	22142907	9:22142907:G:A	rs10811664	A	Others		-0.001
6	135419018	6:135419018:T:C	rs9399137	C	Intronic	HBS1L	0.001
6	33546498	6:33546498:C:T	rs5745582	T	Intronic	BAK1	0.001
2	27730940	2:27730940:T:C	rs1260326	C	PAVs	GCKR	-0.001
14	93516465	14:93516465:T:C	rs2180369	C	Intronic	ITPK1	0.001
5	1287194	5:1287194:G:A	rs2853677	A	Intronic	TERT	-0.001
16	28506428	16:28506428:C:T	rs151233	T	PCVs	APOBR	0.001
9	91569248	9:91569248:G:T	rs11137467	T	Others		-0.001
5	177610700	5:177610700:A:G	rs10074768	G	Others	GMCL1P1	-0.001
1	198974904	1:198974904:C:T	rs10919615	T	Others	RP11-16L9.3	0.001
2	68615546	2:68615546:A:C	rs34338164	C	PAVs	PLEK	-0.001
1	2994717	1:2994717:C:A	rs4648451	A	Intronic	PRDM16	-0.001
2	160729005	2:160729005:C:T	rs1397706	T	PAVs	LY75, LY75-CD302	0.001
18	20720973	18:20720973:G:T	rs11082304	T	Intronic	CABLES1	-0.001
17	33884804	17:33884804:T:C	rs10512472	C	PAVs	SLFN14	0.001
6	33548394	6:33548394:G:T	rs5745568	T	Others	GGNBP1, BAK1	0.001
5	111066697	5:111066697:T:C	rs11559	C	PAVs	NREP	-0.001
3	184087417	3:184087417:C:T	rs9849502	T	Intronic	EIF2B5	0.001
2	31464829	2:31464829:A:G	rs647316	G	Intronic	EHD3	0.001
2	112167931	2:112167931:T:C	rs62160676	C	Intronic	MIR4435-1HG	0.001
15	99248132	15:99248132:G:T	rs4966015	T	Intronic	IGF1R	-0.001
16	85415838	16:85415838:T:C	rs4783187	C	Others		-0.001
1	27239920	1:27239920:C:G	rs6659176	G	PAVs	NR0B2	0.001
2	160714958	2:160714958:C:T	rs114821641	T	PTVs	LY75, LY75-CD302	0.001
6	135381351	6:135381351:A:G	rs11759077	G	PTVs	CTA-212D2.2	-0.001
20	30392884	20:30392884:A:G	rs6089075	G	Others	TPX2, RP11-243J16.8	-0.001
10	81164146	10:81164146:C:T	rs116052829	T	Intronic	RP11-342M3.5, ZCCHC24	0.001
16	88573347	16:88573347:G:T	rs17700789	T	Intronic	ZFPM1	0.001
8	30280833	8:30280833:G:A	rs2979489	A	Intronic	RBPMS	0.001
13	95897758	13:95897758:T:C	rs4148442	C	Intronic	ABCC4	0.001
9	91401893	9:91401893:C:T	rs9410344	T	Others		-0.001
19	45422946	19:45422946:A:G	rs4420638	G	Others	APOC1	-0.001
13	95898207	13:95898207:A:G	rs4148441	G	Intronic	ABCC4	0.001
11	113953622	11:113953622:G:A	rs73000929	A	Intronic	ZBTB16	-0.001
2	160687061	2:160687061:C:T	rs2556097	T	Intronic	LY75, LY75-CD302	-0.001
6	135427144	6:135427144:C:A	rs9376092	A	Others	HBS1L	0.001
9	99091009	9:99091009:C:T	rs10990535	T	Intronic	SLC35D2	0.001
1	8918313	1:8918313:T:C	rs10864368	C	Others	ENO1	0.001
20	8604593	20:8604593:G:A	rs6077396	A	Intronic	PLCB1	-0.001
20	54989326	20:54989326:T:G	rs11905250	G	Intronic	CASS4	0.001
12	48215749	12:48215749:G:A	rs1859281	A	Intronic	HDAC7	0.001
7	130761235	7:130761235:G:A	rs10954300	A	Intronic	LINC-PINT	-0.001
13	95896321	13:95896321:T:C	rs6492772	C	Intronic	ABCC4	0.001
6	31322367	6:31322367:T:G	rs3819299	G	Others	HLA-B	0.001
20	1923623	20:1923623:T:G	rs4144201	G	Others	RP4-684O24.5	-0.001
11	119207928	11:119207928:C:T	rs6649	T	UTR	RNF26	0.001
1	226555302	1:226555302:A:G	rs1136410	G	PAVs	PARP1	-0.001
6	135036003	6:135036003:A:G	rs6902798	G	Others		0.001
3	12267648	3:12267648:A:G	rs7616006	G	Others		-0.001
11	47431703	11:47431703:A:G	rs61897432	G	PAVs	SLC39A13	0.001
19	51728477	19:51728477:C:T	rs12459419	T	PAVs	CD33	-0.001
8	144996029	8:144996029:A:G	rs7833924	G	PAVs	PLEC	0.001

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 27034 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

Y. Tanigawa and M. Kellis. Power of inclusion: Enhancing polygenic prediction with admixed individuals. Am J Hum Genet. (2023).
- We introduce our inclusive PGS approach in this publication.
- Supplementary Data Files at figshare (doi: 10.6084/m9.figshare.22905368)
- MIT News article: Making genetic prediction models more inclusive
- Author interview: Inside AJHG: A Chat with Yosuke Tanigawa
J. Qian, Y. Tanigawa, W. Du, M. Aguirre, C. Chang, R. Tibshirani, M. A. Rivas, T. Hastie. A fast and scalable framework for large-scale and ultrahigh-dimensional sparse regression with application to the UK Biobank. PLoS Genet. 16, e1009141 (2020).
- In iPGS, we fit penalized multivariate regression on individual-level data using the BASIL algorithm implemented in the R snpnet package. This publication describes the BASIL algorithm and the R snpnet package.