Global Biobank analyses provide lessons for developing polygenic risk scores across diverse cohorts

Wang, Ying; Lopera, Esteban; Kerminen, Sini; Bhatta, Laxmi; Deelen, Patrick; Murakami, Yoshinori; Ar, Martin; Zhou, Wei; Kanai, Masahiro; Wu, Kuan-Han Hank; Rasheed, Humaira; Tsuo, Kristin; Hirbo, Jibril; Wang, Ying; Bhattacharya, Arjun; Zhao, Huiling; Namba, Shinichi; Surakka, Ida; Wolford, Brooke N.; Faro, Valeria Lo; Lopera-Maya, Esteban A.; Läll, Kristi; Favé, Marie-Julie; Chapman, Sinéad B.; Karjalainen, Juha; Kurki, Mitja; Maasha, Mutaamba; Partanen, Juulia; Brumpton, Ben; Chavan, Sameer; Chen, Tzu-Ting; Daya, Michelle; Ding, Yi; Feng, Yen‐Chen Anne; Gignoux, Christopher R.; Graham, Sarah E.; Hornsby, Whitney E.; Ingold, Nathan; Johnson, Ruth; Laisk, Triin; Lin, Kuang; Lv, Jun; Millwood, Iona Y.; Palta, Priit; Pandit, Anita; Preuss, Michael; Þorsteinsdóttir, Unnur; Uzunović, Jasmina; Zawistowski, Matthew; Zhong, Xue; Campbell, Archie; Crooks, Kristy; Bock, Geertruida H. de; Douville, Nicholas J.; Finer, Sarah; Fritsche, Lars G.; Griffiths, Christopher J.; Guo, Yu; Hunt, Karen A.; Konuma, Takahiro; Marioni, Riccardo E.; Nomdo, Jansonius; Patil, Snehal; Rafaels, Nicholas; Richmond, Anne; Shortt, Jonathan A.; Straub, Péter; Tao, Ran; Vanderwerff, Brett; Barnes, Kathleen C.; Boezen, Marike; Chen, Zhengming; Chen, Chia‐Yen; Cho, Judy H.; Smith, George Davey; Finucane, Hilary; Franke, Lude; Gamazon, Eric R.; Ganna, Andrea; Gaunt, Tom R.; Ge, Tian; Huang, Hailiang; Huffman, Jennifer E.; Koskela, Jukka; Lajonchere, Clara; Law, Matthew; Li, Liming; Lindgren, Cecilia M.; Loos, Ruth J. F.; MacGregor, Stuart; Matsuda, Koichi; Olsen, Catherine M.; Porteous, David J.; Shavit, Jordan A.; Snieder, Harold; Trembath, Richard; Vonk, Judith M.; Whiteman, David C.; Wicks, Stephen J.; Wijmenga, Cisca; Wright, John; Zheng, Jie; Zhou, Xiang; Awadalla, Philip; Boehnke, Michael; Cox, Nancy J.; Geschwind, Daniel H.; Hayward, Caroline; Hveem, Kristian; Kenny, Eimear E.; Lin, Yen-Feng; Mägi, Reedik; Martin, Hilary C.; Medland, Sarah E.; Okada, Yukinori; Palotie, Aarno; Paşaniuc, Bogdan; Sanna, Serena; Smoller, Jordan W.; Stefánsson, Kári; Heel, David A. van; Walters, Robin; Zöllner, Sebastian; Martin, Alicia R.; Willer, Cristen J.; Daly, Mark J.; Neale, Benjamin M.

doi:10.1016/j.xgen.2022.100241

Cited by 27 publications

(27 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous studies observed that PRS model derived from HapMap3 SNPs have comparable performance to models that uses SNP from 1000 Genomes Project 89 . As such, we used SNPs in HapMap3 to construct PRS.…”

Section: Quality Control Of Gwas Summary Statisticsmentioning

confidence: 97%

“…For our PRS model derivation, we applied nine PRS methods that do not require validation datasets for parameter tuning, including P+T, SBayesR 69 , SBLUP 70 , SDPR 71 , LDpred-Inf 72 , LDpred-funct 73 , PUMAS 74 , PRS-CS-auto 75 , and LASSOSUM 76 , to three GWAS summary statistics. We used 503 European samples from 1000 Genome Project Phase 3 100 as a reference panel for estimating linkage disequilibrium coefficients, per recommendations by previous work 89 .…”

Section: Construction Of Prs Modelsmentioning

confidence: 99%

“…For the P+T method, we followed the recommendation from the Global Biobank Meta-Analysis Initiative 89 . Specifically, we ran PLINK 101 with the following flags: -clump-p1 1 -clump-p2 1 -clump-r2 0.1 -clumpkb 250.…”

Section: Construction Of Prs Modelsmentioning

confidence: 99%

See 2 more Smart Citations

Multi-ancestry and multi-trait genome-wide association meta-analyses inform clinical risk prediction for systemic lupus erythematosus

Khunsriraksakul

Markus

et al. 2023

Nat Commun

View full text Add to dashboard Cite

Systemic lupus erythematosus is a heritable autoimmune disease that predominantly affects young women. To improve our understanding of genetic etiology, we conduct multi-ancestry and multi-trait meta-analysis of genome-wide association studies, encompassing 12 systemic lupus erythematosus cohorts from 3 different ancestries and 10 genetically correlated autoimmune diseases, and identify 16 novel loci. We also perform transcriptome-wide association studies, computational drug repurposing analysis, and cell type enrichment analysis. We discover putative drug classes, including a histone deacetylase inhibitor that could be repurposed to treat lupus. We also identify multiple cell types enriched with putative target genes, such as non-classical monocytes and B cells, which may be targeted for future therapeutics. Using this newly assembled result, we further construct polygenic risk score models and demonstrate that integrating polygenic risk score with clinical lab biomarkers improves the diagnostic accuracy of systemic lupus erythematosus using the Vanderbilt BioVU and Michigan Genomics Initiative biobanks.

show abstract

Section: Quality Control Of Gwas Summary Statisticsmentioning

confidence: 97%

Section: Construction Of Prs Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Multi-ancestry and multi-trait genome-wide association meta-analyses inform clinical risk prediction for systemic lupus erythematosus

Khunsriraksakul

Markus

et al. 2023

Nat Commun

View full text Add to dashboard Cite

show abstract

“…A major issue of current interest is the transferability of the scores between different scenarios. In particular, the scores may not transfer easily between human populations [1,24,30], mainly due to differences in allele frequencies, LD-structure, and effect size. Moreover, scores may show reduced accuracy even within a single population where most above differences are negligible [21], including in prediction of within-family variation [27], with changes in covariates such as socioeconomic status, age and sex leading to decreased accuracy, possibly due to Gene-by-Environment interactions.…”

Section: Introductionmentioning

confidence: 99%

Selecting Covariates for Genome-Wide Association Studies

Dor

Margaliot

Brandes

et al. 2023

Preprint

View full text Add to dashboard Cite

The choice of which covariates to include in a Genome-Wide Association Study (GWAS) is important since it affects the ability to detect true association signal of variants, to correct for confounders and avoid false positives, and the running time of the analysis. Commonly used covariates include age, sex, genotyping batches, genotyping array type, as well as an arbitrary number of Principal Components (PCs) used to adjust for population structure. Despite the importance of this issue, there is no consensus or clear guidelines for the right choice of covariates. Therefore, studies typically employ heuristics for their choice with no clear justification. Here, we explore the dependence of the GWAS analysis results on the choice of covariates for a wide range of quantitative and binary human phenotypes. We propose guidelines for covariates choice based on the phenotype's type (quantitative vs. disease), the heritability, and the disease prevalence, with the goal of maximizing the statistical power to detect true associations and fit accurate polygenic scores while avoiding spurious associations and minimizing computation time. We analyze 36 traits in the UK-Biobank dataset. We show that the genotype batch and assessment center can be safely removed as covariates, thus significantly reducing the GWAS computational burden for these traits.

show abstract

“…In practice, this type of data can be nearly impossible to obtain 34 . In order to have broad applications, PRS models need to use the increasingly accessible GWAS summary statistics from global populations [35][36][37] as input.…”

mentioning

confidence: 99%

Quantifying portable genetic effects and improving cross-ancestry genetic prediction with GWAS summary statistics

et al. 2023

View full text Add to dashboard Cite

Polygenic risk scores (PRS) calculated from genome-wide association studies (GWAS) of Europeans are known to have substantially reduced predictive accuracy in non-European populations, limiting their clinical utility and raising concerns about health disparities across ancestral populations. Here, we introduce a statistical framework named X-Wing to improve predictive performance in ancestrally diverse populations. X-Wing quantifies local genetic correlations for complex traits between populations, employs an annotation-dependent estimation procedure to amplify correlated genetic effects between populations, and combines multiple population-specific PRS into a unified score with GWAS summary statistics alone as input. Through extensive benchmarking, we demonstrate that X-Wing pinpoints portable genetic effects and substantially improves PRS performance in non-European populations, showing 14.1%–119.1% relative gain in predictive R2 compared to state-of-the-art methods based on GWAS summary statistics. Overall, X-Wing addresses critical limitations in existing approaches and may have broad applications in cross-population polygenic risk prediction.

show abstract

Global Biobank analyses provide lessons for developing polygenic risk scores across diverse cohorts

Cited by 27 publications

References 51 publications

Multi-ancestry and multi-trait genome-wide association meta-analyses inform clinical risk prediction for systemic lupus erythematosus

Multi-ancestry and multi-trait genome-wide association meta-analyses inform clinical risk prediction for systemic lupus erythematosus

Selecting Covariates for Genome-Wide Association Studies

Quantifying portable genetic effects and improving cross-ancestry genetic prediction with GWAS summary statistics

Contact Info

Product

Resources

About