Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases

Elle M Weeks; Jacob C Ulirsch; Nathan Y Cheng; Brian L Trippe; Rebecca S Fine; Jenkai Miao; Tejal A Patwardhan; Masahiro Kanai; Joseph Nasser; Charles P Fulco; Katherine C Tashman; Francois Aguet; Taibo Li; Jose Ordovas-Montanes; Christopher S Smillie; Moshe Biton; Alex K Shalek; Ashwin N Ananthakrishnan; Ramnik J Xavier; Aviv Regev; Rajat M Gupta; Kasper Lage; Kristin G Ardlie; Joel N Hirschhorn; Eric S Lander; Jesse M Engreitz; Hilary K Finucane

doi:10.1038/s41588-023-01443-6

Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases

Nat Genet. 2023 Aug;55(8):1267-1276. doi: 10.1038/s41588-023-01443-6. Epub 2023 Jul 13.

Authors

Elle M Weeks^#¹, Jacob C Ulirsch^#^{2

3

4}, Nathan Y Cheng², Brian L Trippe^{5

6}, Rebecca S Fine^{2

7

8

9}, Jenkai Miao^{2

7}, Tejal A Patwardhan^{2

10}, Masahiro Kanai^{2

11

12

13}, Joseph Nasser², Charles P Fulco^{2

14}, Katherine C Tashman², Francois Aguet², Taibo Li^{2

15}, Jose Ordovas-Montanes^{2

16

17

18}, Christopher S Smillie^{2

5}, Moshe Biton^{2

19

20}, Alex K Shalek^{2

21

22

23

24}, Ashwin N Ananthakrishnan²⁵, Ramnik J Xavier^{2

19

25

26}, Aviv Regev^{2

23

27

28}, Rajat M Gupta^{2

29

30}, Kasper Lage^{2

31}, Kristin G Ardlie^{2

32}, Joel N Hirschhorn^{2

7

8

33}, Eric S Lander^{2

34

35}, Jesse M Engreitz^{2

36

37}, Hilary K Finucane^{38

39}

Affiliations

¹ Broad Institute of MIT and Harvard, Cambridge, MA, USA. eweeks@broadinstitute.org.
² Broad Institute of MIT and Harvard, Cambridge, MA, USA.
³ Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA.
⁴ Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA.
⁵ Program in Computational & Systems Biology, MIT, Cambridge, MA, USA.
⁶ Computer Science & Artificial Intelligence Lab, MIT, Cambridge, MA, USA.
⁷ Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, MA, USA.
⁸ Department of Genetics, Harvard Medical School, Boston, MA, USA.
⁹ Vertex Pharmaceuticals Incorporated, Boston, MA, USA.
¹⁰ Department of Statistics, Harvard University, Cambridge, MA, USA.
¹¹ Analytic and Translational Genetics Unit, MGH, Boston, MA, USA.
¹² Program in Bioinformatics and Integrative Genomics, Harvard Medical School, Boston, MA, USA.
¹³ Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan.
¹⁴ Bristol Myers Squibb, Cambridge, MA, USA.
¹⁵ MD-PhD Program, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
¹⁶ Division of Gastroenterology, Hepatology, and Nutrition, Boston Children's Hospital, Boston, MA, USA.
¹⁷ Program in Immunology, Harvard Medical School, Boston, MA, USA.
¹⁸ Harvard Stem Cell Institute, Cambridge, MA, USA.
¹⁹ Department of Molecular Biology, MGH, Boston, MA, USA.
²⁰ Department of Biological Regulation, Weizmann Institute of Science, Rehovot, Israel.
²¹ Institute for Medical Engineering and Science, MIT, Cambridge, MA, USA.
²² Department of Chemistry, MIT, Cambridge, MA, USA.
²³ Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA.
²⁴ Ragon Institute of MGH, MMIT, Cambridge, MA, USA.
²⁵ Gastrointestinal Unit and Center for the Study of Inflammatory Bowel Disease, MGH, Boston, MA, USA.
²⁶ Center for Computational and Integrative Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
²⁷ Howard Hughes Medical Institute, MIT, Cambridge, MA, USA.
²⁸ Genentech, San Francisco, CA, USA.
²⁹ Division of Cardiovascular Medicine and Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
³⁰ Harvard Medical School, Boston, MA, USA.
³¹ Department of Surgery, MGH, Boston, MA, USA.
³² Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
³³ Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
³⁴ Department of Biology, MIT, Cambridge, MA, USA.
³⁵ Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
³⁶ Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
³⁷ BASE Initiative, Betty Irene Moore Children's Heart Center, Lucile Packard Children's Hospital, Stanford University School of Medicine, Stanford, CA, USA.
³⁸ Broad Institute of MIT and Harvard, Cambridge, MA, USA. finucane@broadinstitute.org.
³⁹ Analytic and Translational Genetics Unit, MGH, Boston, MA, USA. finucane@broadinstitute.org.

^# Contributed equally.

Abstract

Genome-wide association studies (GWASs) are a valuable tool for understanding the biology of complex human traits and diseases, but associated variants rarely point directly to causal genes. In the present study, we introduce a new method, polygenic priority score (PoPS), that learns trait-relevant gene features, such as cell-type-specific expression, to prioritize genes at GWAS loci. Using a large evaluation set of genes with fine-mapped coding variants, we show that PoPS and the closest gene individually outperform other gene prioritization methods, but observe the best overall performance by combining PoPS with orthogonal methods. Using this combined approach, we prioritize 10,642 unique gene-trait pairs across 113 complex traits and diseases with high precision, finding not only well-established gene-trait relationships but nominating new genes at unresolved loci, such as LGR4 for estimated glomerular filtration rate and CCR7 for deep vein thrombosis. Overall, we demonstrate that PoPS provides a powerful addition to the gene prioritization toolbox.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Genetic Predisposition to Disease / genetics
Genome-Wide Association Study / methods
Humans
Multifactorial Inheritance* / genetics
Phenotype
Polymorphism, Single Nucleotide / genetics
Quantitative Trait Loci* / genetics

Abstract

Publication types

MeSH terms

Grants and funding