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Selecting causal genes from genome-wide association studies via functionally coherent subnetworks

Abstract

Genome-wide association (GWA) studies have linked thousands of loci to human diseases, but the causal genes and variants at these loci generally remain unknown. Although investigators typically focus on genes closest to the associated polymorphisms, the causal gene is often more distal. Reliance on published work to prioritize candidates is biased toward well-characterized genes. We describe a 'prix fixe' strategy and software that uses genome-scale shared-function networks to identify sets of mutually functionally related genes spanning multiple GWA loci. Using associations from 100 GWA studies covering ten cancer types, our approach outperformed the common alternative strategy in ranking known cancer genes. As more GWA loci are discovered, the strategy will have increased power to elucidate the causes of human disease.

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Figure 1: Overview of the prix fixe strategy.
Figure 2: Functional connectivity patterns in prostate cancer.
Figure 3: Rank-based analysis of SCGC prioritization.
Figure 4: Prix fixe gene-score distribution and functional enrichment.

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Acknowledgements

We thank members of the Roth lab and the Center for Cancer Systems Biology (CCSB) at the Dana-Farber Cancer Institute (DFCI) for helpful comments and discussion; the lab of Q. Morris for assistance with GeneMANIA data; and M. Çokol and J. Mellor for useful conversations and advice during manuscript preparation. This work was primarily supported by Center of Excellence in Genomic Science (CEGS) grant P50 (HG004233) from the NHGRI awarded to M.V. and F.P.R. F.P.R. is additionally supported by US National Institutes of Health (NIH) grants (HG003224 and HL107440), the Krembil and Avon Foundations, a Canadian Ontario Research Fund Research Excellence Award and the Canada Excellence Research Chairs Program. C.A.M. was supported in this work by an NIH grant (HL098938), the Leducq Foundation and the Harvard Stem Cell Institute. M.T. was supported by an NIH grant (HG004098).

Author information

Authors and Affiliations

Authors

Contributions

M.T., G.M., C.A.M. and F.P.R. conceived of the project. M.T., G.M. and T.H. performed computational analyses. M.T., G.M., C.A.M. and F.P.R. wrote the manuscript. M.V., C.A.M. and F.P.R. oversaw and guided the research effort.

Corresponding authors

Correspondence to Murat Taşan or Frederick P Roth.

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Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Much of the human proteome is unrepresented in protein-protein interaction databases.

The proteome as defined here consists of all 20484 “protein-coding” genes in the NCBI GENE database. Interactions, both binary and co-complex, are taken from the HINT high-quality amortized protein-protein interaction database.

Supplementary Figure 2 Historical investigation bias in the literature.

Genes characterized at earlier dates continue to appear in publications at higher rates than more-recently characterized genes. Circle positions indicate mean publication rate (y-axis) for genes first characterized during or before each year from 1990–2012 (x-axis). Circle sizes indicate the (cumulative) number of genes characterized during or before each year.

Supplementary Figure 3 Cofunction network (CFN) coverage in terms of genes and gene pairs.

(a,b) Coverage shown for genes (a) and gene pairs (b). “HumanFunc” and “GeneMania” are computed as described in Online Methods. NCBI Gene data downloaded on 2013-07-17.

Supplementary Figure 4 Efficient enrichment for dense prix fixe subnetworks using a prostate cancer case study.

Boxplot shows candidate prix fixe subnetwork fitness evolution over 20 generations, circles within boxes indicated mean fitness, whiskers extend cover the full range of observed fitnesses. Marginal histogram (on right) indicates distribution of final generations’ mean fitnesses for 1000 random trials (see Online Methods). Empirical P-value for final generation’s subnetwork enrichment is computed against this marginal distribution (dashed line).

Supplementary Figure 5 Prix fixe scores are uncorrelated with LD (r2) values.

Each scatter plot point is a candidate breast cancer gene. Correlation is computed using Kendall’s τ rank coefficient. Blue genes indicate significantly differentially-expressed mRNA levels in matched case-control TCGA breast cancer (BRCA) samples, while red genes indicate no evidence of cancer-dependent differential expression. Flanking boxplots indicate score distributions of differentially- and not-differentially-expressed genes. Boxplot whiskers extend to 1.5×IQR; outliers not shown. Boxplots compared by one-sided Wilcoxon rank sum tests.

Supplementary Figure 6 Prix fixe score robustness with respect to varying LD (r2) thresholds.

Each histogram represents a collection of Kendall’s τ rank correlation coefficients. Each single correlation coefficient represents a comparison of prix fixe rank orders for a single analyzed trait when the method is repeated using two different r2 thresholds. (a) “Pure” replication (to measure stochastic variance) of the primary analyses using the identical r2 ≥ 0.50 threshold. (b) Comparison of scores between primary analyses (r2 ≥ 0.50) and a ‘permissive’ (r2 ≥ 0.25) threshold. (c) Comparison of scores between primary analyses (r2 ≥ 0.50) and a ‘restrictive’ (r2 ≥ 0.75) threshold.

Supplementary Figure 7 Rank-based analysis of Sanger cancer gene census (SCGC) prioritization when using a ‘permissive’ LD threshold of r2 ≥0.25.

Genes are ranked within each cancer-associated locus and normalized ranks of known cancer (i.e. SCGC) genes are shown as dots for prix fixe-based (“PF”, left) and LD-based (“r2”, right) rankings (100 is highest ranked, 0 is lowest). Average relative rank of SCGC genes (for both methods) within each locus identified by horizontal bars; number of multigenic loci shown above as “n”. Right-most plot (“Union”) shows pooled results across all cancer-associated loci. PF SCGC ranks significantly outperform LD-based SCGC ranks (P = 0.025, one-sided paired Wilcoxon signed-rank test).

Supplementary Figure 8 Rank-based analysis of Sanger cancer gene census (SCGC) prioritization when using a ‘restrictive’ LD threshold of r2 ≥0.75.

Genes are ranked within each cancer-associated locus and normalized ranks of known cancer (i.e. SCGC) genes are shown as dots for prix fixe-based (“PF”, left) and LD-based (“r2”, right) rankings (100 is highest ranked, 0 is lowest). Average relative rank of SCGC genes (for both methods) within each locus identified by horizontal bars; number of multigenic loci shown above as “n”. Right-most plot (“Union”) shows pooled results across all cancer-associated loci. PF SCGC ranks significantly outperform LD-based SCGC ranks (P = 0.028, one-sided paired Wilcoxon signed-rank test).

Supplementary Figure 9 Prix fixe score robustness with respect to varying cofunction networks (CFNs).

Each histogram represents a collection of Kendall’s τ rank correlation coefficients. Each single correlation coefficient represents a comparison of prix fixe rank orders for a single analyzed trait when the method is repeated using two different CFNs. (a) Comparison of scores between primary analyses’ CFN (HF GM) and the HF-alone CFN. (b) Comparison of scores between primary analyses’ CFN (HF GM) and the GM-alone CFN. (c) Comparison of scores between primary analyses’ CFN (HF GM) and a STRING-augmented CFN (HF GM STRING).

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–9 (PDF 891 kb)

Supplementary Table 1

GWAS data This table provides all of the input data originating in published GWA studies. The majority of the data here originate directly from the NHGRI GWAS catalog [2]. Each ‘sheet’ represents a single study in this work. Column data is taken directly from the catalog. Each row represents a single trait-associated tagSNP. (XLSX 159 kb)

Supplementary Table 2

Analysis Scores Run results for all studies examined in this work. Each ‘sheet’ corresponds to a single trait. (XLSX 665 kb)

Supplementary Table 3

Functional enrichment results GO term functional enrichment results for all traits. (XLSX 373 kb)

Supplementary Table 4

Extended summary table GO term functional enrichment results for all traits. (XLSX 33 kb)

Supplementary Table 5

T2D replication results Results for the replication experiment using type-II diabetes (T2D) loci (identified independently from the T2D analysis included in our primary study). Three sheets hold (i) genes and prix fixe scores, (ii) annotations for genes having causal links to diabetes, and (iii) ‘ordered’ functional enrichment results. (XLSX 34 kb)

Prix fixe software

Includes the R package to run a prix fixe analysis (beta version), a reference manual, and an R/Bioconductor vignette for the package. (ZIP 280 kb)

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Taşan, M., Musso, G., Hao, T. et al. Selecting causal genes from genome-wide association studies via functionally coherent subnetworks. Nat Methods 12, 154–159 (2015). https://doi.org/10.1038/nmeth.3215

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