Computational Analysis of Microarray Data

Functional genomics data has the potential to increase GWAS power by identifying SNPs that have a higher prior probability of association. Here, we introduce a method that leverages polygenic functional enrichment to incorporate coding, conserved, regulatory, and LD-related genomic annotations into association analyses. We show via simulations with real genotypes that the method, functionally informed novel discovery of risk loci (FINDOR), correctly controls the false-positive rate at null loci and attains a 9%–38% increase in the number of independent associations detected at causal loci, depending on trait polygenicity and sample size. We applied FINDOR to 27 independent complex traits and diseases from the interim UK Biobank release (average N = 130K). Averaged across traits, we attained a 13% increase in genome-wide significant loci detected (including a 20% increase for disease traits) compared to unweighted raw p values that do not use functional data. We replicated the additional loci in independent UK Biobank and non-UK Biobank data, yielding a highly statistically significant replication slope (0.66–0.69) in each case. Finally, we applied FINDOR to the full UK Biobank release (average N = 416K), attaining smaller relative improvements (consistent with simulations) but larger absolute improvements, detecting an additional 583 GWAS loci. In conclusion, leveraging functional enrichment using our method robustly increases GWAS power.

Leveraging Polygenic Functional Enrichment to Improve GWAS Power

For multiple hypothesis testing in genomics and other large-scale data analyses, the independent hypothesis weighting (IHW) approach uses data-driven P-value weight assignment to improve power while controlling the false discovery rate. Hypothesis weighting improves the power of large-scale multiple testing. We describe independent hypothesis weighting (IHW), a method that assigns weights using covariates independent of the P-values under the null hypothesis but informative of each test's power or prior probability of the null hypothesis (
 http://www.bioconductor.org/packages/IHW
 ). IHW increases power while controlling the false discovery rate and is a practical approach to discovering associations in genomics, high-throughput biology and other large data sets.

/pdf/data-driven-hypothesis-weighting-increases-detection-power-2pke3h7o4c.pdf

Data-driven hypothesis weighting increases detection power in genome-scale multiple testing

At macroscopic scales, the human connectome comprises anatomically distinct brain areas, the structural pathways connecting them and their functional interactions. Annotation of phenotypic associations with variation in the connectome and cataloging of neurophenotypes promise to transform our understanding of the human brain. In this Review, we provide a survey of magnetic resonance imaging–based measurements of functional and structural connectivity. We highlight emerging areas of development and inquiry and emphasize the importance of integrating structural and functional perspectives on brain architecture.

Imaging human connectomes at the macroscale

Functional genomics data has the potential to increase GWAS power by identifying SNPs that have a higher prior probability of association. Here, we introduce a method that leverages polygenic functional enrichment to incorporate coding, conserved, regulatory and LD-related genomic annotations into association analyses. We show via simulations with real genotypes that the method, Functionally Informed Novel Discovery Of Risk loci (FINDOR), correctly controls the false-positive rate at null loci and attains a 9-38% increase in the number of independent associations detected at causal loci, depending on trait polygenicity and sample size. We applied FINDOR to 27 independent complex traits and diseases from the interim UK Biobank release (average N=130K). Averaged across traits, we attained a 13% increase in genome-wide significant loci detected (including a 20% increase for disease traits) compared to unweighted raw p-values that do not use functional data. We replicated the novel loci in independent UK Biobank and non-UK Biobank data, yielding a highly statistically significant replication slope (0.66-0.69) in each case. Finally, we applied FINDOR to the full UK Biobank release (average N=416K), attaining smaller relative improvements (consistent with simulations) but larger absolute improvements, detecting an additional 583 GWAS loci. In conclusion, leveraging functional enrichment using our method robustly increases GWAS power.

https://www.biorxiv.org/content/biorxiv/early/2017/11/20/222265.full.pdf

Leveraging polygenic functional enrichment to improve GWAS power

In the context of large-scale multiple hypothesis testing, the hypotheses often possess certain group structures based on additional information such as Gene Ontology in gene expression data and phenotypes in genome-wide association studies. It is hence desirable to incorporate such information when dealing with multiplicity problems to increase statistical power. In this article, we demonstrate the benefit of considering group structure by presenting a p-value weighting procedure which utilizes the relative importance of each group while controlling the false discovery rate under weak conditions. The procedure is easy to implement and shown to be more powerful than the classical Benjamini–Hochberg procedure in both theoretical and simulation studies. By estimating the proportion of true null hypotheses, the data-driven procedure controls the false discovery rate asymptotically. Our analysis on one breast cancer dataset confirms that the procedure performs favorably compared with the classical method.

/pdf/false-discovery-rate-control-with-groups-2c5rzuuvsh.pdf

James X. Hu

Papers

False Discovery Rate Control With Groups