Additional file 1: Figures S1 and S2. of Leveraging local ancestry to detect gene-gene interactions in genome-wide data

Power comparison between SNP-based and local ancestry-based interaction tests when using inferred ancestry and 1 M genotyped SNPs. Figure S2. Power comparison between SNP-based and local ancestry-based interaction tests when using a two steps approach. (PDF 555 kb

Top causal paths reported in real data analysis that localized within GWAS regions for 8 autoimmune diseases.

<p>For each path, we report the chromosome, the RSID of the implicated SNP, the implicated mark type, the posterior probability we assigned to this path, three Z-scores (SNP to mark association, mark to expression association, SNP to expression association), the GENCODE gene around which this region was centered, the ChromHMM [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1007240#pgen.1007240.ref024" target="_blank">24</a>] annotation for the SNP, and the number of regulatory motifs altered by the SNP, as designated by HaploReg [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1007240#pgen.1007240.ref025" target="_blank">25</a>].</p

Additional file 2: of Leveraging local ancestry to detect gene-gene interactions in genome-wide data

R script and corresponding input files to perform 1 ° of freedom interaction test between ancestry segments. (ZIP 40 kb

Methods for fine-mapping with chromatin and expression data

<div><p>Recent studies have identified thousands of regions in the genome associated with chromatin modifications, which may in turn be affecting gene expression. Existing works have used heuristic methods to investigate the relationships between genome, epigenome, and gene expression, but, to our knowledge, none have explicitly modeled the chain of causality whereby genetic variants impact chromatin, which impacts gene expression. In this work we introduce a new hierarchical fine-mapping framework that integrates information across all three levels of data to better identify the causal variant and chromatin mark that are concordantly influencing gene expression. In simulations we show that our method is more accurate than existing approaches at identifying the causal mark influencing expression. We analyze empirical genetic, chromatin, and gene expression data from 65 African-ancestry and 47 European-ancestry individuals and show that many of the paths prioritized by our method are consistent with the proposed causal model and often lie in likely functional regions.</p></div

Schematic of hierarchical model whereby SNPs affect histone marks, which in turn affect gene expression.

<p>We illustrate a scenario where SNP <i>g</i>₁ and mark <i>h</i>₁ are causal. All other induced correlations, such as the effect of <i>g</i>₁ on <i>h</i>₂, are an effect of LD and/or correlations among marks. To the right we show our mathematical model for this hierarchical framework. On the top level, we model mark-expression associations with a Multivariate Normal (MVN) distribution. On the bottom, we jointly model all associations between all SNPs and marks with a Matrix Variate Normal distribution (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1007240#sec008" target="_blank">Methods</a>).</p

90% credible sets for SNP-, mark-, and path-mapping.

<p>We compare <i>pathfinder</i> to the technique of independently fine-mapping the two levels of data, with respect to (A) the calibration of their credible sets and (B) the size of their credible sets. In (A), we compare the proportion of causal variants that were captured in the 90% credible sets using <i>pathfinder</i> vs. independent fine-mapping against the expected proportion (represented by the dotted line). In (B), we display the corresponding sizes of these credible sets.</p

Comparison of our method to standard eQTL + hQTL overlap analyses.

<p>In overlap analyses, only the top SNP for association to each histone mark and gene expression is considered. We demonstrate significant gains in our method with respect to mark-finding accuracy, where SNP-mapping performance is comparable between the two methods.</p

Spatial relationships between SNP, mark, and TSS in top paths reported by <i>pathfinder</i> vs random paths.

<p>(A) Distances from SNP to mark (B) Distances from mark to TSS (C) Distances from SNP to TSS.</p

Performance of our method as we vary levels of variance explained, SNP LD, mark correlations, and the prior variance parameter.

<p>(A-C) We simultaneously vary the variance explained by SNP and mark from 0.1 to 0.5 per region. (D-I) We stratified based on mean SNP/mark correlations at the causal SNP/mark. (J-L) We show that <i>pathfinder</i> is not sensitive to variations in our prior variance parameter.</p

Relationship between the product of the SNP-mark and mark-expression effect sizes against the overall SNP-expression effect size.

<p>(A) We observe a high correlation (r = 0.91) between these effect size vectors, indicating that our method is identifying many pathways that are likely to be following our causal model. Here we included only the top paths whose posterior probabilities for causality were assigned to be greater than 0.1. (B) We show that a significant correlation does not exist for randomly chosen paths.</p