12 research outputs found
Heatmap displays the rank of 105 tissues (y-axis) in terms of their relevance for each of the 43 GWAS traits (x-axis).
<p>Traits are organized by hierarchical clustering. Tissues are organized into ten tissue groups.</p
Simulation results for comparing using multiple annotations versus a single annotation.
<p>(A) Power to detect trait-relevant tissues by different approaches in various settings at a fixed FDR of 0.1. x-axis shows the values of the two annotation coefficients used in the simulations. Settings where at least one annotation coefficient is zero are shaded in grey. The setting where the annotation coefficients equal to the median estimates from real data (i.e. <b><i>α</i></b> = <b>(0.1, 0.05)</b>) is shaded in gold. The first number for each method in the figure legend represents the number of times each method is ranked as the best in 25 simulation settings where none of the annotations have zero coefficients; while the second number represents the number of times each method is ranked as the best in 11 simulation settings where at least one annotation has a zero coefficient. (B) Annotation coefficient estimates by SMART are centered around the truth (horizontal dotted gold lines). (C) Mean power (y-axis) to detect trait-relevant tissues by different approaches at different FDR values (x-axis). Error bar shows the standard deviation computed across 10 simulation groups, each of which contains 1,000 simulation replicates (i.e. a total of 10,000 simulations). <i>p</i>-values from the paired t-test are used to compare methods at different FDR cutoffs. Note that the error bar is large due to the small number of simulation replicates within each simulation group. For (B) and (C), simulations were done at <b><i>α</i></b> = <b>(0.1, 0.05)</b>. FDR, false discovery rate.</p
Identifying and exploiting trait-relevant tissues with multiple functional annotations in genome-wide association studies
<div><p>Genome-wide association studies (GWASs) have identified many disease associated loci, the majority of which have unknown biological functions. Understanding the mechanism underlying trait associations requires identifying trait-relevant tissues and investigating associations in a trait-specific fashion. Here, we extend the widely used linear mixed model to incorporate multiple SNP functional annotations from omics studies with GWAS summary statistics to facilitate the identification of trait-relevant tissues, with which to further construct powerful association tests. Specifically, we rely on a generalized estimating equation based algorithm for parameter inference, a mixture modeling framework for trait-tissue relevance classification, and a weighted sequence kernel association test constructed based on the identified trait-relevant tissues for powerful association analysis. We refer to our analytic procedure as the Scalable Multiple Annotation integration for trait-Relevant Tissue identification and usage (SMART). With extensive simulations, we show how our method can make use of multiple complementary annotations to improve the accuracy for identifying trait-relevant tissues. In addition, our procedure allows us to make use of the inferred trait-relevant tissues, for the first time, to construct more powerful SNP set tests. We apply our method for an in-depth analysis of 43 traits from 28 GWASs using tissue-specific annotations in 105 tissues derived from ENCODE and Roadmap. Our results reveal new trait-tissue relevance, pinpoint important annotations that are informative of trait-tissue relationship, and illustrate how we can use the inferred trait-relevant tissues to construct more powerful association tests in the Wellcome trust case control consortium study.</p></div
Association results for SNP set tests in WTCCC using different SNP weights.
<p>Results are shown for 17 genes identified to be significant by at least one SNP weighting option in four dieseases from the WTCCC data (CD, RA, T1D and T2D). All these genes have been previously identified to be associated with the corresponding trait (cited references). Approaches that yield a p-value passing the genome-wide significance threshold (8.95x10<sup>-6</sup>) are highlighted in bold.</p
SNP set test results on Crohn’s disease (CD) using different SNP weights.
<p>(A) Manhattan plot shows association signal across genes (x-axis) detected by SNP set tests using three different sets of SNP weights. EqualWeight (black): equal SNP weights. HC (red): SNP weights constructed using the estimated coefficient parameters for continuous histone mark based annotations in the GWAS consortium study. HB (green): SNP weights constructed using the estimated coefficient parameters for binary histone mark based annotations in the GWAS consortium study. The gold dashed line represents genome-wide significance threshold (8.95x10<sup>-6</sup>). (B) The same results are displayed with QQ plot of -log10 p-values. Grey shaded area represents the 95% point-wise confidence interval.</p
Simulation results for using different weights to construct SNP set tests.
<p>(A) QQ plot of -log10 p values from SNP set tests using different SNP weights under the null simulations. Tests using different weights all control type I error well. (B) Power to detect causal blocks by SNP set tests using different SNP weights in the simulation setting where <b>α</b> = <b>(0.4, 0.4)</b>. (C) Power to detect causal blocks by SNP set tests using different SNP weights in the simulation setting where <b>α</b> = <b>(0.4, 0).</b> For both (B) and (C), Power are evaluated at a genome-wide significance threshold of 1x10<sup>-4</sup>. Standard errors are computed across 1,000 simulation replicates. The x-axis shows the proportion of causal SNPs that have identical values for the two annotations, which measures correlation between the two annotations.</p
Additional file 1 of Feasible intervention combinations for achieving a safe exit of the Zero-COVID policy in China and its determinants: an individual-based model study
Additional file 1
Additional file 1 of Association of immune cell composition with the risk factors and incidence of acute coronary syndrome
Additional file 1. Figure S1. Histograms of immune cell proportions after arcsine square root transformation. Immune cell composition observed from routine blood tests (A) and estimated from DNA methylation profiles (B). Lym, lymphocyte proportion; Mono, monocyte proportion; Neu, neutrophil proportion; CD8T, CD8+ T cell proportion; CD4T, CD4+ T cell proportion; B, B cell proportion; and NK, natural killer cell proportion
Additional file 2 of Association of immune cell composition with the risk factors and incidence of acute coronary syndrome
Additional file 2. Table S3. Association between immune cell composition and risk factors of ACS
DataSheet1.docx
<p>The domestic water buffalo is native to the Asian continent but through historical migrations and recent importations, nowadays has a worldwide distribution. The two types of water buffalo, i.e., river and swamp, display distinct morphological and behavioral traits, different karyotypes and also have different purposes and geographical distributions. River buffaloes from Pakistan, Iran, Turkey, Egypt, Romania, Bulgaria, Italy, Mozambique, Brazil and Colombia, and swamp buffaloes from China, Thailand, Philippines, Indonesia and Brazil were genotyped with a species-specific medium-density 90K SNP panel. We estimated the levels of molecular diversity and described population structure, which revealed historical relationships between populations and migration events. Three distinct gene pools were identified in pure river as well as in pure swamp buffalo populations. Genomic admixture was seen in the Philippines and in Brazil, resulting from importations of animals for breed improvement. Our results were largely consistent with previous archeological, historical and molecular-based evidence for two independent domestication events for river- and swamp-type buffaloes, which occurred in the Indo-Pakistani region and close to the China/Indochina border, respectively. Based on a geographical analysis of the distribution of diversity, our evidence also indicated that the water buffalo spread out of the domestication centers followed two major divergent migration directions: river buffaloes migrated west from the Indian sub-continent while swamp buffaloes migrated from northern Indochina via an east-south-eastern route. These data suggest that the current distribution of water buffalo diversity has been shaped by the combined effects of multiple migration events occurred at different stages of the post-domestication history of the species.</p