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Including such features would improve benchmarking studies and accelerate the development of methods for genetic analysis. However, phenotypes are frequently simulated as an additive function of randomly selected variants, neglecting biological complexity such as non-random occurrence of causal SNPs, epistatic effects, heritability and dominance. Due to the absence of ground truth data, simulated genotype and phenotype data is needed to benchmark these methods.
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Many computational methods aim to identify genetic variants associated with diseases and complex traits. Finally, by analyzing a real data set on autoimmune diseases, we demonstrate the ability to obtain novel insights about the shared genetic architecture between ten pediatric autoimmune diseases. Numerical studies are conducted under both model generated data and simulated genetic data to show the superiority of the proposed methods and their applicability to the analysis of real genetic data. Moreover, we construct confidence intervals and statistical tests for these parameters, and provide theoretical justifications for the methods, including the coverage probability and expected length of the confidence intervals, as well as the size and power of the proposed tests.
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The rates of convergence for these estimators are studied and their asymptotic normality is established under mild conditions. A novel weighted debiasing method is developed for the logistic Lasso estimator and computationally efficient debiased estimators are proposed. Specifically, under the high-dimensional logistic regression model, we define parameters characterizing the cross-trait genetic correlation, the genetic covariance and the trait-specific genetic variance. This paper studies the problem of statistical inference for genetic relatedness between binary traits based on individual-level genome-wide association data. For the two binary traits, we let one trait be associated with 50 SNPs from the above regions with 25 SNPs for each chromosome, and let the other trait be associated only with 25 SNPs from chromosome 10, among which 12 SNPs are shared between two traits. Specifically, focusing on the Scenario I with n 1 = n 2 = n, for given choices of p and n, using the R package sim1000G (Dimitromanolakis et al., 2019), we generated genotypes of 2n unrelated individuals containing 2p SNPs based on a comprehensive haplotype map integrated over 1,184 reference individuals (International HapMap 3 Consortium, 2010), and the sequencing data over two different regions, one from chromosome 9 (GrCH37: bp 40,900 to bp 2,000,000) and the other from chromosome 10 (GrCH37: bp 7,000 to bp 2,000,000), of 503 European samples from the 1000 Genomes Project Phase 3 (1000 Genomes Project Consortium,, with p SNPs from each region. In order to justify our proposed methods for analyzing real genetic data sets, we carried out additional numerical experiments under the settings where the covariates are simulated genotypes with possible LD structures that resemble those of the human genome, and the inferences are made at a chromosomal basis.
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The region-level framework (implemented in publicly available software, SEGMENT-SCAN) has important implications for the elucidation of molecular mechanisms of disease and the rational design of potentially novel therapeutics. Notably, the introgressed segment showed a much higher concentration of expression-mediated causal effect on severity (0.9–11.5 times) than the entire locus, explaining, on average, 15.7% of the causal effect. Through a large-scale phenome-wide scan for the genes in the locus, several potential complications, including inflammatory, immunity, olfactory, and gustatory traits, were identified. We confirmed that individuals who carry the introgressed archaic segment in the locus have a substantially higher risk of developing the severe disease phenotype, estimating its contribution to expression-mediated heritability using a new summary-statistics-based approach we developed here. We identified putative causal genes, including SLC6A20, CXCR6, CCR9, and CCR5 in the locus on 3p21.31, quantifying their effect on mediating expression and on severe COVID-19. To illustrate the framework, we applied it to understanding the host genetics of COVID-19 severity. We developed an integrative transcriptomic, evolutionary, and causal inference framework for a deep region-level analysis, which integrates several published approaches and a new summary-statistics-based methodology.