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Including patient-specific information about nearby somatic and germline alterations improves the accuracy of neoantigen prediction, potentially impacting cancer vaccine design.
GeneATLAS is a web resource that presents genetic association results for 118 non-binary and 660 binary traits using UK Biobank data. This atlas allows researchers to query these results without incurring high computational costs.
The authors extend stratified linkage disequilibrium score regression to partition the heritability of both low-frequency and common variants in 40 heritable traits from the UK Biobank, providing insights into low-frequency and rare variant functional architectures.
Analysis of mRNA splicing in the dorsolateral prefrontal cortex from two cohorts established to study aging identifies variations in pre-mRNA splicing events that are associated with Alzheimer’s disease.
Signed linkage disequilibrium profile regression is a new method for detecting directional effects of genomic annotations on disease risk. The results implicate new causal disease genes and can suggest mechanisms underlying the effects of causal genes on disease.
Analysis of GTEx, cancer and autism data sets shows that cis-regulatory variation can modify the penetrance of coding variants. Deleterious coding variants on regulatory haplotypes resulting in high expression are enriched in disease cohorts and selected against in general populations.
SAIGE (Scalable and Accurate Implementation of GEneralized mixed model) is a generalized mixed model association test that can efficiently analyze large data sets while controlling for unbalanced case-control ratios and sample relatedness, as shown by applying SAIGE to the UK Biobank data for > 1,400 binary phenotypes.
The authors identify whole-genome doubling (WGD) in 30% of ~10,000 sequenced tumors from patients with advanced cancer. WGD correlates with increased risk of death across cancer types.
A new set of functional annotations based on fine-mapped molecular quantitative trait loci from GTEx and BLUEPRINT consortium data are enriched for disease heritability across 41 diseases and complex traits.
Analysis of individuals with neurodevelopmental disorders (NDDs) with epilepsy identifies 33 genes with a significant excess of de novo variants. Comparison of rates of de novo variants between NDDs with or without epilepsy highlights differences between these phenotypic groups.
This analysis compares methods for estimating the heritability and genetic architecture of complex traits using whole-genome data. The results provide guidance for best practices and proper interpretation of published heritability estimates.
MACHINA is an algorithm that analyzes metastatic cancer sequence data to simultaneously infer clone trees and migration histories. Analysis of different metastatic cancer datasets provides more evidence for simple, rather than complex, migration patterns.
This study presents a framework to evaluate rare and de novo variation from whole-genome sequencing (WGS). The work suggests that robust results from WGS studies will require large cohorts and strategies that consider the substantial multiple-testing burden.
BayesS estimates SNP-based heritability, polygenicity, and the relationship between effect size and minor allele frequency using genome-wide SNP data. Applying BayesS to UK Biobank data identifies signatures of natural selection for 23 complex traits.
A new method tests whether disease heritability is enriched near genes with high tissue-specific expression. The authors use gene expression data together with GWAS summary statistics for 48 diseases and traits to identify disease-relevant tissues.