Partitioning and aggregating cross-tissue and tissue-specific genetic effects to identify gene-trait associations.

TWAS have shown great promise in extending GWAS loci to a functional understanding of disease mechanisms. In an effort to fully unleash the TWAS and GWAS information, we propose MTWAS, a statistical framework that partitions and aggregates cross-tissue and tissue-specific genetic effects in identifying gene-trait associations. We introduce a non-parametric imputation strategy to augment the inaccessible tissues, accommodating complex interactions and non-linear expression data structures across various tissues. We further classify eQTLs into cross-tissue eQTLs and tissue-specific eQTLs via a stepwise procedure based on the extended Bayesian information criterion, which is consistent under high-dimensional settings. We show that MTWAS significantly improves the prediction accuracy across all 47 tissues of the GTEx dataset, compared with other single-tissue and multi-tissue methods, such as PrediXcan, TIGAR, and UTMOST. Applying MTWAS to the DICE and OneK1K datasets with bulk and single-cell RNA sequencing data on immune cell types showcases consistent improvements in prediction accuracy. MTWAS also identifies more predictable genes, and the improvement can be replicated with independent studies. We apply MTWAS to 84 UK Biobank GWAS studies, which provides insights into disease etiology.

Practical guidance and workflows for identifying fast evolving non-coding genomic elements using PhyloAcc.

Comparative genomics provides ample ways to study genome evolution and its relationship to phenotypic traits. By developing and testing alternate models of evolution throughout a phylogeny, one can estimate rates of molecular evolution along different lineages in a phylogeny and link these rates with observations in extant species, such as convergent phenotypes. Pipelines for such work can help identify when and where genomic changes may be associated with, or possibly influence, phenotypic traits. We recently developed a set of models called PhyloAcc, using a Bayesian framework to estimate rates of nucleotide substitution on different branches a phylogenetic tree and evaluate their association with pre-defined or estimated phenotypic traits PhyloAcc-ST and PhyloAcc-GT both allow users to define a priori a set of target lineages and then compare different models to identify loci accelerating in one or more target lineages. Whereas ST considers only one species tree across all input loci, GT considers alternate topologies for every locus. PhyloAcc-C simultaneously models molecular rates and rates of continuous trait evolution,allowing the user to ask whether the two are associated. Here we describe these models and provide tips and workflows on how to prepare the input data and run PhyloAcc.

A phylogenetic method linking nucleotide substitution rates to rates of continuous trait evolution.

Genomes contain conserved non-coding sequences that perform important biological functions, such as gene regulation. We present a phylogenetic method, PhyloAcc-C, that associates nucleotide substitution rates with changes in a continuous trait of interest. The method takes as input a multiple sequence alignment of conserved elements, continuous trait data observed in extant species, and a background phylogeny and substitution process. Gibbs sampling is used to assign rate categories (background, conserved, accelerated) to lineages and explore whether the assigned rate categories are associated with increases or decreases in the rate of trait evolution. We test our method using simulations and then illustrate its application using mammalian body size and lifespan data previously analyzed with respect to protein coding genes. Like other studies, we find processes such as tumor suppression, telomere maintenance, and p53 regulation to be related to changes in longevity and body size. In addition, we also find that skeletal genes, and developmental processes, such as sprouting angiogenesis, are relevant.