A multi-trait approach identified 7 novel genes for back pain.

Introduction: Back pain (BP) is a complex heritable trait with an estimated heritability of 40% to 60%. Less than half of this can be explained by known genetic variants identified in genome-wide association studies.

Objectives: We applied a powerful multi-trait and gene-based approach to association analysis of BP to identify novel genes associated with BP.

A New Method for Conditional Gene-Based Analysis Effectively Accounts for the Regional Polygenic Background.

Gene-based association analysis is a powerful tool for identifying genes that explain trait variability. An essential step of this analysis is a conditional analysis. It aims to eliminate the influence of SNPs outside the gene, which are in linkage disequilibrium with intragenic SNPs.

Multi-Trait Exome-Wide Association Study of Back Pain-Related Phenotypes.

Back pain (BP) is a major contributor to disability worldwide, with heritability estimated at 40-60%. However, less than half of the heritability is explained by common genetic variants identified by genome-wide association studies. More powerful methods and rare and ultra-rare variant analysis may offer additional insight.

Genetic Structure Analysis of 155 Transboundary and Local Populations of Cattle (, and ) Based on STR Markers.

Every week, 1-2 breeds of farm animals, including local cattle, disappear in the world. As the keepers of rare allelic variants, native breeds potentially expand the range of genetic solutions to possible problems of the future, which means that the study of the genetic structure of these breeds is an urgent task. Providing nomadic herders with valuable resources necessary for life, domestic yaks have also become an important object of study.

A Novel Framework for Analysis of the Shared Genetic Background of Correlated Traits.

We propose a novel effective framework for the analysis of the shared genetic background for a set of genetically correlated traits using SNP-level GWAS summary statistics. This framework called SHAHER is based on the construction of a linear combination of traits by maximizing the proportion of its genetic variance explained by the shared genetic factors. SHAHER requires only full GWAS summary statistics and matrices of genetic and phenotypic correlations between traits as inputs.