Big datasets, accumulated from biomedical and agronomic studies, provide the potential to
identify genes that control complex human diseases and agriculturally important traits …

False positives in a Genome-Wide Association Study (GWAS) can be effectively controlled
by a fixed effect and random effect Mixed Linear Model (MLM) that incorporates population …

We propose a novel statistical framework for integrating the result from molecular
quantitative trait loci (QTL) mapping into genome-wide genetic association analysis of …

Most human diseases and agriculturally important traits are complex. Dissecting their
genetic architecture requires continued development of innovative and powerful statistical …

Background Recent developments in technology result in the generation of big data. In
genome-wide association studies (GWAS), we can get tens of million SNPs that need to be …

The mixed linear model has been widely used in genome-wide association studies (GWAS),
but its application to multi-locus GWAS analysis has not been explored and assessed. Here …

Genome-Wide Association Studies shed light on the identification of genes underlying
human diseases and agriculturally important traits. This potential has been shadowed by …

Motivation Very low-depth sequencing has been proposed as a cost-effective approach to
capture low-frequency and rare variation in complex trait association studies. However, a full …

Genome-wide association study (GWAS) and genomic prediction/selection (GP/GS) are the
two essential enterprises in genomic research. Due to the great magnitude and complexity …

Background PLINK 1 is a widely used open-source C/C++ toolset for genome-wide
association studies (GWAS) and research in population genetics. However, the steady …