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

Background The inheritance of most human diseases and agriculturally important traits is
controlled by many genes with small effects. Identifying these genes, while simultaneously …

Genome-wide association studies (GWAS) have been widely used in genetic dissection of
complex traits. However, common methods are all based on a fixed-SNP-effect mixed linear …

The popularization of large-scale genotyping projects has led to the widespread adoption of
genetic association studies as the tool of choice in the search for single nucleotide …

Previous studies have reported that some important loci are missed in single-locus genome-
wide association studies (GWAS), especially because of the large phenotypic error in field …

Compared with linear mixed model-based genome-wide association (GWA) methods,
generalized linear mixed model (GLMM)-based methods have better statistical properties …

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 …

Approaches based on linear mixed models (LMMs) have recently gained popularity for
modelling population substructure and relatedness in genome-wide association studies. In …

Motivation: Genome-wide association studies (GWAS) involving half a million or more single
nucleotide polymorphisms (SNPs) allow genetic dissection of complex diseases in a holistic …

Methods) that includes family members and non-Caucasians, FaST-LMM-Select yielded
slightly less inflation, fewer false positives and fewer false negatives (due to lack of dilution) …