Related Experiment Video
Updated: Jun 26, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
Published on: July 27, 2021
Application of genome-wide SNP data for uncovering pairwise relationships and quantitative trait loci
P C Sham1, S S Cherny, S Purcell
1Department of Psychiatry, Genome Research Centre, Li Ka Shing Faculty of Medicine, The University of Hong Kong, L10-69, Laboratory Block, 21 Sassoon Road, Pokfulam, Hong Kong. pcsham@hku.hk
Abstract:
The genetic analysis of quantitative traits in humans is changing as a result of the availability of whole-genome SNP data. Heritability analysis can make use of actual genetic sharing between pairs of individuals estimated from the genotype data, rather than the expected genetic sharing implied by their family relationship. This could provide more accurate heritability estimates and help to overcome the equal environment assumption. Quantitative trait locus (QTL) linkage mapping can make use of local genetic sharing inferred from very dense local genotype data from pedigree members or individuals not previously known to be related. This approach may be particularly suited for detecting loci that contain rare variants with major effect on the phenotype. Finally, whole-genome SNP data can be used to measure the genetic similarity between individuals to provide matched sets for association studies, in order to avoid spurious association from population stratification.
Related Concept Videos
Genome-wide Association Studies-GWAS
GWAS does not require the identification of the target gene involved in...
Single Nucleotide Polymorphisms-SNPs
Comparing Copy Number Variations and SNPs
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
Evolutionary Relationships through Genome Comparisons
Pharmacogenomics: Identification of New Drug Targets
Modern Molecular Taxonomy

