User:IKingGrizz/Complex traits

Methods[edit]
See also: Quantitative genetics, Quantitative trait locus, and Genome-wide association study

QTL mapping[edit]
A quantitative trait locus (QTL) is a section of the genome associated with variation in a quantitative, or complex, trait. To identify QTLs, QTL mapping is performed on individuals with differing genotypes. First, mapping involves either full-genome sequencing or the genotyping of many marker loci throughout the genome; then, phenotypes of interest are measured. For example, the expression levels of different genes in the genome is one commonly-measured phenotype (the associated loci are called eQTLs). At each locus, individuals are grouped by their genotype, and statistical tests are performed to determine whether measured trait values for one group differ significantly from the overall mean for all groups. Identified loci may not be QTLs themselves, but are likely in linkage disequilibrium—and therefore strongly associate—with the loci actually influencing the trait.

GWAS[edit]
A genome-wide association study (GWAS) is a method similar to QTL mapping used to identify variants associated with complex traits. Association mapping differs from QTL mapping primarily in that GWASs are only performed with random-mating populations; because all the alleles in the population are tested at the same time, multiple alleles at each locus can be compared.

Twin Studies
Twin Studies is an observational test that can help figure out what components of a complex trait are genetic and what are environmental. They do this by comparing the differences in a trait between monozygotic twins since they each have the same DNA and the only difference between them should come from environmental factors.

GWAS
A Genome-Wide Association Study (GWAS) is a technique used to find gene variants linked to complex traits. A GWAS is done with populations that mate randomly because all the genetic variants are tested at once. Then researchers can compare the different alleles at a locus. It is similar to QTL mapping. The most common set-up for a GWAS is a case study which creates two populations one with the complex trait we are looking at and one without the complex trait. With the two populations researchers will map every subject's genome and compare them to find different variance in the SNPs between the two populations. Both populations should have similar environmental backgrounds. GWAS is only looking at the DNA and does not include differences that would be caused by environmental factors.

Statistical test, such as a chi squared is used to find if there is association with the trait and each of the SNPs tested. The statistical test produces a p-value which the researcher will use to conclude if the SNP is significant. This number can range from being higher or lower at the researcher's discretion. The data can then be visualized in a Manhattan plot which takes the -log (p-value) so all the significant SNPs are at the top of the graph.