Genome-wide complex trait analysis

Genome-wide complex trait analysis (GCTA) GREML is a statistical method for variance component estimation in genetics which quantifies the total narrowsense (additive) contribution to a trait's heritability of a particular subset of genetic variants (typically limited to SNPs with MAF >1%, hence terms such as "chip heritability"/"SNP heritability"). This is done by directly quantifying the chance genetic similarity of unrelated strangers and comparing it to their measured similarity on a trait; if two strangers are relatively similar genetically and also have similar trait measurements, then this indicates that the measured genetics causally influence that trait, and how much. This can be seen as plotting prediction error against relatedness. The GCTA framework extends to bivariate genetic correlations between traits; it can also be done on a per-chromosome basis comparing against chromosome length; and it can also examine changes in heritability over aging and development.

GCTA heritability estimates are useful because they can lower bound the genetic contributions to traits such as intelligence without relying on the assumptions used in twin studies and other family studies and pedigree analyses, thereby corroborating them, and enabling the design of well-powered Genome-wide association study (GWAS) designs to find the specific genetic variants. For example, a GCTA estimate of 30% SNP heritability is consistent with a larger total genetic heritability of 70%. However, if the GCTA estimate was ~0%, then that would imply one of three things: a) there is no genetic contribution, b) the genetic contribution is entirely in the form of genetic variants not included, or c) the genetic contribution is entirely in the form of non-additive effects such as epistasis/dominance. The ability to run GCTA on subsets of chromosomes and regress against chromosome length can reveal whether the responsible genetic variants cluster or are distributed evenly across the genome or are sex-linked. Examining genetic correlations can reveal to what extent observed correlations, such as between intelligence and socioeconomic status, are due to the same genetic traits, and in the case of diseases, can indicate shared causal pathways such as the overlap of schizophrenia with other mental diseases and intelligence-reducing variants.