Genetic Correlation

A technique that leverages the correlation structure between genetic variants to estimate genetic correlation and SNP heritability directly from GWAS summary statistics. Unlike methods requiring individual-level data, LDSC regresses association test statistics against LD scores—measures of how much a variant tags its neighbors. The slope of this regression reveals the contribution of true polygenic signal versus confounding biases like population stratification. This makes LDSC a computationally efficient, standard tool for quantifying shared genetic architecture between two traits without accessing raw genotypes.

The foundational discovery engine that powers genetic correlation analysis. A GWAS scans millions of single nucleotide polymorphisms (SNPs) across the genome in large population cohorts to identify statistical associations with a phenotype. The output—summary statistics containing effect sizes, standard errors, and p-values for each variant—serves as the raw input for genetic correlation methods. Without robust, well-powered GWAS, estimates of shared genetic effects between traits remain unreliable. Key quality control steps include correcting for genomic inflation and ensuring adequate sample size.

The proportion of phenotypic variance in a population attributable to the additive effects of all measured common genetic variants. This metric sets the upper bound for genetic correlation—two traits cannot share more genetic influence than their individual heritabilities allow. Estimated via methods like LD Score regression or GREML, h²SNP distinguishes true polygenic signal from environmental factors. A trait with near-zero heritability cannot exhibit meaningful genetic correlation, making this a critical preliminary check before investigating shared genetic architecture.

A causal inference framework that uses genetic variants as instrumental variables to test whether one trait causally influences another. While genetic correlation measures shared architecture, it does not distinguish causation from pleiotropy or confounding. MR addresses this by leveraging variants robustly associated with an exposure to estimate its causal effect on an outcome, provided core assumptions hold: the variant associates with the exposure, is independent of confounders, and affects the outcome only through the exposure. MR and genetic correlation are complementary analyses in post-GWAS pipelines.

The phenomenon where a single genetic variant or locus influences multiple, seemingly unrelated phenotypes. Pleiotropy is the biological mechanism underlying observed genetic correlations. - Horizontal pleiotropy: A variant affects two traits through independent biological pathways—a source of bias in Mendelian randomization. - Vertical pleiotropy: A variant affects a downstream trait only through its effect on an upstream trait, representing a causal chain. Distinguishing between these forms is essential for interpreting whether a genetic correlation reflects shared biology or causal relationships.

Systematic allele frequency differences between subpopulations due to ancestry, which can induce spurious genetic correlations if uncorrected. When both traits and allele frequencies vary across population subgroups, variants tagging ancestry—rather than causal biology—can generate inflated estimates of shared genetic effects. Methods like principal component analysis (PCA) and genomic control adjust for this confounding. Cross-ancestry genetic correlation studies require particular care, as LD patterns and allele frequencies differ substantially between continental populations, threatening the portability of findings.