MiXeR constructs a univariate mixture model for each trait followed by a bivariate mixture model for cross-trait analysis, incorporating minor allele frequency (MAF), sample size, effects of LD structure, genomic inflation due to cryptic relatedness and sample overlap into the model. Firstly, univariate MiXeR assumes that, for each trait, common genetic variants are a mixture of: 1) ‘causal’ variants and 2) non-causal variants. Under this assumption, MiXeR estimates the polygenicity (π – fraction of causal variants) and discoverability (σ2 – variance of effect size per causal variant) for a given trait using maximum likelihood estimation. SNP-based heritability h2SNP is derived from these estimates. To aid interpretation, polygenicity is presented as the number of causal variants with strongest effects required to explain 90% h2snp. A threshold of 90% is applied to prevent extrapolating model parameters into variants with infinitesimally small effects.
