  Ancestry-specific proteome-wide association studies (PWAS) based on
genetically predicted protein expression can reveal complex disease etiology
specific to certain ancestral groups. These studies require ancestry-specific
models for protein expression as a function of SNP genotypes. In order to
improve protein expression prediction in ancestral populations historically
underrepresented in genomic studies, we propose a new penalized maximum
likelihood estimator for fitting ancestry-specific joint protein quantitative
trait loci models. Our estimator borrows information across ancestral groups,
while simultaneously allowing for heterogeneous error variances and regression
coefficients. We propose an alternative parameterization of our model which
makes the objective function convex and the penalty scale invariant. To improve
computational efficiency, we propose an approximate version of our method and
study its theoretical properties. Our method provides a substantial improvement
in protein expression prediction accuracy in individuals of African ancestry,
and in a downstream PWAS analysis, leads to the discovery of multiple
associations between protein expression and blood lipid traits in the African
ancestry population.
