Unified tests for fine scale mapping and identifying sparse high-dimensional sequence associations

Joint adjustment of complex or cryptic relatedness can help to greatly improve the identification of rare and common genetic variants for quantitative traits. In deep sequencing studies of admixed individuals, cryptic relatedness and population structure notoriously confound the association analyses of high-dimensional marker sets. Existing association tests are powerful for identification of functional variants in large samples with random relatedness. These tests, however, have low power to identify susceptible variants in high-dimensional SNP sets, where n (the number of observations) is smaller than or close to m (the size of the SNP set under testing). We propose a unified test (uFineMap) for accurately localizing causal loci and a unified test (uHDSet) for identifying high-dimensional sparse associations in deep sequencing genomic data of multi-ethnic individuals. These novel tests are based on scaled sparse linear mixed regressions with Lp (0<p<1) norm regularization. They jointly adjusts for cryptic relatedness, population structure and other confounders to prevent false discoveries and improve statistical power for identifying promising individual markers and marker sets that harbor functional genetic variants of a complex trait. Under a wide range of simulated scenarios, the proposed tests appropriately controlled Type I error rate and appeared more powerful than several existing prominent methods. In terms of the detection power of susceptible variants set, the proposed approach appeared more powerful than several existing prominent methods. The practical utility of the proposed approach is illustrated by application to real DNA sequence data of Framingham Heart Study for osteoporosis data. The proposed tests identified 11 novel significant genes that were missed by the prominent famSKAT and GEMMA. Four out of six most significant pathways identified by the uHDSet have been reported to be related to BMD or osteoporosis in the literature.