Evaluation of five candidate genes from GWAS for association with oligozoospermia in a Han Chinese population.

backgroundOligozoospermia is one of the severe forms of idiopathic male infertility. However, its pathology is largely unknown, and few genetic factors have been defined. Our previous genome-wide association study (GWAS) has identified four risk loci for non-obstructive azoospermia (NOA).

objectiveTo investigate the potentially functional genetic variants (including not only common variants, but also less-common and rare variants) of these loci on spermatogenic impairment, especially oligozoospermia. DESIGN SETTING AND

participantsA total of 784 individuals with oligozoospermia and 592 healthy controls were recruited to this study from March 2004 and January 2011. MEASUREMENTS: We conducted a two-stage study to explore the association between oligozoospermia and new makers near NOA risk loci. In the first stage, we used next generation sequencing (NGS) in 96 oligozoospermia cases and 96 healthy controls to screen oligozoospermia-susceptible genetic variants. Next, we validated these variants in a large cohort containing 688 cases and 496 controls by SNPscan for high-throughput Single Nucleotide Polymorphism (SNP) genotyping. RESULTS AND LIMITATIONS: Totally, we observed seven oligozoospermia associated variants (rs3791185 and rs2232015 in PRMT6, rs146039840 and rs11046992 in Sox5, rs1129332 in PEX10, rs3197744 in SIRPA, rs1048055 in SIRPG) in the first stage. In the validation stage, rs3197744 in SIRPA and rs11046992 in Sox5 were associated with increased risk of oligozoospermia with an odds ratio (OR) of 4.62 (P  =  0.005, 95%CI 1.58-13.4) and 1.82 (P  =  0.005, 95%CI 1.01-1.64), respectively. Further investigation in larger populations and functional characterizations are needed to validate our findings.

conclusionsOur study provides evidence of independent oligozoospermia risk alleles driven by variants in the potentially functional regions of genes discovered by GWAS. Our findings suggest that integrating sequence data with large-scale genotyping will serve as an effective strategy for discovering risk alleles in the future.