Non-obstructive azoospermia （NOA） is a severe defect in male reproductive health that occurs in 1% of adult men. In a previous study, we identified that rs7099208 is located within the last intron of FAM160B1 at 10q...Non-obstructive azoospermia （NOA） is a severe defect in male reproductive health that occurs in 1% of adult men. In a previous study, we identified that rs7099208 is located within the last intron of FAM160B1 at 10q25.3. In this study, we analysed expression Quantitative Trait Loci （eQTL） of FAM16OB1, ABLIM1 and TRUB1, the three genes surrounding rs7099208. Only the expression level of FAM16OB1 was reduced for the homozygous alternate genotype （GG） of rs7099208, but not for the homozygous reference or heterozygous geno- types. FAM160B1 is predominantly expressed in human testes, where it is found in spermatocytes and round sper- matids. From 17 patients with NOA and five with obstructive azoospermia （OA）, immunohistochemistry revealed that expression of FAM160B1 is reduced, or undetectable in NOA patients, but not in OA cases or normal men. We conclude that rs7099208 is associated with NOA via a reduction in the expression of FAM160B1.展开更多
Non-coding genomic variants constitute the majority of trait-associated genome variations;however,the identification of functional non-coding variants is still a challenge in human genetics,and a method for systematic...Non-coding genomic variants constitute the majority of trait-associated genome variations;however,the identification of functional non-coding variants is still a challenge in human genetics,and a method for systematically assessing the impact of regulatory variants on gene expression and linking these regulatory variants to potential target genes is still lacking.Here,we introduce a deep neural network(DNN)-based computational framework,RegVar,which can accurately predict the tissue-specific impact of non-coding regulatory variants on target genes.We show that by robustly learning the genomic characteristics of massive variant-gene expression associations in a variety of human tissues,RegVar vastly surpasses all current non-coding variant prioritization methods in predicting regulatory variants under different circumstances.The unique features of RegVar make it an excellent framework for assessing the regulatory impact of any variant on its putative target genes in a variety of tissues.展开更多
基金supported by the grants from the 973 program(2011CB944304 and 2015CB943003)
文摘Non-obstructive azoospermia （NOA） is a severe defect in male reproductive health that occurs in 1% of adult men. In a previous study, we identified that rs7099208 is located within the last intron of FAM160B1 at 10q25.3. In this study, we analysed expression Quantitative Trait Loci （eQTL） of FAM16OB1, ABLIM1 and TRUB1, the three genes surrounding rs7099208. Only the expression level of FAM16OB1 was reduced for the homozygous alternate genotype （GG） of rs7099208, but not for the homozygous reference or heterozygous geno- types. FAM160B1 is predominantly expressed in human testes, where it is found in spermatocytes and round sper- matids. From 17 patients with NOA and five with obstructive azoospermia （OA）, immunohistochemistry revealed that expression of FAM160B1 is reduced, or undetectable in NOA patients, but not in OA cases or normal men. We conclude that rs7099208 is associated with NOA via a reduction in the expression of FAM160B1.
基金supported by the General Program of the National Natural Science Foundation of China(Grant No.31771397)the Beijing Nova Program(Grant No.20180059).
文摘Non-coding genomic variants constitute the majority of trait-associated genome variations;however,the identification of functional non-coding variants is still a challenge in human genetics,and a method for systematically assessing the impact of regulatory variants on gene expression and linking these regulatory variants to potential target genes is still lacking.Here,we introduce a deep neural network(DNN)-based computational framework,RegVar,which can accurately predict the tissue-specific impact of non-coding regulatory variants on target genes.We show that by robustly learning the genomic characteristics of massive variant-gene expression associations in a variety of human tissues,RegVar vastly surpasses all current non-coding variant prioritization methods in predicting regulatory variants under different circumstances.The unique features of RegVar make it an excellent framework for assessing the regulatory impact of any variant on its putative target genes in a variety of tissues.
