不孕不育症困擾著大約15%的夫婦，其中很多夫婦不孕不育是因為機體存在缺陷的基因所致，但至今隱藏在不孕不育下的遺傳原因研究者並不清楚。近日來自康奈爾大學的科學家通過研究開發了一種新型的實驗性策略在人類機體中鑒別出了引發不孕不育的基因突變，這些突變被稱為單核苷酸多態性（SNPs），其是人類機體中常見的遺傳突變類型，每一個SNP都代表著單一DNA或核苷酸元件的差異。
研究者John Schimenti表示，如果我們闡明了SNP是有害的，隨後當病人進行基因組測序時就應該幫其確定哪些SNPs是應該存在的；如果我們知道哪些突變是好的，哪些是壞的，醫生們就會進行一定的遺傳診斷。當前鑒別引發疾病的SNPs的標準方法包括將健康個體和患病個體的基因組進行對比來縮小染色體位點，隨後利用計算機算法來預測哪些SNPs是有害的，但由於不孕不育是一個涉及許多基因的非常複雜的過程，因此並沒有方法可以確定引發不孕不育的基因。
文章中，研究者開發了一種新型策略，他們瀏覽了引發小鼠不育的所有基因，隨後利用計算機技術通過搜尋人類遺傳突變數據庫中的數據鑒別出了人類機體具有相同功能的SNPs；隨後研究者探究了引發人類不孕不育的4個基因的SNPs，利用名為CRISPR/Cas的基因編輯技術，研究者就對小鼠機體的同源基因進行編輯以便小鼠可以模擬人類機體的不育SNPs；通過在小鼠機體中分析研究人源化的SNPs，研究者就檢測了是否人類的這些突變會引發小鼠不育，在4種SNPs中研究者發現有一種SNP可以引發小鼠不育。
研究者預測，未來將會開發出新型的個體化療法來對個體進行基因組測序，從而幫助醫生和病人確定自身的遺傳健康；而鑒別出相應的不孕不育基因或許也會幫助臨床醫生開發新型治療不孕不育的療法，來給廣大不孕不育夫婦帶來福利。
doi:10.1073/PNAS.1506974112
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The genetics of human infertility by functional interrogation of SNPs in mice
Priti Singh and John C. Schimenti1
Infertility is a prevalent health issue, affecting ～15% of couples of childbearing age. Nearly one-half of idiopathic infertility cases are thought to have a genetic basis, but the underlying causes are largely unknown. Traditional methods for studying inheritance, such as genome-wide association studies and linkage analyses, have been confounded by the genetic and phenotypic complexity of reproductive processes. Here we describe an association- and linkage-free approach to identify segregating infertility alleles, in which CRISPR/Cas9 genome editing is used to model putatively deleterious nonsynonymous SNPs (nsSNPs) in the mouse orthologs of fertility genes. Mice bearing “humanized” alleles of four essential meiosis genes, each predicted to be deleterious by most of the commonly used algorithms for analyzing functional SNP consequences, were examined for fertility and reproductive defects. Only a Cdk2 allele mimicking SNP rs3087335, which alters an inhibitory WEE1 protein kinase phosphorylation site, caused infertility and revealed a novel function in regulating spermatogonial stem cell maintenance. Our data indicate that segregating infertility alleles exist in human populations. Furthermore, whereas computational prediction of SNP effects is useful for identifying candidate causal mutations for diverse diseases, this study underscores the need for in vivo functional evaluation of physiological consequences. This approach can revolutionize personalized reproductive genetics by establishing a permanent reference of benign vs. infertile alleles.
