Approximately 10% of people in the U.S. suffer from infertility, about half of whom are thought to have a genetic basis. However, the underlying causes remain undetermined in the great majority of patients. Traditional methods for identifying inherited disease loci, such as GWAS, have been confounded by heterogeneity of infertility phenotypes and the large numbers of genes involved in reproduction. Nevertheless, there are probably numerous ?infertility? alleles segregating in populations, affecting diverse processes at all stages of gamete development. Our goal is to identify these alleles, their nature, and their in vivo impacts to reproduction. Under previous funding, we used a radically different approach to the problem that involved prediction and modeling of human coding variants biochemically and in mice. Here, we propose to employ innovative strategies for identifying and characterizing infertility variants (particularly SNPs) that segregate as minor alleles in populations. A multidisciplinary team with expertise in high-throughput genomics, reproductive genetics, proteomics, computational biology, and transcriptional regulation has been assembled to identify both protein-coding and regulatory variants affecting ?fertility? genes.
The Specific Aims are to: 1) Use computational approaches and high-throughput in vitro assays to identify nonsynonymous SNPs in human reproduction genes that are likely to disrupt protein function. These alleles will be precisely modeled in mice using CRISPR/Cas9 genome editing, and thoroughly phenotyped to inform patient diagnosis. 2) Exploit subfertile mouse models of human variants, exhibiting decreased chiasmata, to understand mechanisms of premature ovarian insufficiency (POI) and recurrent pregnancy loss. 3) Identify human germ cell regulatory variants via indentification of active (eRNA-transcribing) enhancers using ChRO-seq technology, followed by mouse transgenic assays. We will also identify eQTL residing in gametogenesis promoters by exploiting GTEx, high-throughput vector-building technology, and expression assays. Successful execution of this project would constitute the most comprehensive study ever conducted to identify and validate both coding and non-coding genetic variants in human populations that contribute to infertility in both sexes. Since the variants are carried by millions of people collectively, this project can have a major and lasting impact on the field of reproductive genetics in the precision genomics era.
The goal of this proposal is to identify genetic variants in human populations that cause infertility and related conditions, including primary ovarian insufficiency, subfertility, and recurrent pregnancy loss. A combination of computational methods, cutting edge genomic assays, and genetically-engineered mouse models will be used to identify these variants that affect both protein coding parts of genes and also regulatory elements. The results on all tested variants will be posted online as they are obtained, providing crucial information for genetic diagnosis of infertility patients for generations to come.
| Meyer, Michael J; Beltrán, Juan Felipe; Liang, Siqi et al. (2018) Interactome INSIDER: a structural interactome browser for genomic studies. Nat Methods 15:107-114 |
| Chen, Siwei; Fragoza, Robert; Klei, Lambertus et al. (2018) An interactome perturbation framework prioritizes damaging missense mutations for developmental disorders. Nat Genet 50:1032-1040 |
| Guo, Yu; Alexander, Katherine; Clark, Andrew G et al. (2016) Integrated network analysis reveals distinct regulatory roles of transcription factors and microRNAs. RNA 22:1663-1672 |
