# Identification and Functional Validation of Human Infertility Alleles

> **NIH NIH R01** · CORNELL UNIVERSITY · 2022 · $608,466

## Abstract

Abstract
 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.

## Key facts

- **NIH application ID:** 10385752
- **Project number:** 5R01HD082568-08
- **Recipient organization:** CORNELL UNIVERSITY
- **Principal Investigator:** John C Schimenti
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $608,466
- **Award type:** 5
- **Project period:** 2015-09-01 → 2025-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10385752

## Citation

> US National Institutes of Health, RePORTER application 10385752, Identification and Functional Validation of Human Infertility Alleles (5R01HD082568-08). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10385752. Licensed CC0.

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