Identifying susceptibility genes for primary ovarian insufficiency on the high-risk genetic background of a fragile X premutation.

OBJECTIVE: To identify modifying genes that explains the risk of fragile X-associated primary ovarian insufficiency (FXPOI). DESIGN: Gene-based, case/control association study, followed by a functional screen of highly ranked genes using a Drosophila model. SETTING: Participants were recruited from academic and clinical settings. PATIENT(S): Women with a premutation (PM) who experienced FXPOI at the age of 35 years or younger (n = 63) and women with a PM who experienced menopause at the age of 50 years or older (n = 51) provided clinical information and a deoxyribonucleic acid sample for whole genome sequencing. The functional screen was on the basis of Drosophila TRiP lines. INTERVENTION(S): Clinical information and a DNA sample were collected for whole genome sequencing. MAIN OUTCOME MEASURES: A polygenic risk score derived from common variants associated with natural age at menopause was calculated and associated with the risk of FXPOI. Genes associated with the risk of FXPOI were identified on the basis of the P-value from gene-based association test and an altered level of fecundity when knocked down in the Drosophila PM model. RESULTS: The polygenic risk score on the basis of common variants associated with natural age at menopause explained approximately 8% of the variance in the risk of FXPOI. Further, SUMO1 and KRR1 were identified as possible modifying genes associated with the risk of FXPOI on the basis of an untargeted gene analysis of rare variants. CONCLUSIONS: In addition to the large genetic effect of a PM on ovarian function, the additive effects of common variants associated with natural age at menopause and the effect of rare modifying variants appear to play a role in FXPOI risk.

Identifying genetic factors that contribute to the increased risk of congenital heart defects in infants with Down syndrome.

Atrioventricular septal defects (AVSD) are a severe congenital heart defect present in individuals with Down syndrome (DS) at a > 2000-fold increased prevalence compared to the general population. This study aimed to identify risk-associated genes and pathways and to examine a potential polygenic contribution to AVSD in DS. We analyzed a total cohort of 702 individuals with DS with or without AVSD, with genomic data from whole exome sequencing, whole genome sequencing, and/or array-based imputation. We utilized sequence kernel association testing and polygenic risk score (PRS) methods to examine rare and common variants. Our findings suggest that the Notch pathway, particularly NOTCH4, as well as genes involved in the ciliome including CEP290 may play a role in AVSD in DS. These pathways have also been implicated in DS-associated AVSD in prior studies. A polygenic component for AVSD in DS has not been examined previously. Using weights based on the largest genome-wide association study of congenital heart defects available (2594 cases and 5159 controls; all general population samples), we found PRS to be associated with AVSD with odds ratios ranging from 1.2 to 1.3 per standard deviation increase in PRS and corresponding liability r2 values of approximately 1%, suggesting at least a small polygenic contribution to DS-associated AVSD. Future studies with larger sample sizes will improve identification and quantification of genetic contributions to AVSD in DS.

Bystro: rapid online variant annotation and natural-language filtering at whole-genome scale.

Accurately selecting relevant alleles in large sequencing experiments remains technically challenging. Bystro ( https://bystro.io/ ) is the first online, cloud-based application that makes variant annotation and filtering accessible to all researchers for terabyte-sized whole-genome experiments containing thousands of samples. Its key innovation is a general-purpose, natural-language search engine that enables users to identify and export alleles and samples of interest in milliseconds. The search engine dramatically simplifies complex filtering tasks that previously required programming experience or specialty command-line programs. Critically, Bystro's annotation and filtering capabilities are orders of magnitude faster than previous solutions, saving weeks of processing time for large experiments.