Scalable approaches for generating, validating and incorporating data from high-throughput functional assays to improve clinical variant classification.

As the adoption and scope of genetic testing continue to expand, interpreting the clinical significance of DNA sequence variants at scale remains a formidable challenge, with a high proportion classified as variants of uncertain significance (VUSs). Genetic testing laboratories have historically relied, in part, on functional data from academic literature to support variant classification. High-throughput functional assays or multiplex assays of variant effect (MAVEs), designed to assess the effects of DNA variants on protein stability and function, represent an important and increasingly available source of evidence for variant classification, but their potential is just beginning to be realized in clinical lab settings. Here, we describe a framework for generating, validating and incorporating data from MAVEs into a semi-quantitative variant classification method applied to clinical genetic testing. Using single-cell gene expression measurements, cellular evidence models were built to assess the effects of DNA variation in 44 genes of clinical interest. This framework was also applied to models for an additional 22 genes with previously published MAVE datasets. In total, modeling data was incorporated from 24 genes into our variant classification method. These data contributed evidence for classifying 4043 observed variants in over 57,000 individuals. Genetic testing laboratories are uniquely positioned to generate, analyze, validate, and incorporate evidence from high-throughput functional data and ultimately enable the use of these data to provide definitive clinical variant classifications for more patients.

Author Correction: Predicting the clinical impact of human mutation with deep neural networks.

In the version of this article originally published, the name of author Serafim Batzoglou was misspelled. The error has been corrected in the HTML and PDF versions of the article.

Predicting the clinical impact of human mutation with deep neural networks.

Millions of human genomes and exomes have been sequenced, but their clinical applications remain limited due to the difficulty of distinguishing disease-causing mutations from benign genetic variation. Here we demonstrate that common missense variants in other primate species are largely clinically benign in human, enabling pathogenic mutations to be systematically identified by the process of elimination. Using hundreds of thousands of common variants from population sequencing of six non-human primate species, we train a deep neural network that identifies pathogenic mutations in rare disease patients with 88% accuracy and enables the discovery of 14 new candidate genes in intellectual disability at genome-wide significance. Cataloging common variation from additional primate species would improve interpretation for millions of variants of uncertain significance, further advancing the clinical utility of human genome sequencing.

Gender preferences among antenatal women: a cross-sectional study from coastal South India.

BACKGROUND: A balanced sex ratio is essential for a stable society. OBJECTIVE: The main objective of the present research was to study the perceptions of women attending the antenatal care (ANC) facility regarding their gender preferences and family composition. METHOD: In this cross-sectional study 132 antenatal women were interviewed in their preferred language using a predesigned semi-structured questionnaire. The collected information was analyzed using SPSS version 11.5. RESULTS: The mean age of the study participants was 27.2 ± 4.1 years. The majority of the antenatal women (60.6%, n=80) did not have any gender preferences. Among those who had a gender preference (39.4%, n=52), male and female preference was reported by 55.7% (n=29) and 44.3% (n=23) of the participants respectively. The overall son preference index was observed to be 1.3. No consistent relationship could be established between the socio-demographic factors and the preference for gender. The mean preferred family size in our study was 1.85±0.531 and more than half of the participants had a balanced gender preference. The majority of the participants were aware that the adverse sex ratio can lead to fall in the number of brides and that it would bring about a social imbalance. CONCLUSION: As a developed society we need to ensure that both the genders get equal respect and are free from any sort of preferences and prejudices. To achieve this, more and more people need to be made aware of the consequences of gender imbalance and adverse sex ratio in a society.