대표연구 논문 실적
A framework to infer de novo exonic variants when parental genotypes are missing enhances association studies of autism
발행년도
20260504
저자
Haeun Moon, Laura Sloofman, Marina Natividad Avila, Lambertus Klei, Bernie Devlin, Joseph D. Buxbaum, Kathryn Roeder
저널
BIOINFORMATICS
Author
admsnulamp
Date
2026-05-20
Views
76
Abstract
Motivation Gene-damaging mutations are highly informative for studies seeking to discover genes underlying developmental disorders. Traditionally, these de novo variants are recognized by evaluating high-quality DNA sequence from affected offspring and parents. However, when parental sequence is unavailable, methods are required to infer de novo status and use this inference for association studies.Results We use data from autism spectrum disorder to illustrate and evaluate methods. Separating de novo from rare inherited variants is challenging because the latter are far more common. Using a classifier for unbalanced data and variants of known inheritance class, we build an inheritance model and then a de novo score for variants when parental data are missing. Next, we propose a new Random Draw (RD) model to use this score for gene discovery. Built into an existing inferential framework, RD produces a more powerful gene-based association test and controls the false discovery rate.Availability and implementation Codes are available at Github (https://github.com/HaeunM/TADA-RD) and Zenodo.
DOI: https://doi.org/10.5281/zenodo.18531769
Motivation Gene-damaging mutations are highly informative for studies seeking to discover genes underlying developmental disorders. Traditionally, these de novo variants are recognized by evaluating high-quality DNA sequence from affected offspring and parents. However, when parental sequence is unavailable, methods are required to infer de novo status and use this inference for association studies.Results We use data from autism spectrum disorder to illustrate and evaluate methods. Separating de novo from rare inherited variants is challenging because the latter are far more common. Using a classifier for unbalanced data and variants of known inheritance class, we build an inheritance model and then a de novo score for variants when parental data are missing. Next, we propose a new Random Draw (RD) model to use this score for gene discovery. Built into an existing inferential framework, RD produces a more powerful gene-based association test and controls the false discovery rate.Availability and implementation Codes are available at Github (https://github.com/HaeunM/TADA-RD) and Zenodo.
DOI: https://doi.org/10.5281/zenodo.18531769
