Causal inference in integrative genomics: exploring de novo mutations and unravelling causal mechanisms via gene regulatory networks in developmental process
type: CreativeWork
description: Integrative genomics has revolutionised our understanding of complex biological processes by synergistically combining data from various omics technologies, including genomics, transcriptomics, and epigenomics. In this study, we first focused on integrating genomics and transcriptomics data to perform causal inference on DNMs occurring in the germline. These germline DNMs have long been implicated as significant contributors to developmental and genetic disorders. Previous investigations of the genetic factors influencing DNMs have been hindered by limited statistical power, primarily due to the challenge of obtaining an adequate number of parent-offspring trios. However, leveraging the rare disease cohort from the UK's 100,000 Genomes Project (100kGP), which comprises over 10,000 trios, provided an unprecedented opportunity to delve into the genetics of germline mutations. Here, we estimated Single Nucleotide Polymorphism (SNP) heritability of DNM count in offspring, which measures the relative contribution of genetic factors to the variance of the trait, in a Principal Component Analysis (PCA)-selected subset of the 100kGP cohort. To gain deeper insights, we separately estimated SNP heritabilities for paternally and maternally transmitted mutations, determined based on parentally phased DNMs in offspring. This estimation was performed using parental genetic variants at various minor allele frequencies and through employing diverse methodologies. Here, although we were not able to find statistically significant evidence for the non-zero heritability of the DNM counts, we were able to explore how the heritability might be sensitive to the structure of the population and to confirm the increased significance of environmental factors in the context of causality of DNM. We further investigated the partitioning of heritability among genes with different expression profiles in different tissues or cells and found a relative heritability enrichment for genes expressed in …
abstract: Integrative genomics has revolutionised our understanding of complex biological processes by synergistically combining data from various omics technologies, including genomics, transcriptomics, and epigenomics. In this study, we first focused on integrating genomics and transcriptomics data to perform causal inference on DNMs occurring in the germline. These germline DNMs have long been implicated as significant contributors to developmental and genetic disorders. Previous investigations of the genetic factors influencing DNMs have been hindered by limited statistical power, primarily due to the challenge of obtaining an adequate number of parent-offspring trios. However, leveraging the rare disease cohort from the UK's 100,000 Genomes Project (100kGP), which comprises over 10,000 trios, provided an unprecedented opportunity to delve into the genetics of germline mutations. Here, we estimated Single Nucleotide Polymorphism (SNP) heritability of DNM count in offspring, which measures the relative contribution of genetic factors to the variance of the trait, in a Principal Component Analysis (PCA)-selected subset of the 100kGP cohort. To gain deeper insights, we separately estimated SNP heritabilities for paternally and maternally transmitted mutations, determined based on parentally phased DNMs in offspring. This estimation was performed using parental genetic variants at various minor allele frequencies and through employing diverse methodologies. Here, although we were not able to find statistically significant evidence for the non-zero heritability of the DNM counts, we were able to explore how the heritability might be sensitive to the structure of the population and to confirm the increased significance of environmental factors in the context of causality of DNM. We further investigated the partitioning of heritability among genes with different expression profiles in different tissues or cells and found a relative heritability enrichment for genes expressed in …
author: Hwang, Seongwon
language: eng
keywords: whole genome sequencing, de novo mutation, statistical genetics, causal inference, graph neural network, 100K genomes project, heritability estimation, Mendelian randomization, node ablation
license: https://www.repository.cam.ac.uk/bitstreams/0ca37725-6fb6-49f7-ab91-e31bb6448f78/download
license: http://purl.org/NET/rdflicense/allrightsreserved
date published: 2024-03-28
date modified: 2026-07-24T01:33:08Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.115039