Sex-dependent placental methylation quantitative trait loci provide insight into the prenatal origins of childhood onset traits and conditions.

Molecular quantitative trait loci (QTLs) allow us to understand the biology captured in genome-wide association studies (GWASs). The placenta regulates fetal development and shows sex differences in DNA methylation. We therefore hypothesized that placental methylation QTL (mQTL) explain variation in genetic risk for childhood onset traits, and that effects differ by sex.

Risk and protection of different rare protein-coding variants of complement component C4A in age-related macular degeneration.

Age-related macular degeneration (AMD) is the leading cause of central vision loss in the elderly. One-third of the genetic contribution to this disease remains unexplained. We analyzed targeted sequencing data from two independent cohorts (4,245 cases, 1,668 controls) which included genomic regions of known AMD loci in 49 genes.

Regional gray matter oligodendrocyte- and myelin-related measures are associated with differential susceptibility to stress-induced behavior in rats and humans.

Individual reactions to traumatic stress vary dramatically, yet the biological basis of this variation remains poorly understood. Recent studies demonstrate the surprising plasticity of oligodendrocytes and myelin with stress and experience, providing a potential mechanism by which trauma induces aberrant structural and functional changes in the adult brain. In this study, we utilized a translational approach to test the hypothesis that gray matter oligodendrocytes contribute to traumatic-stress-induced behavioral variation in both rats and humans.

Cascading epigenomic analysis for identifying disease genes from the regulatory landscape of GWAS variants.

The majority of genetic variants detected in genome wide association studies (GWAS) exert their effects on phenotypes through gene regulation. Motivated by this observation, we propose a multi-omic integration method that models the cascading effects of genetic variants from epigenome to transcriptome and eventually to the phenome in identifying target genes influenced by risk alleles. This cascading epigenomic analysis for GWAS, which we refer to as CEWAS, comprises two types of models: one for linking cis genetic effects to epigenomic variation and another for linking cis epigenomic variation to gene expression.