Genetic Epidemiology最新文献

Genetic selection occurs at different stages before a successful birth. The genetic makeup of a couple may influence the likelihood of needing assisted reproductive technology (ART) to achieve conception. However, frequent early fetal losses may also be perceived as reduced couple fertility and may thus be a contributing factor to the need for ART treatment. As ART procedures may enhance early fetal survival, genes that impact fetal viability may have a different allele distribution in ART offspring than expected under Mendelian transmission, as well as compared with the general population. With genetic data available from the Norwegian Mother, Father, and Child Cohort Study, we defined fetal survival as the study outcome and analyzed 1336 case-parent triads and dyads where the offspring were conceived by ART. Using log-linear models implemented in the R package Haplin, we conducted genome-wide scans to estimate fetal, maternal, and parent-of-origin effects and provided a detailed discussion on how these effects are estimated and interpreted. We detected fetal effects for single-nucleotide polymorphisms (SNPs) located in CXXC4-AS1, OPCML, and DYNLRB2-AS1. Since these effects were not observed in a limited follow-up analysis of non-ART triads, the identified effects are unlikely caused by genetic selection before fertilization.

Conventional genome-wide association studies (GWAS) are designed to assess the effect of a genetic locus on phenotypic mean by genotype. Such loci explain a proportion of phenotypic variance known as narrow-sense heritability. In contrast, variance quantitative trait loci (vQTL) are associated with the phenotypic variance by genotype. These loci explain an additional proportion of phenotypic variance and contribute to broad-sense heritability but not to narrow-sense heritability. Here, a genome-wide vQTL analysis in 22,805 African Americans yielded eight loci for body mass index (BMI). Of these loci, three were replicated in 6002 sub-Saharan Africans. No locus reached genome-wide significance using the standard additive model. Furthermore, no locus showed evidence for natural selection, haplotype effects, or gene × sex or gene × study interactions. Two loci showed evidence for an effect of locus-specific ancestry resulting from admixture and for a gene × gene interaction. One locus showed evidence for interaction with diastolic blood pressure, consistent with this vQTL capturing an unmodeled gene × covariate interaction. These analyses demonstrate that relevant BMI loci can be detected by evaluating vQTL and that these loci contribute to the underexplored broad-sense heritability for this trait.

DNA methylation (DNAm) is a chemical modification of DNA that can be influenced by various factors, including age, the environment, and lifestyle. An epigenetic clock is a predictive tool that measures biological age based on DNAm levels. It can provide insights into an individual's biological age, which may differ from their chronological age. This difference, known as the epigenetic age acceleration, may reflect health status and the risk for age-related diseases. Moreover, epigenetic clocks are used in studies of aging to assess the effectiveness of antiaging interventions and to understand the underlying mechanisms of aging and disease. Various epigenetic clocks have been developed using samples from different populations, tissues, and cell types, typically by training high-dimensional linear regression models with an elastic net penalty. While these models can predict mean biological age based on DNAm with high precision, there is a lack of uncertainty quantification which is important for interpreting the precision of age estimations and for clinical decision-making. To understand the distribution of a biological age clock beyond its mean, we propose a general pipeline for training epigenetic clocks, based on an integration of high-dimensional quantile regression and conformal prediction, to effectively reveal population heterogeneity and construct prediction intervals. Our approach produces adaptive prediction intervals not only achieving nominal coverage but also accounting for the inherent variability across individuals. By using the data collected from 728 blood samples in 11 DNAm data sets from children, we find that our quantile regression-based prediction intervals are narrower than those derived from conventional mean regression-based epigenetic clocks. This observation demonstrates an improved statistical efficiency over the existing pipeline for training epigenetic clocks. In addition, the resulting intervals have a synchronized varying pattern to age acceleration, effectively revealing cellular evolutionary heterogeneity in age patterns in different developmental stages during individual childhoods and adolescent cohort. Our findings suggest that conformalized high-dimensional quantile regression can produce valid prediction intervals and uncover underlying population heterogeneity. Although our methodology focuses on the distribution of measures of biological aging in children, it is applicable to a broader range of age groups to improve understanding of epigenetic age beyond the mean. This inference-based toolbox could provide valuable insights for future applications of epigenetic interventions for age-related diseases.

Childhood asthma is more common among children whose mothers have asthma than among those whose fathers have asthma. The reasons for this are unknown, and we hypothesize that genomic imprinting may partly explain this observation. Our aim is to assess parent-of-origin (PoO) effects on childhood asthma by analyzing SNP array genotype data from a large population-based cohort. To estimate PoO effects in parent-reported childhood asthma at 7 years of age, we fit a log-linear model implemented in the HAPLIN R package to SNP array genotype data from 915 mother–father–child case triads, 603 mother–child case dyads, and 113 father–child case dyads participating in the Norwegian Mother, Father, and Child Cohort Study (MoBa). We found that alleles at two SNPs—rs3003214 and rs3003211—near the adenylosuccinate synthase 2 gene (ADSS2 on chromosome 1q44) showed significant PoO effects at a false positive rate ≤ 0.05. The ratio of the effect of the maternally and paternally inherited G-allele at rs3003214 was 1.68 (95% CI: 1.41–2.03, p value = 1.13E−08). Our results suggest PoO effects at the ADSS2 gene, particularly the maternally inherited G-allele at rs3003214, may contribute to the maternal effect in childhood asthma.

A large proportion of genetic variations involved in complex diseases are rare and located within noncoding regions, making the interpretation of underlying biological mechanisms a daunting task. Although technical and methodological progress has been made to annotate the genome, current disease-rare-variant association tests incorporating such annotations suffer from two major limitations. First, they are generally restricted to case−control designs of unrelated individuals, which often require tens or hundreds of thousands of individuals to achieve sufficient power. Second, they were not evaluated with region-based annotations needed to interpret the causal regulatory mechanisms. In this work, we propose RetroFun-RVS, a new retrospective family-based score test, incorporating functional annotations. A critical feature of the proposed method is to aggregate genotypes to compare against rare variant-sharing expectations among affected family members. Through extensive simulations, we have demonstrated that RetroFun-RVS integrating networks based on 3D genome contacts as functional annotations reach greater power over the region-wide test, other strategies to include subregions and competing methods. Also, the proposed framework shows robustness to non-informative annotations, maintaining its power when causal variants are spread across regions. Asymptotic p-values are susceptible to Type I error inflation when the number of families with rare variants is small, and a bootstrap procedure is recommended in these instances. Application of RetroFun-RVS is illustrated on whole genome sequence in the Eastern Quebec Schizophrenia and Bipolar Disorder Kindred Study with networks constructed from 3D contacts and epigenetic data on neurons. In summary, the integration of functional annotations corresponding to regions or networks with transcriptional impacts in rare variant tests appears promising to highlight regulatory mechanisms involved in complex diseases.