Genetic Epidemiology最新文献

For genetic association analysis based on multiple SNP regression of genotypes obtained by dense DNA sequencing or array data imputation, multi-collinearity can be a severe issue causing failure to fit the regression model. In this study, we propose a method of Dimension Reduction using Local Principal Components (DRLPC) which aims to resolve multi-collinearity by removing SNPs under the assumption that the remaining SNPs can capture the effect of a removed SNP due to high linear dependency. This approach to dimension reduction is expected to improve the power of regression-based statistical tests. We apply DRLPC to chromosome 22 SNPs of two data sets, the 1000 Genomes Project (phase 3) and the Canadian Longitudinal Study on Aging (CLSA), and calculate variance inflation factors (VIF) in various SNP-sets before and after implementing DRLPC as a metric of collinearity. Notably, DRLPC addresses multi-collinearity by excluding variables with a VIF exceeding a predetermined threshold (VIF = 20), thereby improving applicability for subsequent regression analyses. The number of variables in a final set for regression analysis is reduced to around 20% on average for larger-sized genes, whereas for smaller ones, the proportion is around 48%; suggesting that DRLPC is particularly effective for larger genes. We also compare the power of several multi-SNP statistics constructed for gene-specific analysis to evaluate power gains achieved by DRLPC. In simulation studies based on 100 genes with ≤ 500 SNPs per gene, DRLPC increases the power of the multiple regression Wald test from 60% to around 80%.

Major depressive disorder (MDD) is prevalent worldwide, substantially and negatively impacting both the quality and length of life of 280 million people globally. The genetic risk factors of MDD have been studied in various previous research, but the findings lack consistency. Sex/gender and racial/ethnic disparities have been reported; however, many previous genetic studies, represented by large-scale genome-wide association studies (GWASs) are known to lack diversity in the study cohorts. All of Us is a biorepository aiming to focus on the historically underrepresented groups. We perform GWASs for the MDD phenotype, using over 200,000 participants' genotypes and carry out sex- and racial/ethnic-specific subgroup studies. We identified a risk locus (chr6:151945242) in Estrogen Receptor Alpha Gene (ESR1) (p = $��\; ��1.70�\; �\times �\; �{10}^{�-�\; �9�}��$), and further confirmed the genetic association is sex-specific. The single-nucleotide polymorphism (SNP) chr6:151945242 was significant only in the male group, but not in the female group. These findings were replicated in the UK Biobank and echo with existing studies on the ESR1 gene and depressive disorders. Our results indicate that the All of Us program is a reliable resource for GWAS, as well as shedding light on further investigation of sex- and racial/ethnic-specific genome association, especially in underrepresented groups of the US population.

The polygenic score (PGS) holds promise for motivating preventive behavioral changes. However, no clinically validated standardization methodology currently exists. Here, we demonstrate the efficacy of a “reference-based” approach for standardization. This method uses the PGS distribution in the general population as a reference for normalization and percentile determination; however, it has not been validated. We investigated three potential influences on PGS computation: (1) the size of the reference population, (2) biases associated with different genotyping platforms, and (3) inclusion of kinship ties within the reference group. Our results indicate that the reference size affects the bootstrap estimate of standard error for PGS percentiles, peaking around the 50th percentile and diminishing at extreme percentiles (1st or 100th). Discrepancies between genotyping platforms, such as different microarrays and whole-genome sequencing, resulted in deviations in PGS (p < 0.05 in Kolmogorov–Smirnov test). However, these deviations were reduced to a nonsignificant level using shared genetic variants in the calculations when the ancestry of the samples and reference were matched. This approach recovered approximately 9.6% of the positive predictive value of PGS by naïve genotype. Our results provide fundamental insights for establishing clinical guidelines for implementing PGS to communicate reliable risks to individuals.

Gene−environment interactions have been observed for childhood asthma, however few have been assessed in ethnically diverse populations. Thus, we examined how polygenic risk score (PRS) modifies the association between ambient air pollution exposure (nitrogen dioxide [NO₂], ozone, particulate matter < 2.5 and < 10 μm) and childhood asthma incidence in a diverse cohort. Participants (n = 1794) were drawn from the Southern California Children's Health Study, a multi-wave prospective cohort followed from 4th to 12th grade. PRS was developed using single nucleotide polymorphisms previously associated with childhood asthma. PRS−asthma associations and PRS−air pollutant interactions were estimated using Poisson regression. An interquartile range PRS increase was associated with 36% (95% CI: 9%, 70%) higher asthma incidence among non-Hispanic children, but not associated with asthma among Hispanic children (rate ratio: 0.81 [95% CI: 0.62, 1.04]). NO₂−PRS interaction was borderline significant in the overall sample (coefficient: 0.23 [95% CI: −0.03, 0.49]). Limited evidence was observed for a positive interaction between PRS and NO₂ exposure associated with asthma incidence; however, the literature-based PRS was not associated with asthma among Hispanic participants. Equitable, diverse genetic sampling approaches are needed to better identify clinically relevant SNPs in this population.

Genotype–phenotype association studies have advanced our understanding of complex traits but often overlook sex-specific genetic signals. The growing awareness of sex-specific influences on human traits and diseases necessitates tailored statistical methodologies to dissect these genetic intricacies. Rare genetic variants play a significant role in disease development, often exhibiting stronger per-allele effects than common variants. In sex-dimorphic analysis, traits are viewed as having two sex-specific subsets rather than being uniformly defined. Existing methods for gene-based analysis of rare variants across multiple traits can identify shared genetic signals but cannot reveal the specific subsets from which significant signals originate. This means that when a significant signal is detected, it remains unclear whether it arises from the male samples, female samples, or both. To address this limitation, we propose SubsetRV, a new methodology capable of identifying genes associated with specific traits or diseases in males, females, or both. SubsetRV can also be applied to broader applications in multiple traits analysis. Simulation studies have demonstrated SubsetRV's reliability, and real data analysis on bipolar disorder and schizophrenia has revealed potential sex-specific genetic signals. SubsetRV offers a valuable tool for identifying sex-specific genetic candidates, aiding in understanding disease mechanisms. An R package for SubsetRV is available on GitHub. It can be accessed directly through this link: https://github.com/Mansouri-S/SubsetRV.

In this article, we proposed a new method named fused mixed graphical model (FMGM), which can infer network structures associated with dichotomous phenotypes. FMGM is based on a pairwise Markov random field model, and statistical analyses including the proposed method were conducted to find biological markers and underlying network structures of the atopic dermatitis (AD) from multiomics data of 6-month-old infants. The performance of FMGM was evaluated with simulations by using synthetic datasets of power-law networks, showing that FMGM had superior performance for identifying the differences of the networks compared to the separate inference with the previous method, causalMGM (F1-scores 0.550 vs. 0.730). Furthermore, FMGM was applied to identify multiomics profiles associated with AD, and significance association was found for the correlation between carotenoid biosynthesis and RNA degradation, suggesting the importance of metabolism related to oxidative stress and microbial RNA balance. R codes can be accessed as an R package “fusedMGM,” and an example data set and a script for analyses can be found at http://figshare.com/articles/dataset/FMGM_synthetic_data_example_zip/20509113.

Integrating multi-omics data may help researchers understand the genetic underpinnings of complex traits and diseases. However, the best ways to integrate multi-omics data and use them to address pressing scientific questions remain a challenge. One important and topical problem is how to assess the aggregate effect of multiple genomic data types (e.g. genotypes and gene expression levels) on a phenotype, particularly while accommodating routine issues, such as having related subjects' data in analyses. In this paper, we extend an existing composite kernel machine regression model to integrate two multi-omics data types, while accommodating for general correlation structures amongst outcomes. Due to the kernel machine regression framework, our methods allow for the integration of high-dimensional omics data with small, nonlinear, and interactive effects, and accommodation of general study designs. Here, we focus on scientific questions that aim to assess the association between a functional grouping (such as a gene or a pathway) and a quantitative trait of interest. We use a kernel machine regression to integrate the two multi-omics data types, as they may relate to the trait, and perform a global test of association. We demonstrate the advantage of this approach over single data type association tests via simulation. Finally, we apply this method to a large, multi-ethnic data set to investigate how predicted gene expression and rare genetic variation may be related to two platelet traits.