Genetic Epidemiology最新文献

Genetic risk scores (GRS) are crucial tools for estimating an individual's genetic liability to various traits and diseases, computed as a weighted sum of trait-associated allele counts. Traditionally, GRS models assume additive, linear effects of risk variants. However, complex traits often involve nonadditive interactions, such as epistasis, which are not captured by these conventional methods. In this study, we investigate the use of random forest (RF) models as a model-free approach for constructing GRS, leveraging RF's capacity to capture complex, nonlinear interactions among genetic variants. Specifically, we introduce two new RF-based GRS strategies to boost RF performance and to incorporate base data information if available, including (1) ctRF, which optimizes linkage disequilibrium (LD) clumping and p-value thresholds within RF; and (2) wRF, which adjusts the chance of SNP inclusion in tree nodes based on their association strength. Through simulation studies and real data applications of Alzheimer's disease, body mass index, and atopy, we find that ctRF consistently outperforms other RF-based methods and classical additive models when traits exhibit complex genetic architectures. Additionally, incorporating informative base data into RF-GRS construction can enhance predictive accuracy. Our findings suggest that RF-based GRS can effectively capture intricate genetic interactions, and offer a robust alternative to traditional GRS methods, especially for complex traits with nonlinear genetic effects.

Inflation in genome-wide association studies (GWAS) summary statistics represents a major challenge, for which correction methods have been developed. These include the genomic control (GC) method, which uses the λ-value to correct summary statistics, and the linkage disequilibrium score regression (LDSR) method, which uses the LDSR intercept. By using type 2 diabetes (T2D) as an exemplar, we explore factors influencing λ-values and the impact of these corrections on association signals. We find that larger sample sizes increase λ-values due to increased captured polygenicity, while including lower frequency variants decreases λ-values due to reduced power. Comparing T2D genetic associations described in overlapping GWAS meta-analyses of increasing sample size, we find that GC correction reduces the false positive rate and leads to the loss of robust associations. In one of the largest meta-analysis, GC correction results in 39.7% loss of independent loci, substantially reducing the number of detected associations. In comparison, the LDSR intercept correction leads to a loss of up to 25.2% of the independent loci, being therefore less conservative than the GC correction. We conclude that in large, well-powered GWAS meta-analysis of polygenic traits, both GC and LDSR intercept correction leads to power loss, highlighting the need for improved genomic inflation correction methods.

Genetic correlation analysis can provide useful insight into the shared genetic basis between traits or conditions of interest. However, most genome-wide analyses only inform about the degree of global (overall) genetic similarity and do not identify the specific genomic regions that give rise to this similarity. Identification of the key genomic regions contributing to shared genetic correlation between traits could allow the genes in these regions to be prioritised for investigation of potential shared biological mechanisms. In recent years, several statistical tools (e.g. LAVA, ρ-HESS, SUPERGNOVA and LOGODetect) have been developed to investigate local (in contrast to global) genetic correlation. These tools partition the genome into multiple segments and provide estimates of the genetic correlation captured by each individual segment. We applied these tools to publicly available European ancestry genome-wide association study (GWAS) summary statistics for three pairs of commonly occurring conditions: hypertension with atrial fibrillation and flutter, hypertension with chronic kidney disease, and hypertension with type 2 diabetes. Despite each of the methods aiming to address the same question, the results were found to be inconsistent across tools, with some identified regions overlapping and others implicated only by a single tool. Computer simulations using genetic data from UK Biobank, carried out under known generating conditions, suggest that LAVA and, to a lesser extent, ρ-HESS, provide the most reliable identification of genuine shared genetic factors. A newly-developed tool, HDL-L, also performed highly competitively. Here we highlight the similarities and differences between the results obtained from these methods and discuss some potential reasons underlying these differences.

For complex traits such as lung disease in Cystic Fibrosis (CF), Gene x Gene or Gene x Environment interactions can impact disease severity but these remain largely unknown. Unaccounted-for genetic interactions introduce a distributional shift in the quantitative trait across the genotypic groups. Joint location and scale tests, or full distributional differences across genotype groups can account for unknown genetic interactions and increase power for gene identification compared with the conventional association test. Here we propose a new joint location and scale test (JLS), a quantile regression-basd JLS (qJLS), that addresses previous limitations. Specifically, qJLS is free of distributional assumptions, thus applies to non-Gaussian traits; is as powerful as the existing JLS tests under Gaussian traits; and is computationally efficient for genome-wide association studies (GWAS). Our simulation studies, which model unknown genetic interactions, demonstrate that qJLS is robust to skewed and heavy-tailed error distributions and is as powerful as other JLS tests in the literature under normality. Without any unknown genetic interaction, qJLS shows a large increase in power with non-Gaussian traits over conventional association tests and is slightly less powerful under normality. We apply the qJLS method to the Canadian CF Gene Modifier Study (n = 1,997) and identified a genome-wide significant variant, rs9513900 on chromosome 13, that had not previously been reported to contribute to CF lung disease. qJLS provides a powerful alternative to conventional genetic association tests, where interactions may contribute to a quantitative trait.