Genetic Variants In Genome-wide Association Studies Research Articles

Estimating the overall fraction of phenotypic variance attributed to high-dimensional predictors measured with error.

In prospective genomic studies (e.g., DNA methylation, metagenomics, and transcriptomics), it is crucial to estimate the overall fraction of phenotypic variance (OFPV) attributed to the high-dimensional genomic variables, a concept similar to heritability analyses in genome-wide association studies (GWAS). Unlike genetic variants in GWAS, these genomic variables are typically measured with error due to technical limitation and temporal instability. While the existing methods developed for GWAS can be used, ignoring measurement error may severely underestimate OFPV and mislead the design of future studies. Assuming that measurement error variances are distributed similarly between causal and noncausal variables, we show that the asymptotic attenuation factor equals to the average intraclass correlation coefficients of all genomic variables, which can be estimated based on a pilot study with repeated measurements. We illustrate the method by estimating the contribution of microbiome taxa to body mass index and multiple allergy traits in the American Gut Project. Finally, we show that measurement error does not cause meaningful bias when estimating the correlation of effect sizes for two traits.

Genetic interaction study of Alzheimer's disease quantitative biomarkers: A polygenic risk score analysis and evaluation.

Relationship between genetic factors and pathological features of Alzheimer's disease (AD) may be studied with biomarker, using both polygenic risk scores (PRSs), as well as individual genetic variants in genome-wide association studies (GWAS). In the Swedish BioFINDER study, we used a priori PRS for AD based on findings in recent GWAS, and AD related biomarkers in cerebrospinal fluid (CSF) in cognitively unimpaired individuals (n = 751), Mild Cognitive Impairment (n = 212), and AD (n = 150) patients. AD related biomarkers were rank-based inverse normal transformed to be used as dependent variables in linear regression models adjusted for age, gender, education, APOE ε4, ε2 count and significant principal components. We also tested individual genetic variants in GWAS for each biomarker. Analyses were performed in the total sample, and after stratification on MMSE results. The PRS was associated with higher CSF P-tau 181 (p ≤1.2e-05) and T-tau (p ≤ 8.14e-05) and lower CSF Aβ42/40 (p ≤ 0.006) and Aβ42 (p ≤ 0.04) (Figure 1, 2). Gene Enrichment of PRS 5 genes [containing 1850 genetic variants mapped to 1607 genes] for Tau biomarker showed 13 Gene Ontology (GO) Biological Process (BP) terms at p-value < e-03 ("Dendrite Morphogenesis": top hit; p-value ≤ 9.20e-06) and 16 KEGG pathway term enriched for genes of PRS 5 ("Phosphatidylinositol signaling system": top hit; p-value ≤ 5.5e-06) (Figure 3, 4). Gene enrichment of PRS 7 [containing 62 genetic variants mapped to 58 genes] for Aβ biomarker returned 12 GO terms ("Integrin-Mediated Signaling Pathway": top hit; p-value ≤1.20e-03) and 1 term enriched for KEGG pathway (Hematopoietic cell lineage). In our predefined list of genes interacting with MAPT (22 genes) and APP (69 genes) we found 3 genes from MAPT and APP set that were involved in PRS 5 and PRS 7 respectively. We also found 9 genes from APP set that was involved in PRS 5. Elevated levels of AD related biomarkers are associated with polygenic risk scores in AD. These findings further strengthens the link between genetic and biomarker disease predictors and indicate a potential role for these markers in disease prediction and patient stratification in AD.

Prioritizing genetic variants in GWAS with lasso using permutation-assisted tuning.

Large scale genome-wide association studies (GWAS) have resulted in the identification of a wide range of genetic variants related to a host of complex traits and disorders. Despite their success, the individual single-nucleotide polymorphism (SNP) analysis approach adopted in most current GWAS can be limited in that it is usually biologically simple to elucidate a comprehensive genetic architecture of phenotypes and statistically underpowered due to heavy multiple-testing correction burden. On the other hand, multiple-SNP analyses (e.g. gene-based or region-based SNP-set analysis) are usually more powerful to examine the joint effects of a set of SNPs on the phenotype of interest. However, current multiple-SNP approaches can only draw an overall conclusion at the SNP-set level and does not directly inform which SNPs in the SNP-set are driving the overall genotype-phenotype association. In this article, we propose a new permutation-assisted tuning procedure in lasso (plasso) to identify phenotype-associated SNPs in a joint multiple-SNP regression model in GWAS. The tuning parameter of lasso determines the amount of shrinkage and is essential to the performance of variable selection. In the proposed plasso procedure, we first generate permutations as pseudo-SNPs that are not associated with the phenotype. Then, the lasso tuning parameter is delicately chosen to separate true signal SNPs and non-informative pseudo-SNPs. We illustrate plasso using simulations to demonstrate its superior performance over existing methods, and application of plasso to a real GWAS dataset gains new additional insights into the genetic control of complex traits. R codes to implement the proposed methodology is available at https://github.com/xyz5074/plasso. Supplementary data are available at Bioinformatics online.

A Penalized Regression Approach for Integrative Analysis in Gen ome- Wide Association Studies

Over one thousand genome-wide association studies (GWAS) have been conducted in the past decade. Increasing biological evidence suggests the polygenic genetic architecture of complex traits: a complex trait is affected by many risk variants with small or moderate effects jointly. Meanwhile, recent progress in GWAS suggests that complex human traits may share common genetic bases, which is known as “pleiotropy”. To further improve statistical power of detecting risk genetic variants in GWAS, we propose a penalized regression method to analyze the GWAS dataset of primary interest by incorporating information from other related GWAS. The proposed method does not require the individual-level of genotype and phenotype data from other related GWAS, making it useful when only summary statistics are available. The key idea of the proposed approach is that related traits may share common genetic basis. Specifically, we propose a linear model for integrative analysis of multiple GWAS, in which risk genetic variants can be detected via identification of nonzero coefficients. Due to the pleiotropy effect, there exist genetic variants affecting multiple traits, which correspond to a consistent nonzero pattern of coefficients across multiple GWAS. To achieve this, we use a group Lasso penalty to identify this nonzero pattern in our model, and then develop an efficient algorithm based on the proximal gradient method. Simulation studies showed that the proposed approach had satisfactory performance. We applied the proposed method to analyze a body mass index (BMI) GWAS dataset from a European American (EA) population and achieved improvement over single GWAS analysis.