Published in Genetic epidemiology
Testing millions of single nucleotide polymorphisms (SNPs) in genetic association studies has become a standard routine for disease gene discovery. In light of recent re-evaluation of statistical practice, it has been suggested that p-values are unfit as summaries of statistical evidence. Despite this criticism, p-values contain information that ca...
Published in Genetic epidemiology
The methodology of transcriptome-wide association studies (TWAS) has become popular in integrating a reference expression quantitative trait (eQTL) data set with an independent main GWAS data set to identify (putatively) causal genes, shedding mechanistic insights to biological pathways from genetic variants to a GWAS trait mediated by gene express...
Published in Genetic epidemiology
Logistic regression is the primary analysis tool for binary traits in genome-wide association studies (GWAS). Multinomial regression extends logistic regression to multiple categories. However, many phenotypes more naturally take ordered, discrete values. Examples include (a) subtypes defined from multiple sources of clinical information and (b) de...
Published in Genetic epidemiology
Hypertension is a common disease worldwide. Alcohol consumption is one of the risk factors for hypertension, however, it is unclear how alcohol consumption elevates blood pressure. Blood pressure could be affected by interactions between genetic variations and alcohol consumption. Thus, we performed a genome-wide interaction study (GWIS) to assess ...
Published in Genetic epidemiology
Testing the association between single-nucleotide polymorphism (SNP) effects and a response is often carried out through kernel machine methods based on least squares, such as the sequence kernel association test (SKAT). However, these least-squares procedures are designed for a normally distributed conditional response, which may not apply. Other ...
Published in Genetic epidemiology
Traditionally, in normal case-control studies of disorder A, the controls are defined as those not developing the disorder. However, in genome wide association (GWA) studies, controls are sometimes (a) unscreened or (b) screened for both disorder A and disorder B, producing super-normal controls. Using simulations, we examine how the observed genet...
Published in Genetic epidemiology
Mendelian randomization (MR) study has become a powerful approach to assess the potential causal effect of a risk exposure on an outcome. Most current MR studies are conducted under the two-sample setting by combining summary data from two separate genome-wide association studies (GWAS), with one providing measures on associations between genetic m...
Published in Genetic epidemiology
Genome-wide association studies (GWAS) have successfully identified many genetic variants associated with complex traits. However, GWAS experience power issues, resulting in the failure to detect certain associated variants. Additionally, GWAS are often unable to parse the biological mechanisms of driving associations. An existing gene-based associ...
Published in Genetic epidemiology
Individual sequencing studies often have limited sample sizes and so limited power to detect trait associations with rare variants. A common strategy is to aggregate data from multiple studies. For studying rare variants, jointly calling all samples together is the gold standard strategy but can be difficult to implement due to privacy restrictions...
Published in Genetic epidemiology
Admixed populations arise when two or more previously isolated populations interbreed. Admixture mapping (AM) methods are used for tracing the ancestral origin of disease-susceptibility genetic loci in the admixed population such as African American and Latinos. AM is different from genome-wide association studies in that ancestry rather than genot...
