Adjusting for principal components can induce collider bias in genome-wide association studies.

Principal component analysis (PCA) is widely used to control for population structure in genome-wide association studies (GWAS). Top principal components (PCs) typically reflect population structure, but challenges arise in deciding how many PCs are needed and ensuring that PCs do not capture other artifacts such as regions with atypical linkage disequilibrium (LD). In response to the latter, many groups suggest performing LD pruning or excluding known high LD regions prior to PCA.

Adjusting for principal components can induce spurious associations in genome-wide association studies in admixed populations.

Principal component analysis (PCA) is widely used to control for population structure in genome-wide association studies (GWAS). Top principal components (PCs) typically reflect population structure, but challenges arise in deciding how many PCs are needed and ensuring that PCs do not capture other artifacts such as regions with atypical linkage disequilibrium (LD). In response to the latter, many groups suggest performing LD pruning or excluding known high LD regions prior to PCA.

Admixture mapping implicates 13q33.3 as ancestry-of-origin locus for Alzheimer disease in Hispanic and Latino populations.

Alzheimer disease (AD) is the most common form of senile dementia, with high incidence late in life in many populations including Caribbean Hispanic (CH) populations. Such admixed populations, descended from more than one ancestral population, can present challenges for genetic studies, including limited sample sizes and unique analytical constraints. Therefore, CH populations and other admixed populations have not been well represented in studies of AD, and much of the genetic variation contributing to AD risk in these populations remains unknown.

Genetic ancestry, differential gene expression, and survival in pediatric B-cell acute lymphoblastic leukemia.

Background: Black children have lower incidence yet worse survival than White and Latinx children with B-cell acute lymphoblastic leukemia (B-ALL). It is unclear how reported race/ethnicity (RRE) is associated with death in B-ALL after accounting for differentially expressed genes associated with genetic ancestry.

Methods: Using Phase 1 and 2 NCI TARGET B-ALL cases (N = 273; RRE-Black = 21, RRE-White = 162, RRE-Latinx = 69, RRE-Other = 9, RRE-Unknown = 12), we estimated proportions of African (AFR), European (EUR), and Amerindian (AMR) genetic ancestry.

Circular RNA Profiles in Viremia and ART Suppression Predict Competing circRNA-miRNA-mRNA Networks Exclusive to HIV-1 Viremic Patients.

Since the onset of the HIV-1/AIDS epidemic in 1981, 75 million people have been infected with the virus, and the disease remains a public health crisis worldwide. Circular RNAs (circRNAs) are derived from excised exons and introns during backsplicing, a form of alternative splicing. The relevance of unconventional, non-capped, and non-poly(A) transcripts to transcriptomics studies remains to be routinely investigated.

Coagulation factor VIII: Relationship to cardiovascular disease risk and whole genome sequence and epigenome-wide analysis in African Americans.

Background: Prospective studies have suggested higher factor VIII (FVIII) levels are an independent risk factor for coronary heart disease (CHD) and stroke. However, limited information, including on genetic and epigenetic contributors to FVIII variation, is available specifically among African Americans (AAs), who have higher FVIII levels than Europeans.

Objectives: We measured FVIII levels in ~3400 AAs from the community-based Jackson Heart Study and assessed genetic, epigenetic, and epidemiological correlates of FVIII, as well as incident cardiovascular disease (CVD) associations.

Genome-wide analysis of dental caries and periodontitis combining clinical and self-reported data.

Dental caries and periodontitis account for a vast burden of morbidity and healthcare spending, yet their genetic basis remains largely uncharacterized. Here, we identify self-reported dental disease proxies which have similar underlying genetic contributions to clinical disease measures and then combine these in a genome-wide association study meta-analysis, identifying 47 novel and conditionally-independent risk loci for dental caries. We show that the heritability of dental caries is enriched for conserved genomic regions and partially overlapping with a range of complex traits including smoking, education, personality traits and metabolic measures.

Genome-wide Significance Thresholds for Admixture Mapping Studies.

Admixture mapping studies have become more common in recent years, due in part to technological advances and growing international efforts to increase the diversity of genetic studies. However, many open questions remain about appropriate implementation of admixture mapping studies, including how best to control for multiple testing, particularly in the presence of population structure. In this study, we develop a theoretical framework to characterize the correlation of local ancestry and admixture mapping test statistics in admixed populations with contributions from any number of ancestral populations and arbitrary population structure.

A fully adjusted two-stage procedure for rank-normalization in genetic association studies.

When testing genotype-phenotype associations using linear regression, departure of the trait distribution from normality can impact both Type I error rate control and statistical power, with worse consequences for rarer variants. Because genotypes are expected to have small effects (if any) investigators now routinely use a two-stage method, in which they first regress the trait on covariates, obtain residuals, rank-normalize them, and then use the rank-normalized residuals in association analysis with the genotypes. Potential confounding signals are assumed to be removed at the first stage, so in practice, no further adjustment is done in the second stage.

Generalizing polygenic risk scores from Europeans to Hispanics/Latinos.

Polygenic risk scores (PRSs) are weighted sums of risk allele counts of single-nucleotide polymorphisms (SNPs) associated with a disease or trait. PRSs are typically constructed based on published results from Genome-Wide Association Studies (GWASs), and the majority of which has been performed in large populations of European ancestry (EA) individuals. Although many genotype-trait associations have generalized across populations, the optimal choice of SNPs and weights for PRSs may differ between populations due to different linkage disequilibrium (LD) and allele frequency patterns.

Illustrating, Quantifying, and Correcting for Bias in Post-hoc Analysis of Gene-Based Rare Variant Tests of Association.

To date, gene-based rare variant testing approaches have focused on aggregating information across sets of variants to maximize statistical power in identifying genes showing significant association with diseases. Beyond identifying genes that are associated with diseases, the identification of causal variant(s) in those genes and estimation of their effect is crucial for planning replication studies and characterizing the genetic architecture of the locus. However, we illustrate that straightforward single-marker association statistics can suffer from substantial bias introduced by conditioning on gene-based test significance, due to the phenomenon often referred to as "winner's curse.

A multistep approach to single nucleotide polymorphism-set analysis: an evaluation of power and type I error of gene-based tests of association after pathway-based association tests.

The aggregation of functionally associated variants given a priori biological information can aid in the discovery of rare variants associated with complex diseases. Many methods exist that aggregate rare variants into a set and compute a single value summarizing association between the set of rare variants and a phenotype of interest. These methods are often called gene-based, rare variant tests of association because the variants in the set are often all contained within the same gene.

A general method for combining different family-based rare-variant tests of association to improve power and robustness of a wide range of genetic architectures.

Current rare-variant, gene-based tests of association often suffer from a lack of statistical power to detect genotype-phenotype associations as a result of a lack of prior knowledge of genetic disease models combined with limited observations of extremely rare causal variants in population-based samples. The use of pedigree data, in which rare variants are often more highly concentrated than in population-based data, has been proposed as 1 possible method for enhancing power. Methods for combining multiple gene-based tests of association into a single summary value are a robust approach to different genetic architectures when little a priori knowledge is available about the underlying genetic disease model.

Local Ancestry Inference in a Large US-Based Hispanic/Latino Study: Hispanic Community Health Study/Study of Latinos (HCHS/SOL).

We estimated local ancestry on the autosomes and X chromosome in a large US-based study of 12,793 Hispanic/Latino individuals using the RFMix method, and we compared different reference panels and approaches to local ancestry estimation on the X chromosome by means of Mendelian inconsistency rates as a proxy for accuracy. We developed a novel and straightforward approach to performing ancestry-specific PCA after finding artifactual behavior in the results from an existing approach. Using the ancestry-specific PCA, we found significant population structure within African, European, and Amerindian ancestries in the Hispanic/Latino individuals in our study.

A general approach for combining diverse rare variant association tests provides improved robustness across a wider range of genetic architectures.

The widespread availability of genome sequencing data made possible by way of next-generation technologies has yielded a flood of different gene-based rare variant association tests. Most of these tests have been published because they have superior power for particular genetic architectures. However, for applied researchers it is challenging to know which test to choose in practice when little is known a priori about genetic architecture.