Association Of Rare Genetic Variants Research Articles

BinomiRare: A robust test for association of a rare genetic variant with a binary outcome for mixed models and any case-control proportion.

SummaryWhole-genome sequencing (WGS) and whole-exome sequencing studies have become increasingly available and are being used to identify rare genetic variants associated with health and disease outcomes. Investigators routinely use mixed models to account for genetic relatedness or other clustering variables (e.g., family or household) when testing genetic associations. However, no existing tests of the association of a rare variant with a binary outcome in the presence of correlated data control the type 1 error where there are (1) few individuals harboring the rare allele, (2) a small proportion of cases relative to controls, and (3) covariates to adjust for. Here, we address all three issues in developing a framework for testing rare variant association with a binary trait in individuals harboring at least one risk allele. In this framework, we estimate outcome probabilities under the null hypothesis and then use them, within the individuals with at least one risk allele, to test variant associations. We extend the BinomiRare test, which was previously proposed for independent observations, and develop the Conway-Maxwell-Poisson (CMP) test and study their properties in simulations. We show that the BinomiRare test always controls the type 1 error, while the CMP test sometimes does not. We then use the BinomiRare test to test the association of rare genetic variants in target genes with small-vessel disease (SVD) stroke, short sleep, and venous thromboembolism (VTE), in whole-genome sequence data from the Trans-Omics for Precision Medicine (TOPMed) program.

Association of Rare Genetic Variants and Early-Onset Atrial Fibrillation in Ethnic Minority Individuals

Although rare variants in cardiac ion channels, transcription factors, and myocardial structural proteins are associated with early-onset atrial fibrillation (AF) in White individuals of European descent, it remains unclear whether genetic variation also contributes to the cause of AF in those of minority ethnicity. To assess the prevalence of rare and novel pathogenic variants in candidate genes in ethnic minority probands with early-onset AF and determine genotype-phenotype associations. In this cohort, family-based study, probands of African and Hispanic descent with early-onset AF (defined as AF occurring in individuals aged ≤66 years) prospectively enrolled in a clinical and genetic biorepository underwent sequencing of 60 candidate genes. Recruitment took place from July 1, 2015, to June 30, 2019. Data were analyzed from February 1 to February 28, 2020. Rare and novel variants categorized as pathogenic or likely pathogenic. The prevalence of rare and novel pathogenic variants in African American and Hispanic/Latinx probands with early-onset AF and genotype-phenotype associations. Among 227 probands with early-onset AF, mean (SD) age at onset of AF was 51.0 (9.9) years, 132 probands (58.1%) were men, 148 (65.2%) were African American, and 79 (34.8%) were Hispanic/Latinx. A family history of AF was verified in 24 probands with early-onset AF (10.6%). Sequencing 60 candidate genes identified 53 (23 rare and 30 novel) variants with 16 of the 227 (7.0%) probands harboring likely pathogenic (43.8%) or pathogenic (56.2%) variants, with most loss-of-function variants in TTN, the gene encoding the sarcomeric protein titin (46.7%). In 6 families with more than 2 affected members, variants of unknown significance in sodium channel (SCN10A), potassium channel (KCNE5), sarcomeric proteins (MYH6 and TTN), and atrial natriuretic peptide (NPPA) cosegregated with AF. In this study, likely pathogenic and pathogenic variants were identified, with most loss-of-function variants in TTN, that increase susceptibility to early-onset AF in African American and Hispanic/Latinx individuals. These findings provide further understanding toward molecular phenotyping of AF and suggest novel mechanism-based therapeutic approaches for this common arrhythmia in ethnic minority groups.

Rare variants association testing for a binary outcome when pooling individual level data from heterogeneous studies.

Whole genome sequencing (WGS) and whole exome sequencing studies are used to test the association of rare genetic variants with health traits. Many existing WGS efforts now aggregate data from heterogeneous groups, for example, combining sets of individuals of European and African ancestries. We here investigate the statistical implications on rare variant association testing with a binary trait when combining together heterogeneous studies, defined as studies with potentially different disease proportion and different frequency of variant carriers. We study and compare in simulations the Type 1 error control and power of the naïve score test, the saddlepoint approximation to the score test, and the BinomiRare test in a range of settings, focusing on low numbers of variant carriers. We show that Type 1 error control and power patterns depend on both the number of carriers of the rare allele and on disease prevalence in each of the studies. We develop recommendations for association analysis of rare genetic variants. (1) The Score test is preferred when the case proportion in the sample is 50%. (2) Do not down-sample controls to balance case-control ratio, because it reduces power. Rather, use a test that controls the Type 1 error. (3) Conduct stratified analysis in parallel with combined analysis. Aggregated testing may have lower power when the variant effect size differs between strata.

Testing association of rare genetic variants with resistance to three common antiseizure medications.

Drug resistance is a major concern in the treatment of individuals with epilepsy. No genetic markers for resistance to individual antiseizure medication (ASM) have yet been identified. We aimed to identify the role of rare genetic variants in drug resistance for three common ASMs: levetiracetam (LEV), lamotrigine (LTG), and valproic acid (VPA). A cohort of 1622 individuals of European descent with epilepsy was deeply phenotyped and underwent whole exome sequencing (WES), comprising 575 taking LEV, 826 LTG, and 782 VPA. We performed gene- and gene set-based collapsing analyses comparing responders and nonresponders to the three drugs to determine the burden of different categories of rare genetic variants. We observed a marginally significant enrichment of rare missense, truncating, and splice region variants in individuals who were resistant to VPA compared to VPA responders for genes involved in VPA pharmacokinetics. We also found a borderline significant enrichment of truncating and splice region variants in the synaptic vesicle glycoprotein (SV2) gene family in nonresponders compared to responders to LEV. We did not see any significant enrichment using a gene-based approach. In our pharmacogenetic study, we identified a slightly increased burden of damaging variants in gene groups related to drug kinetics or targeting in individuals presenting with drug resistance to VPA or LEV. Such variants could thus determine a genetic contribution to drug resistance.

Association of Rare Genetic Variants in Opioid Receptors with Tourette Syndrome

Association of Rare Genetic Variants in Opioid Receptors with Tourette Syndrome

Association of Rare Genetic Variants in Opioid Receptors with Tourette Syndrome.

BackgroundGenes involved in Tourette syndrome (TS) remain largely unknown. We aimed to identify genetic factors contributing to TS in a French cohort of 120 individuals using a combination of hypothesis-driven and exome-sequencing approaches.MethodsWe first sequenced exons of SLITRK1-6 and HDC in the TS cohort and subsequently sequenced the exome of 12 individuals harboring rare variants in these genes to find additional rare variants contributing to the disorder under the hypothesis of oligogenic inheritance. We further screened three candidate genes (OPRK1, PCDH10, and NTSR2) preferentially expressed in the basal ganglia, and three additional genes involved in neurotensin and opioid signaling (OPRM1, NTS, and NTSR1), and compared variant frequencies in TS patients and 788 matched control individuals. We also investigated the impact of altering the expression of Oprk1 in zebrafish.ResultsThirteen ultrarare missense variants of SLITRK1-6 and HDC were identified in 12 patients. Exome sequencing in these patients revealed rare possibly deleterious variants in 3,041 genes, 54 of which were preferentially expressed in the basal ganglia. Comparison of variant frequencies altering selected candidate genes in TS and control individuals revealed an excess of potentially disrupting variants in OPRK1, encoding the opioid kappa receptor, in TS patients. Accordingly, we show that downregulation of the Oprk1 orthologue in zebrafish induces a hyperkinetic phenotype in early development.DiscussionThese results support a heterogeneous and complex genetic etiology of TS, possibly involving rare variants altering the opioid pathway in some individuals, which could represent a novel therapeutic target in this disorder.

Sequence Kernel Association Test for Quantitative Traits in Family Samples

A large number of rare genetic variants have been discovered with the development in sequencing technology and the lowering of sequencing costs. Rare variant analysis may help identify novel genes associated with diseases and quantitative traits, adding to our knowledge of explaining heritability of these phenotypes. Many statistical methods for rare variant analysis have been developed in recent years, but some of them require the strong assumption that all rare variants in the analysis share the same direction of effect, and others requiring permutation to calculate the P-values are computer intensive. Among these methods, the sequence kernel association test (SKAT) is a powerful method under many different scenarios. It does not require any assumption on the directionality of effects, and statistical significance is computed analytically. In this paper, we extend SKAT to be applicable to family data. The family-based SKAT (famSKAT) has a different test statistic and null distribution compared to SKAT, but is equivalent to SKAT when there is no familial correlation. Our simulation studies show that SKAT has inflated type I error if familial correlation is inappropriately ignored, but has appropriate type I error if applied to a single individual per family to obtain an unrelated subset. In contrast, famSKAT has the correct type I error when analyzing correlated observations, and it has higher power than competing methods in many different scenarios. We illustrate our approach to analyze the association of rare genetic variants using glycemic traits from the Framingham Heart Study.

A gene-based approach for testing association of rare alleles

Rare genetic variants have been shown to be important to the susceptibility of common human diseases. Methods for detecting association of rare genetic variants are drawing much attention. In this report, we applied a gene-based approach to the 200 simulated data sets of unrelated individuals. The test can detect the association of some genes with multiple rare variants.

Three Ways of Combining Genotyping and Resequencing in Case-Control Association Studies

We describe three statistical results that we have found to be useful in case-control genetic association testing. All three involve combining the discovery of novel genetic variants, usually by sequencing, with genotyping methods that recognize previously discovered variants. We first consider expanding the list of known variants by concentrating variant-discovery in cases. Although the naive inclusion of cases-only sequencing data would create a bias, we show that some sequencing data may be retained, even if controls are not sequenced. Furthermore, for alleles of intermediate frequency, cases-only sequencing with bias-correction entails little if any loss of power, compared to dividing the same sequencing effort among cases and controls. Secondly, we investigate more strongly focused variant discovery to obtain a greater enrichment for disease-related variants. We show how case status, family history, and marker sharing enrich the discovery set by increments that are multiplicative with penetrance, enabling the preferential discovery of high-penetrance variants. A third result applies when sequencing is the primary means of counting alleles in both cases and controls, but a supplementary pooled genotyping sample is used to identify the variants that are very rare. We show that this raises no validity issues, and we evaluate a less expensive and more adaptive approach to judging rarity, based on group-specific variants. We demonstrate the important and unusual caveat that this method requires equal sample sizes for validity. These three results can be used to more efficiently detect the association of rare genetic variants with disease.

Two-stage case–control designs for rare genetic variants

The search for the association of rare genetic variants with common diseases is of high interest, yet challenging because of cost considerations. We present an efficient two-stage design that uses diseased cases to first screen for rare variants at stage-1. If too few cases are found to carry any variants, the study stops. Otherwise, the selected variants are screened at stage-2 in a larger set of cases and controls, and the frequency of variants is compared between cases and controls by an exact test that corrects for the stage-1 ascertainment. Simulations show that our new method provides conservative Type-I error rates, similar to the conservative aspect of Fisher's exact test. We show that the probability of stopping at stage-1 increases with a smaller number of cases screened at stage-1, a larger stage-1 continuation threshold, or a smaller carrier probability. Our simulations also show how these factors impact the power at stage-2. To balance stopping early when there are few variant carriers versus continuation to stage-2 when the variants have a reasonable effect size on the phenotype, we provide guidance on designing an optimal study that minimizes the expected sample size when the null hypothesis is true, yet achieves the desired power.

Changing the engines of change: Natural resource conservation in the era of social media

<h3>Abstract</h3> Whole genome and exome sequencing studies are used to test the association of rare genetic variants with health traits. Many existing WGS efforts now aggregate data from heterogeneous groups, e.g. combining sets of individuals of European and African ancestries. We here investigate the statistical implications on rare variant association testing with a binary trait when combining together heterogeneous studies, defined as studies with potentially different disease proportion and different frequency of variant carriers. We study and compare in simulations the type 1 error control and power of the naïve Score test, the saddlepoint approximation to the score test (SPA test), and the BinomiRare test in a range of settings, focusing on low numbers of variant carriers. We show that type 1 error control and power patterns depend on both the number of carriers of the rare allele and on disease prevalence in each of the studies. We develop recommendations for association analysis of rare genetic variants. (1) The Score test is preferred when the case proportion in the sample is 50%. (2) Do not down-sample controls to balance case-control ratio, because it reduces power. Rather, use a test that controls the type 1 error. (3) Conduct stratified analysis in parallel with combined analysis. Aggregated testing may have lower power when the variant effect size differs between strata.