Methods For Rare Variant Analysis Research Articles

A unified method for rare variant analysis of gene-environment interactions.

Advanced technology in whole-genome sequencing has offered the opportunity to comprehensively investigate the genetic contribution, particularly rare variants, to complex traits. Several region-based tests have been developed to jointly model the marginal effect of rare variants, but methods to detect gene-environment (GE) interactions are underdeveloped. Identifying the modification effects of environmental factors on genetic risk poses a considerable challenge. To tackle this challenge, we develop a method to detect GE interactions for rare variants using generalized linear mixed effect model. The proposed method can accommodate either binary or continuous traits in related or unrelated samples. Under this model, genetic main effects, GE interactions, and sample relatedness are modeled as random effects. We adopt a kernel-based method to leverage the joint information across rare variants and implement variance component score tests to reduce the computational burden. Our simulation studies of continuous and binary traits show that the proposed method maintains correct type I error rates and appropriate power under various scenarios, such as genotype main effects and GE interaction effects in opposite directions and varying the proportion of causal variants in the model. We apply our method in the Framingham Heart Study to test GE interaction of smoking on body mass index or overweight status and replicate the Cholinergic Receptor Nicotinic Beta 4 gene association reported in previous large consortium meta-analysis of single nucleotide polymorphism-smoking interaction. Our proposed set-based GE test is computationally efficient and is applicable to both binary and continuous phenotypes, while appropriately accounting for familial or cryptic relatedness.

Age-specific changes in genome-wide methylation enrich for Foxa2 and estrogen receptor alpha binding sites.

The role of DNA methylation patterns in complex phenotypes remains unclear. To explore this question, we adapted our methods for rare variant analysis to characterize genome-wide murine DNA hybridization array to investigate methylation at CpG islands, shores, and regulatory elements. We have applied this platform to compare age and tissue- specific methylation differences in the brain and spleen of young and aged mice. As expected from prior studies, there are clear global differences in organ-specific, but not age-specific, methylation due mostly to changes at repetitive elements. Surprisingly, out of 200,000 loci there were only 946 differentially methylated cytosines (DMCs) between young and old samples (529 hypermethylated, 417 hypomethylated in aged mice) compared to thousands of tissue-specific DMCs. Hypermethylated loci were clustered around the promoter region of Sfi1, exon 2 of Slc11a2, Drg1, Esr1 and Foxa2 transcription factor binding sites. In particular, there were 75 hypermethylated Foxa2 binding sites across a 2.7 Mb region of chromosome 11. Hypomethylated loci were clustered around Mid1, Isoc2b and genome-wide loci with binding sites for Foxa2 and Esr1, which are known to play important roles in development and aging. These data suggest discreet tissue-independent methylation changes associated with aging processes such as cell division (Sfi1, Mid1), energy production (Drg1, Isoc2b) and cell death (Foxa2, Esr1).

Knowledge-driven binning approach for rare variant association analysis: application to neuroimaging biomarkers in Alzheimer\u2019s disease

BackgroundRapid advancement of next generation sequencing technologies such as whole genome sequencing (WGS) has facilitated the search for genetic factors that influence disease risk in the field of human genetics. To identify rare variants associated with human diseases or traits, an efficient genome-wide binning approach is needed. In this study we developed a novel biological knowledge-based binning approach for rare-variant association analysis and then applied the approach to structural neuroimaging endophenotypes related to late-onset Alzheimer’s disease (LOAD).MethodsFor rare-variant analysis, we used the knowledge-driven binning approach implemented in Bin-KAT, an automated tool, that provides 1) binning/collapsing methods for multi-level variant aggregation with a flexible, biologically informed binning strategy and 2) an option of performing unified collapsing and statistical rare variant analyses in one tool. A total of 750 non-Hispanic Caucasian participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort who had both WGS data and magnetic resonance imaging (MRI) scans were used in this study. Mean bilateral cortical thickness of the entorhinal cortex extracted from MRI scans was used as an AD-related neuroimaging endophenotype. SKAT was used for a genome-wide gene- and region-based association analysis of rare variants (MAF (minor allele frequency) < 0.05) and potential confounding factors (age, gender, years of education, intracranial volume (ICV) and MRI field strength) for entorhinal cortex thickness were used as covariates. Significant associations were determined using FDR adjustment for multiple comparisons.ResultsOur knowledge-driven binning approach identified 16 functional exonic rare variants in FANCC significantly associated with entorhinal cortex thickness (FDR-corrected p-value < 0.05). In addition, the approach identified 7 evolutionary conserved regions, which were mapped to FAF1, RFX7, LYPLAL1 and GOLGA3, significantly associated with entorhinal cortex thickness (FDR-corrected p-value < 0.05). In further analysis, the functional exonic rare variants in FANCC were also significantly associated with hippocampal volume and cerebrospinal fluid (CSF) Aβ1–42 (p-value < 0.05).ConclusionsOur novel binning approach identified rare variants in FANCC as well as 7 evolutionary conserved regions significantly associated with a LOAD-related neuroimaging endophenotype. FANCC (fanconi anemia complementation group C) has been shown to modulate TLR and p38 MAPK-dependent expression of IL-1β in macrophages. Our results warrant further investigation in a larger independent cohort and demonstrate that the biological knowledge-driven binning approach is a powerful strategy to identify rare variants associated with AD and other complex disease.

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.

Genetic association of cyclic AMP signaling genes with bipolar disorder

The genetic basis for bipolar disorder (BPD) is complex with the involvement of multiple genes. As it is well established that cyclic adenosine monophosphate (cAMP) signaling regulates behavior, we tested variants in 29 genes that encode components of this signaling pathway for associations with BPD type I (BPD I) and BPD type II (BPD II). A total of 1172 individuals with BPD I, 516 individuals with BPD II and 1728 controls were analyzed. Single SNP (single-nucleotide polymorphism), haplotype and SNP × SNP interactions were examined for association with BPD. Several statistically significant single-SNP associations were observed between BPD I and variants in the PDE10A gene and between BPD II and variants in the DISC1 and GNAS genes. Haplotype analysis supported the conclusion that variation in these genes is associated with BPD. We followed-up PDE10A's association with BPD I by sequencing a 23-kb region in 30 subjects homozygous for seven minor allele risk SNPs and discovered eight additional rare variants (minor allele frequency <1%). These single-nucleotide variants were genotyped in 999 BPD cases and 801 controls. We obtained a significant association for these variants in the combined sample using multiple methods for rare variant analysis. After using newly developed methods to account for potential bias from sequencing BPD cases only, the results remained significant. In addition, SNP × SNP interaction studies suggested that variants in several cAMP signaling pathway genes interact to increase the risk of BPD. This report is among the first to use multiple rare variant analysis methods following common tagSNPs associations with BPD.

Multiple Regression Methods Show Great Potential for Rare Variant Association Tests

The investigation of associations between rare genetic variants and diseases or phenotypes has two goals. Firstly, the identification of which genes or genomic regions are associated, and secondly, discrimination of associated variants from background noise within each region. Over the last few years, many new methods have been developed which associate genomic regions with phenotypes. However, classical methods for high-dimensional data have received little attention. Here we investigate whether several classical statistical methods for high-dimensional data: ridge regression (RR), principal components regression (PCR), partial least squares regression (PLS), a sparse version of PLS (SPLS), and the LASSO are able to detect associations with rare genetic variants. These approaches have been extensively used in statistics to identify the true associations in data sets containing many predictor variables. Using genetic variants identified in three genes that were Sanger sequenced in 1998 individuals, we simulated continuous phenotypes under several different models, and we show that these feature selection and feature extraction methods can substantially outperform several popular methods for rare variant analysis. Furthermore, these approaches can identify which variants are contributing most to the model fit, and therefore both goals of rare variant analysis can be achieved simultaneously with the use of regression regularization methods. These methods are briefly illustrated with an analysis of adiponectin levels and variants in the ADIPOQ gene.

Common Variants in the Periostin Gene Influence Development of Atherosclerosis in Young Persons

We investigated the influence of genetic variants (rare and common) in the gene encoding periostin (POSTN) on atherosclerosis as measured in arterial specimens from the Pathobiological Determinants of Atherosclerosis in Youth (PDAY) study. A comprehensive survey of common POSTN variants (87 single-nucleotide polymorphisms [SNPs]) in PDAY subjects (n = 2527) identified numerous SNPs associated with raised lesions in abdominal aorta and with fatty streaks in thoracic aorta. These SNPs belonged to a small number of correlation bins that spanned the entire locus. To examine effects of rare variants, we resequenced POSTN functional regions in PDAY cases with raised lesions (n = 291) and controls with no raised lesions (n = 294). However, we found no significant associations with case-control status for carriers of POSTN rare variants using the weighted-sum method for rare variant analysis. We identified common variants in POSTN that are associated with arterial lesions in young persons from the PDAY study. This finding strongly supports a role for periostin in atherogenesis, as suggested by recent proteomics analysis that found abundant expression of periostin in atherosclerotic lesions. Genetic variation may influence atherosclerosis via periostin's known involvement in multiple relevant pathways, including angiogenesis, vascular remodeling, and stimulation of migration and differentiation of vascular smooth muscle cells.

A New Testing Strategy to Identify Rare Variants with Either Risk or Protective Effect on Disease

Rapid advances in sequencing technologies set the stage for the large-scale medical sequencing efforts to be performed in the near future, with the goal of assessing the importance of rare variants in complex diseases. The discovery of new disease susceptibility genes requires powerful statistical methods for rare variant analysis. The low frequency and the expected large number of such variants pose great difficulties for the analysis of these data. We propose here a robust and powerful testing strategy to study the role rare variants may play in affecting susceptibility to complex traits. The strategy is based on assessing whether rare variants in a genetic region collectively occur at significantly higher frequencies in cases compared with controls (or vice versa). A main feature of the proposed methodology is that, although it is an overall test assessing a possibly large number of rare variants simultaneously, the disease variants can be both protective and risk variants, with moderate decreases in statistical power when both types of variants are present. Using simulations, we show that this approach can be powerful under complex and general disease models, as well as in larger genetic regions where the proportion of disease susceptibility variants may be small. Comparisons with previously published tests on simulated data show that the proposed approach can have better power than the existing methods. An application to a recently published study on Type-1 Diabetes finds rare variants in gene IFIH1 to be protective against Type-1 Diabetes.