Identification of gene-gene interaction using principal components

After more than 200 genome-wide association studies, there have been some successful identifications of a single novel locus. Thus, the identification of single-nucleotide polymorphisms (SNP) with interaction effects is of interest. Using the Genetic Analysis Workshop 16 data from the North American Rheumatoid Arthritis Consortium, we propose an approach to screen for SNP-SNP interaction using a two-stage method and an approach for detecting gene-gene interactions using principal components. We selected a set of 17 rheumatoid arthritis candidate genes to assess both approaches. Our approach using principal components holds promise in detecting gene-gene interactions. However, further study is needed to evaluate the power and the feasibility for a whole genome-wide association analysis using the principal components approach

Robust, flexible, and scalable tests for Hardy-Weinberg Equilibrium across diverse ancestries

Traditional Hardy-Weinberg equilibrium (HWE) tests (the χ2 test and the exact test) have long been used as a metric for evaluating genotype quality, as technical artifacts leading to incorrect genotype calls often can be identified as deviations from HWE. However, in datasets comprised of individuals from diverse ancestries, HWE can be violated even without genotyping error, complicating the use of HWE testing to assess genotype data quality. In this manuscript, we present the Robust Unified Test for HWE (RUTH) to test for HWE while accounting for population structure and genotype uncertainty, and evaluate the impact of population heterogeneity and genotype uncertainty on the standard HWE tests and alternative methods using simulated and real sequence datasets. Our results demonstrate that ignoring population structure or genotype uncertainty in HWE tests can inflate false positive rates by many orders of magnitude. Our evaluations demonstrate different tradeoffs between false positives and statistical power across the methods, with RUTH consistently amongst the best across all evaluations. RUTH is implemented as a practical and scalable software tool to rapidly perform HWE tests across millions of markers and hundreds of thousands of individuals while supporting standard VCF/BCF formats. RUTH is publicly available at https://www.github.com/statgen/ruth

Mutations in Ribonucleic Acid Binding Protein Gene Cause Familial Dilated Cardiomyopathy

ObjectivesWe sought to identify a novel gene for dilated cardiomyopathy (DCM).BackgroundDCM is a heritable, genetically heterogeneous disorder that remains idiopathic in the majority of patients. Familial cases provide an opportunity to discover unsuspected molecular bases of DCM, enabling pre-clinical risk detection.MethodsTwo large families with autosomal-dominant DCM were studied. Genome-wide linkage analysis was used to identify a disease locus, followed by fine mapping and positional candidate gene sequencing. Mutation scanning was then performed in 278 unrelated subjects with idiopathic DCM, prospectively identified at the Mayo Clinic.ResultsOverlapping loci for DCM were independently mapped to chromosome 10q25-q26. Deoxyribonucleic acid sequencing of affected individuals in each family revealed distinct heterozygous missense mutations in exon 9 of RBM20, encoding ribonucleic acid (RNA) binding motif protein 20. Comprehensive coding sequence analyses identified missense mutations clustered within this same exon in 6 additional DCM families. Mutations segregated with DCM (peak composite logarithm of the odds score >11.49), were absent in 480 control samples, and altered residues within a highly conserved arginine/serine (RS)-rich region. Expression of RBM20 messenger RNA was confirmed in human heart tissue.ConclusionsOur findings establish RBM20as a DCM gene and reveal a mutation hotspot in the RS domain. RBM20is preferentially expressed in the heart and encodes motifs prototypical of spliceosome proteins that regulate alternative pre-messenger RNA splicing, thus implicating a functionally distinct gene in human cardiomyopathy. RBM20mutations are associated with young age at diagnosis, end-stage heart failure, and high mortality

Pollen spectrum of honey of Apis mellifera L. and stingless bees (Hymenoptera: Apidae) from the semi-arid region of Bahia State, Brazil

Pollen in honey reflects its botanical origin and melissopalynology is used to identify origin, type, and quantities of pollen grains of the botanical species visited by bees. This study aimed to identify the pollen spectrum of honeys from Apis mellifera and stingless bees produced in the semi-arid region of Bahia, Brazil. We analysed 78 honey samples, which were submitted to the acetolysis process for identification and quantification of pollen types. Fabaceae, Asteraceae and Euphorbiaceae were the most predominant families in pollen types. For Fabaceae, the most representative pollen types were Chamaecrista 1, Mimosa caesalpiniifolia, Mimosa pudica, Mimosa tenuiflora, Prosopis and Senna. The results indicate that the flora explored by the bees to collect nectar is diverse in the semi-arid region of Bahia and the honeys analysed were classified as multifloral.info:eu-repo/semantics/publishedVersio

A Genomic Pathway Approach to a Complex Disease: Axon Guidance and Parkinson Disease

While major inroads have been made in identifying the genetic causes of rare Mendelian disorders, little progress has been made in the discovery of common gene variations that predispose to complex diseases. The single gene variants that have been shown to associate reproducibly with complex diseases typically have small effect sizes or attributable risks. However, the joint actions of common gene variants within pathways may play a major role in predisposing to complex diseases (the paradigm of complex genetics). The goal of this study was to determine whether polymorphism in a candidate pathway (axon guidance) predisposed to a complex disease (Parkinson disease [PD]). We mined a whole-genome association dataset and identified single nucleotide polymorphisms (SNPs) that were within axon-guidance pathway genes. We then constructed models of axon-guidance pathway SNPs that predicted three outcomes: PD susceptibility (odds ratio = 90.8, p = 4.64 × 10−38), survival free of PD (hazards ratio = 19.0, p = 5.43 × 10−48), and PD age at onset (R2 = 0.68, p = 1.68 × 10−51). By contrast, models constructed from thousands of random selections of genomic SNPs predicted the three PD outcomes poorly. Mining of a second whole-genome association dataset and mining of an expression profiling dataset also supported a role for many axon-guidance pathway genes in PD. These findings could have important implications regarding the pathogenesis of PD. This genomic pathway approach may also offer insights into other complex diseases such as Alzheimer disease, diabetes mellitus, nicotine and alcohol dependence, and several cancers

Assessment of genotype imputation methods

Several methods have been proposed to impute genotypes at untyped markers using observed genotypes and genetic data from a reference panel. We used the Genetic Analysis Workshop 16 rheumatoid arthritis case-control dataset to compare the performance of four of these imputation methods: IMPUTE, MACH, PLINK, and fastPHASE. We compared the methods' imputation error rates and performance of association tests using the imputed data, in the context of imputing completely untyped markers as well as imputing missing genotypes to combine two datasets genotyped at different sets of markers. As expected, all methods performed better for single-nucleotide polymorphisms (SNPs) in high linkage disequilibrium with genotyped SNPs. However, MACH and IMPUTE generated lower imputation error rates than fastPHASE and PLINK. Association tests based on allele "dosage" from MACH and tests based on the posterior probabilities from IMPUTE provided results closest to those based on complete data. However, in both situations, none of the imputation-based tests provide the same level of evidence of association as the complete data at SNPs strongly associated with disease

Genetics Analysis Workshop 20: Methods and Strategies for the New Frontiers of Epigenetics and Pharmacogenomics

GAW20 provided a platform for developing and evaluating statistical methods to analyze human lipid-related phenotypes, DNA methylation, and single-nucleotide markers in a study involving a pharmaceutical intervention. In this article, we present an overview of the data sets and the contributions analyzing these data. The data, donated by the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) investigators, included data from 188 families (N = 1105) which included genome-wide DNA methylation data before and after a 3-week treatment with fenofibrate, single-nucleotide polymorphisms, metabolic syndrome components before and after treatment, and a variety of covariates. The contributions from individual research groups were extensively discussed prior, during, and after the Workshop in groups based on discussion themes, before being submitted for publication

Evaluating gene by sex and age interactions on cardiovascular risk factors in Brazilian families

Background: In family studies, it is important to evaluate the impact of genes and environmental factors on traits of interest. In particular, the relative influences of both genes and the environment may vary in different strata of the population of interest, such as young and old individuals, or males and females. Methods: In this paper, extensions of the variance components model are used to evaluate heterogeneity in the genetic and environmental variance components due to the effects of sex and age (the cutoff between young and old was 43 yrs). The data analyzed were from 81 Brazilian families (1,675 individuals) of the Baependi Family Heart Study. Results: The models allowing for heterogeneity of variance components by sex suggest that genetic and environmental variances are not different in males and females for diastolic blood pressure, LDL-cholesterol, and HDL-cholesterol, independent of the covariates included in the models. However, for systolic blood pressure, fasting glucose and triglycerides, the evidence for heterogeneity was dependent on the covariates in the model. For instance, in the presence of sex and age covariates, heterogeneity in the genetic variance component was suggested for fasting glucose. But, for systolic blood pressure, there was no evidence of heterogeneity in any of the two variance components. Except for the LDL-cholesterol, models allowing for heterogeneity by age provide evidence of heterogeneity in genetic variance for triglycerides and systolic and diastolic blood pressure. There was evidence of heterogeneity in environmental variance in fasting glucose and HDL-cholesterol. Conclusions: Our results suggest that heterogeneity in trait variances should not be ignored in the design and analyses of gene-finding studies involving these traits, as it may generate additional information about gene effects, and allow the investigation of more sophisticated models such as the model including sex-specific oligogenic variance components

Heritability of cardiovascular risk factors in a Brazilian population: Baependi Heart Study

<p>Abstract</p> <p>Background</p> <p>The heritability of cardiovascular risk factors is expected to differ between populations because of the different distribution of environmental risk factors, as well as the genetic make-up of different human populations.</p> <p>Methods</p> <p>The purpose of this analysis was to evaluate genetic and environmental influences on cardiovascular risk factor traits, using a variance component approach, by estimating the heritability of these traits in a sample of 1,666 individuals in 81 families ascertained randomly from a highly admixed population of a city in a rural area in Brazil.</p> <p>Results</p> <p>Before adjustment for sex, age, age², and age × sex interaction, polygenic heritability of systolic (SBP) and diastolic (DBP) blood pressure were 15.0% and 16.4%, waist circumference 26.1%, triglycerides 25.7%, fasting glucose 32.8%, HDL-c 31.2%, total cholesterol 28.6%, LDL-c 26.3%, BMI 39.1%. Adjustment for covariates increased polygenic heritability estimates for all traits mainly systolic and diastolic blood pressure (25.9 and 26.2%, respectively), waist circumference (40.1%), and BMI (51.0%).</p> <p>Conclusion</p> <p>Heritability estimates for cardiovascular traits in the Brazilian population are high and not significantly different from other studied worldwide populations. Mapping efforts to identify genetic loci associated with variability of these traits are warranted.</p