scholarly journals Using Gene Genealogies to Localize Rare Variants Associated with Complex Traits in Diploid Populations

2018 ◽  
Vol 83 (1) ◽  
pp. 30-39 ◽  
Author(s):  
Charith B. Karunarathna ◽  
Jinko Graham
Keyword(s):  
Complex Traits ◽  
Rare Variants ◽  
Diploid Populations

scholarly journals Using gene genealogies to localize rare variants associated with complex traits in diploid populations

2017 ◽  
Author(s):  
Charith B. Karunarathna ◽  
Jinko Graham
Keyword(s):  
Complex Traits ◽  
Rare Variants ◽  
Causal Variant ◽  
Mantel Test ◽  
Diploid Population ◽  
Exact Test ◽  
Causal Variants ◽  
Genomic Regions ◽  
Intermediate Performance ◽  
Diploid Populations

AbstractBackground and AimsMany methods can detect trait association with causal variants in candidate genomic regions; however, a comparison of their ability to localize causal variants is lacking. We extend a previous study of the detection abilities of these methods to a comparison of their localization abilities.MethodsThrough coalescent simulation, we compare several popular association methods. Cases and controls are sampled from a diploid population to mimic human studies. As benchmarks for comparison, we include two methods that cluster phenotypes on the true genealogical trees, a naive Mantel test considered previously in haploid populations and an extension that takes into account whether case haplotypes carry a causal variant. We first work through a simulated dataset to illustrate the methods. We then perform a simulation study to score the localization and detection properties.ResultsIn our simulations, the association signal was localized least precisely by the naive Mantel test and most precisely by its extension. Most other approaches had intermediate performance similar to the single-variant Fisher’s-exact test.ConclusionsOur results confirm earlier findings in haploid populations about potential gains in performance from genealogy-based approaches. They also highlight differences between haploid and diploid populations when localizing and detecting causal variants.

scholarly journals A Bayesian linear mixed model for prediction of complex traits

2020 ◽  
Author(s):  
Yang Hai ◽  
Yalu Wen
Keyword(s):  
Complex Traits ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Rare Variants ◽  
Disease Risk ◽  
R Package ◽  
Underlying Disease ◽  
Supplementary Information ◽  
True Effect Size ◽  
Bayes Algorithm

Abstract Motivation Accurate disease risk prediction is essential for precision medicine. Existing models either assume that diseases are caused by groups of predictors with small-to-moderate effects or a few isolated predictors with large effects. Their performance can be sensitive to the underlying disease mechanisms, which are usually unknown in advance. Results We developed a Bayesian linear mixed model (BLMM), where genetic effects were modelled using a hybrid of the sparsity regression and linear mixed model with multiple random effects. The parameters in BLMM were inferred through a computationally efficient variational Bayes algorithm. The proposed method can resemble the shape of the true effect size distributions, captures the predictive effects from both common and rare variants, and is robust against various disease models. Through extensive simulations and the application to a whole-genome sequencing dataset obtained from the Alzheimer’s Disease Neuroimaging Initiatives, we have demonstrated that BLMM has better prediction performance than existing methods and can detect variables and/or genetic regions that are predictive. Availability The R-package is available at https://github.com/yhai943/BLMM Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Exome Sequencing to Detect Rare Variants Associated With General Cognitive Ability: A Pilot Study

2015 ◽  
Vol 18 (2) ◽  
pp. 117-125 ◽  
Author(s):  
Michelle Luciano ◽  
Victoria Svinti ◽  
Archie Campbell ◽  
Riccardo E. Marioni ◽  
Caroline Hayward ◽  
...  
Keyword(s):  
Cognitive Ability ◽  
Exome Sequencing ◽  
Genetic Variants ◽  
Complex Traits ◽  
Rare Variants ◽  
Copy Number Variant ◽  
Apical Part ◽  
General Cognitive Ability ◽  
Sequencing Studies ◽  
Rare Genetic Variants

Variation in human cognitive ability is of consequence to a large number of health and social outcomes and is substantially heritable. Genetic linkage, genome-wide association, and copy number variant studies have investigated the contribution of genetic variation to individual differences in normal cognitive ability, but little research has considered the role of rare genetic variants. Exome sequencing studies have already met with success in discovering novel trait-gene associations for other complex traits. Here, we use exome sequencing to investigate the effects of rare variants on general cognitive ability. Unrelated Scottish individuals were selected for high scores on a general component of intelligence (g). The frequency of rare genetic variants (in n = 146) was compared with those from Scottish controls (total n = 486) who scored in the lower to middle range of the g distribution or on a proxy measure of g. Biological pathway analysis highlighted enrichment of the mitochondrial inner membrane component and apical part of cell gene ontology terms. Global burden analysis showed a greater total number of rare variants carried by high g cases versus controls, which is inconsistent with a mutation load hypothesis whereby mutations negatively affect g. The general finding of greater non-synonymous (vs. synonymous) variant effects is in line with evolutionary hypotheses for g. Given that this first sequencing study of high g was small, promising results were found, suggesting that the study of rare variants in larger samples would be worthwhile.

scholarly journals Using Gene Genealogies to Detect Rare Variants Associated with Complex Traits

2014 ◽  
Vol 78 (3-4) ◽  
pp. 117-130 ◽  
Author(s):  
Kelly M. Burkett ◽  
Brad McNeney ◽  
Jinko Graham ◽  
Celia M.T. Greenwood
Keyword(s):  
Complex Traits ◽  
Rare Variants

scholarly journals RAREsim: A simulation method for very rare genetic variants

2021 ◽  
Author(s):  
Megan Null ◽  
Josée Dupuis ◽  
Christopher R. Gignoux ◽  
Audrey E. Hendricks
Keyword(s):  
Rare Variant ◽  
Complex Traits ◽  
Rare Variants ◽  
Simulated Data ◽  
Real Data ◽  
Simulation Method ◽  
Sequencing Data ◽  
Variant Annotation ◽  
Causal Variants ◽  
Rare Genetic Variants

AbstractIdentification of rare variant associations is crucial to fully characterize the genetic architecture of complex traits and diseases. Essential in this process is the evaluation of novel methods in simulated data that mirrors the distribution of rare variants and haplotype structure in real data. Additionally, importing real variant annotation enables in silico comparison of methods that focus on putative causal variants, such as rare variant association tests, and polygenic scoring methods. Existing simulation methods are either unable to employ real variant annotation or severely under- or over-estimate the number of singletons and doubletons reducing the ability to generalize simulation results to real studies. We present RAREsim, a flexible and accurate rare variant simulation algorithm. Using parameters and haplotypes derived from real sequencing data, RAREsim efficiently simulates the expected variant distribution and enables real variant annotations. We highlight RAREsim’s utility across various genetic regions, sample sizes, ancestries, and variant classes.

scholarly journals The future of genomics for developmentalists

2013 ◽  
Vol 25 (4pt2) ◽  
pp. 1263-1278 ◽  
Author(s):  
Robert Plomin ◽  
Michael A. Simpson
Keyword(s):  
Complex Traits ◽  
Rare Variants ◽  
Behavioral Development ◽  
Genomic Research ◽  
Whole Genome Sequence ◽  
Whole Genome ◽  
Risk And Resilience ◽  
Polygenic Scores ◽  
Dna Variants ◽  
The Future

AbstractThe momentum of genomic science will carry it far into the future and into the heart of research on typical and atypical behavioral development. The purpose of this paper is to focus on a few implications and applications of these advances for understanding behavioral development. Quantitative genetics is genomic and will chart the course for molecular genomic research now that these two worlds of genetics are merging in the search for many genes of small effect. Although current attempts to identify specific genes have had limited success, known as the missing heritability problem, whole-genome sequencing will improve this situation by identifying all DNA sequence variations, including rare variants. Because the heritability of complex traits is caused by many DNA variants of small effect in the population, polygenic scores that are composites of hundreds or thousands of DNA variants will be used by developmentalists to predict children's genetic risk and resilience. The most far-reaching advance will be the widespread availability of whole-genome sequence for children, which means that developmentalists would no longer need to obtain DNA or to genotype children in order to use genomic information in research or in the clinic.

scholarly journals A Novel Approach for the Simultaneous Analysis of Common and Rare Variants in Complex Traits

2012 ◽  
Vol 6 ◽  
pp. BBI.S8852 ◽  
Author(s):  
Ao Yuan ◽  
Guanjie Chen ◽  
Yanxun Zhou ◽  
Amy Bentley ◽  
Charles Rotimi
Keyword(s):  
Complex Traits ◽  
Rare Variants ◽  
Sequence Data ◽  
Association Studies ◽  
Simultaneous Analysis ◽  
Genome Wide Association Studies ◽  
Common Variants ◽  
Disease Etiology ◽  
Novel Approach ◽  
Common Genetic Variants

Genome-wide association studies (GWAS) have been successful in detecting common genetic variants underlying common traits and diseases. Despite the GWAS success stories, the percent trait variance explained by GWAS signals, the so called “missing heritability” has been, at best, modest. Also, the predictive power of common variants identified by GWAS has not been encouraging. Given these observations along with the fact that the effects of rare variants are often, by design, unaccounted for by GWAS and the availability of sequence data, there is a growing need for robust analytic approaches to evaluate the contribution of rare variants to common complex diseases. Here we propose a new method that enables the simultaneous analysis of the association between rare and common variants in disease etiology. We refer to this method as SCARVA (simultaneous common and rare variants analysis). SCARVA is simple to use and is efficient. We used SCARVA to analyze two independent real datasets to identify rare and common variants underlying variation in obesity among participants in the Africa America Diabetes Mellitus (AADM) study and plasma triglyceride levels in the Dallas Heart Study (DHS). We found common and rare variants associated with both traits, consistent with published results.

scholarly journals A probabilistic method for identifying rare variants underlying complex traits

BMC Genomics ◽  
2013 ◽  
Vol 14 (Suppl 1) ◽  
pp. S11 ◽  
Author(s):  
Jiayin Wang ◽  
Zhongmeng Zhao ◽  
Zhi Cao ◽  
Aiyuan Yang ◽  
Jin Zhang
Keyword(s):  
Complex Traits ◽  
Rare Variants ◽  
Probabilistic Method
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