1,542 research outputs found
The Impact of Incomplete Linkage Disequilibrium and Genetic Model Choice on the Analysis and Interpretation of Genome-wide Association Studies
When conducting a genetic association study, it has previously been observed that a multiplicative risk model tends to fit better at a disease-associated marker locus than at the ungenotyped causative locus. This suggests that, while overall risk decreases as linkage disequilibrium breaks down, non-multiplicative components are more affected. This effect is investigated here, in particular the practical consequences it has on testing for trait/marker associations and the estimation of mode of inheritance and risk once an associated locus has been found. The extreme significance levels required for genome-wide association studies define a restricted range of detectable allele frequencies and effect sizes. For such parameters there is little to be gained by using a test that models the correct mode of inheritance rather than the multiplicative; thus the Cochran-Armitage trend test, which assumes a multiplicative model, is preferable to a more general model as it uses fewer degrees of freedom. Equally when estimating risk, it is likely that a multiplicative risk model will provide a good fit to the data, regardless of the underlying mode of inheritance at the true susceptibility locus. This may lead to problems in interpreting risk estimates
A Bayesian Method for Detecting and Characterizing Allelic Heterogeneity and Boosting Signals in Genome-Wide Association Studies
The standard paradigm for the analysis of genome-wide association studies
involves carrying out association tests at both typed and imputed SNPs. These
methods will not be optimal for detecting the signal of association at SNPs
that are not currently known or in regions where allelic heterogeneity occurs.
We propose a novel association test, complementary to the SNP-based approaches,
that attempts to extract further signals of association by explicitly modeling
and estimating both unknown SNPs and allelic heterogeneity at a locus. At each
site we estimate the genealogy of the case-control sample by taking advantage
of the HapMap haplotypes across the genome. Allelic heterogeneity is modeled by
allowing more than one mutation on the branches of the genealogy. Our use of
Bayesian methods allows us to assess directly the evidence for a causative SNP
not well correlated with known SNPs and for allelic heterogeneity at each
locus. Using simulated data and real data from the WTCCC project, we show that
our method (i) produces a significant boost in signal and accurately identifies
the form of the allelic heterogeneity in regions where it is known to exist,
(ii) can suggest new signals that are not found by testing typed or imputed
SNPs and (iii) can provide more accurate estimates of effect sizes in regions
of association.Comment: Published in at http://dx.doi.org/10.1214/09-STS311 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Common DNA markers can account for more than half of the genetic influence on cognitive abilities
For nearly a century, twin and adoption studies have yielded substantial estimates of heritability for cognitive abilities, although it has proved difficult for genomewide-association studies to identify the genetic variants that account for this heritability (i.e., the missing-heritability problem). However, a new approach, genomewide complex-trait analysis (GCTA), forgoes the identification of individual variants to estimate the total heritability captured by common DNA markers on genotyping arrays. In the same sample of 3,154 pairs of 12-year-old twins, we directly compared twin-study heritability estimates for cognitive abilities (language, verbal, nonverbal, and general) with GCTA estimates captured by 1.7 million DNA markers. We found that DNA markers tagged by the array accounted for .66 of the estimated heritability, reaffirming that cognitive abilities are heritable. Larger sample sizes alone will be sufficient to identify many of the genetic variants that influence cognitive abilities
Synergy Disequilibrium Plots: graphical visualization of pairwise synergies and redundancies of SNPs with respect to a phenotype
Summary:We present a visualization tool applied on genome-wide association data, revealing disease-associated haplotypes, epistatically interacting loci, as well as providing visual signatures of multivariate correlations of genetic markers with respect to a phenotype
Novel rheumatoid arthritis susceptibility locus at 22q12 identified in an extended UK genome-wide association study
© 2014 The Authors. Arthritis & Rheumatology is published by Wiley Periodicals, Inc. on behalf of the American College of Rheumatology.Peer reviewedPublisher PD
Another tool in the genome-wide association study arsenal: population-based detection of somatic gene conversion
The hunt for the genetic contributors to complex disease has used a number of strategies, resulting in the identification of variants associated with many of the common diseases affecting society. However most of the genetic variants detected to date are single nucleotide polymorphisms (SNPs) and copy number variants (CNVs) and fall far short of explaining the full genetic component of any given disease. An as yet untapped genomic mechanism is somatic gene conversion and deletion, which could be complicit in disease risk but has been challenging to detect in genome-wide datasets. In a recent publication in BMC Medicine by Kenneth Ross, the author uses existing datasets to look at somatic gene conversion and deletion in human disease. Here, we describe how Ross's recent efforts to detect such occurrences could impact the field going forward
Exploring Case-Control Genetic Association Tests Using Phase Diagrams
Background: By a new concept called "phase diagram", we compare two commonly
used genotype-based tests for case-control genetic analysis, one is a
Cochran-Armitage trend test (CAT test at , or CAT0.5) and another
(called MAX2) is the maximization of two chi-square test results: one from the
two-by-two genotype count table that combines the baseline homozygotes and
heterozygotes, and another from the table that combines heterozygotes with risk
homozygotes. CAT0.5 is more suitable for multiplicative disease models and MAX2
is better for dominant/recessive models.
Methods: We define the CAT0.5-MAX2 phase diagram on the disease model space
such that regions where MAX2 is more powerful than CAT0.5 are separated from
regions where the CAT0.5 is more powerful, and the task is to choose the
appropriate parameterization to make the separation possible.
Results: We find that using the difference of allele frequencies ()
and the difference of Hardy-Weinberg disequilibrium coefficients
() can separate the two phases well, and the phase boundaries
are determined by the angle , which is an
improvement over the disease model selection using only.
Conclusions: We argue that phase diagrams similar to the one for CAT0.5-MAX2
have graphical appeals in understanding power performance of various tests,
clarifying simulation schemes, summarizing case-control datasets, and guessing
the possible mode of inheritance
A Powerful Approach to Sub-Phenotype Analysis in Population-Based Genetic Association Studies
The ultimate goal of genome-wide association (GWA) studies is to identify genetic variants contributing effects to complex phenotypes in order to improve our understanding of the biological architecture underlying the trait. One approach to allow us to meet this challenge is to consider more refined sub-phenotypes of disease, defined by pattern of symptoms, for example, which may be physiologically distinct, and thus may have different underlying genetic causes. The disadvantage of sub-phenotype analysis is that large disease cohorts are sub-divided into smaller case categories, thus reducing power to detect association. To address this issue, we have developed a novel test of association within a multinomial regression modeling framework, allowing for heterogeneity of genetic effects between sub-phenotypes. The modeling framework is extremely flexible, and can be generalized to any number of distinct sub-phenotypes. Simulations demonstrate the power of the multinomial regression-based analysis over existing methods when genetic effects differ between sub-phenotypes, with minimal loss of power when these effects are homogenous for the unified phenotype. Application of the multinomial regression analysis to a genome-wide association study of type 2 diabetes, with cases categorized according to body mass index, highlights previously recognized differential mechanisms underlying obese and non-obese forms of the disease, and provides evidence of a potential novel association that warrants follow-up in independent replication cohorts
A20 deletion in T cells modulates acute graft-versus-host disease in mice
The NF-kappa B regulator A20 limits inflammation by providing negative feedback in myeloid cells and B cells. Functional lack of A20 has been linked to several inflammatory and autoimmune diseases. To define how A20 affects the functionality of T effector cells in a highly inflammatory environment, we performed conventional allogeneic hematopoietic stem cell transplantation (allo-HSCT) with A20-deficient CD4(+) and CD8(+) donor T cells in mice. Severity and mortality of graft-versus-host disease (GVHD) after allo-HSCT was drastically reduced in recipients transplanted with conventional doses of A20-deficient T cells. Consistently, we found that the A20-deficient donor T-cell compartment was strongly diminished at various timepoints after allo-HSCT. However, proportionally more A20-deficient donor T cells produced IFN-gamma and systemic inflammationwas elevated early after allo-HSCT. Consequently, increasing the dose of transplanted A20-deficient T cells reversed the original phenotype and resulted in enhanced GVHD mortality compared to recipients that received A20(+/+) T cells. Still, A20-deficient T cells, activated either through T cell receptor-dependent or -independent mechanisms, were less viable than control A20(+/+) T cells, highlighting that A20 balances both, T-cell activation and survival. Thus, our findings suggest that targeting A20 in T cells may allow to modulate T-cell-mediated inflammatory diseases like GVHD
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