329 research outputs found
Dissecting genetic interactions in complex traits
Of central importance in the dissection of the components that govern complex traits is understanding
the architecture of natural genetic variation. Genetic interaction, or epistasis,
constitutes one aspect of this, but epistatic analysis has been largely avoided in genome wide
association studies because of statistical and computational difficulties. This thesis explores
both issues in the context of two-locus interactions.
Initially, through simulation and deterministic calculations it was demonstrated that not only
can epistasis maintain deleterious mutations at intermediate frequencies when under selection,
but that it may also have a role in the maintenance of additive variance. Based on the epistatic
patterns that are evolutionarily persistent, and the frequencies at which they are maintained, it
was shown that exhaustive two dimensional search strategies are the most powerful approaches
for uncovering both additive variance and the other genetic variance components that are co-precipitated.
However, while these simulations demonstrate encouraging statistical benefits, two dimensional
searches are often computationally prohibitive, particularly with the marker densities and sample
sizes that are typical of genome wide association studies. To address this issue different
software implementations were developed to parallelise the two dimensional triangular search
grid across various types of high performance computing hardware. Of these, particularly effective
was using the massively-multi-core architecture of consumer level graphics cards. While
the performance will continue to improve as hardware improves, at the time of testing the speed
was 2-3 orders of magnitude faster than CPU based software solutions that are in current use.
Not only does this software enable epistatic scans to be performed routinely at minimal cost,
but it is now feasible to empirically explore the false discovery rates introduced by the high
dimensionality of multiple testing. Through permutation analysis it was shown that the significance threshold for epistatic searches is a function of both marker density and population
sample size, and that because of the correlation structure that exists between tests the threshold
estimates currently used are overly stringent.
Although the relaxed threshold estimates constitute an improvement in the power of two dimensional
searches, detection is still most likely limited to relatively large genetic effects. Through
direct calculation it was shown that, in contrast to the additive case where the decay of estimated
genetic variance was proportional to falling linkage disequilibrium between causal variants and
observed markers, for epistasis this decay was exponential. One way to rescue poorly captured
causal variants is to parameterise association tests using haplotypes rather than single markers.
A novel statistical method that uses a regularised parameter selection procedure on two locus
haplotypes was developed, and through extensive simulations it can be shown that it delivers a
substantial gain in power over single marker based tests.
Ultimately, this thesis seeks to demonstrate that many of the obstacles in epistatic analysis
can be ameliorated, and with the current abundance of genomic data gathered by the scientific
community direct search may be a viable method to qualify the importance of epistasis
An Evolutionary Perspective on Epistasis and the Missing Heritability
<div><p>The relative importance between additive and non-additive genetic variance has been widely argued in quantitative genetics. By approaching this question from an evolutionary perspective we show that, while additive variance can be maintained under selection at a low level for some patterns of epistasis, the majority of the genetic variance that will persist is actually non-additive. We propose that one reason that the problem of the “missing heritability” arises is because the additive genetic variation that is estimated to be contributing to the variance of a trait will most likely be an artefact of the non-additive variance that can be maintained over evolutionary time. In addition, it can be shown that even a small reduction in linkage disequilibrium between causal variants and observed SNPs rapidly erodes estimates of epistatic variance, leading to an inflation in the perceived importance of additive effects. We demonstrate that the perception of independent additive effects comprising the majority of the genetic architecture of complex traits is biased upwards and that the search for causal variants in complex traits under selection is potentially underpowered by parameterising for additive effects alone. Given dense SNP panels the detection of causal variants through genome-wide association studies may be improved by searching for epistatic effects explicitly.</p> </div
Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score
Mendelian randomization (MR) is a method of exploiting genetic variation to
unbiasedly estimate a causal effect in presence of unmeasured confounding. MR
is being widely used in epidemiology and other related areas of population
science. In this paper, we study statistical inference in the increasingly
popular two-sample summary-data MR design. We show a linear model for the
observed associations approximately holds in a wide variety of settings when
all the genetic variants satisfy the exclusion restriction assumption, or in
genetic terms, when there is no pleiotropy. In this scenario, we derive a
maximum profile likelihood estimator with provable consistency and asymptotic
normality. However, through analyzing real datasets, we find strong evidence of
both systematic and idiosyncratic pleiotropy in MR, echoing the omnigenic model
of complex traits that is recently proposed in genetics. We model the
systematic pleiotropy by a random effects model, where no genetic variant
satisfies the exclusion restriction condition exactly. In this case we propose
a consistent and asymptotically normal estimator by adjusting the profile
score. We then tackle the idiosyncratic pleiotropy by robustifying the adjusted
profile score. We demonstrate the robustness and efficiency of the proposed
methods using several simulated and real datasets.Comment: 59 pages, 5 figures, 6 table
Population phenomena inflate genetic associations of complex social traits
Heritability, genetic correlation, and genetic associations estimated from samples of unrelated individuals are often perceived as confirmation that genotype causes the phenotype(s). However, these estimates can arise from indirect mechanisms due to population phenomena including population stratification, dynastic effects, and assortative mating. We introduce these, describe how they can bias or inflate genotype-phenotype associations, and demonstrate methods that can be used to assess their presence. Using data on educational achievement and parental socioeconomic position as an exemplar, we demonstrate that both heritability and genetic correlation may be biased estimates of the causal contribution of genotype. These results highlight the limitations of genotype-phenotype estimates obtained from samples of unrelated individuals. Use of these methods in combination with family-based designs may offer researchers greater opportunities to explore the mechanisms driving genotype-phenotype associations and identify factors underlying bias in estimates
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