Population genetics and GWAS: A primer

Gibson, Greg

doi:10.1371/journal.pbio.2005485

Cited by 38 publications

(26 citation statements)

References 28 publications

(30 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We would be wrong to underestimate the sheer magnitude of this task [29]. The compilation of large cohorts of ALS patients and controls, where the temporal exposome of each subject has been recorded in detail, is something which is only just the beginning.…”

Section: Empirical Approaches To Unraveling Gene-environment-time Intmentioning

confidence: 99%

Gene-Environment-Time Interactions in Neurodegenerative Diseases: Hypotheses and Research Approaches

et al. 2018

View full text Add to dashboard Cite

Background: Amyotrophic lateral sclerosis (ALS), Alzheimer’s, and Parkinson’s diseases are age-related neurodegenerative diseases. ALS is not a single entity but a syndrome with many different causes. In all 3 diseases, gene mutations account for only 10–15% of cases. Many environmental and lifestyle factors have been implicated as risk factors for ALS, though none have been proven to cause the disease. It is generally believed that ALS results from interactions between environmental risk factors and genetic predisposing factors. The advent of next-generation sequencing and recent advances in research into environmental risk factors offer the opportunity to investigate these interactions. Summary: We propose a hypothesis to explain the syndrome of ALS based on the interaction of many individual environmental risk factors with many individual genetic predisposing factors. We hypothesize that there are many such combinations of individual, specific, genetic, and environmental factors, and that each combination can lead to the development of the syndrome of ALS. We also propose a hypothesis that explains the overlap between the age-related neurodegenerations and their genetic underpinnings. Age and duration of exposure are crucial factors in these age-related neurodegenerative diseases, and we consider how these may relate to gene-environment interactions. Key Messages: To date, genetic studies and environmental studies have investigated the causes of ALS separately. We argue that this univariate approach will not lead to discoveries of important gene-environment interactions. We propose new research approaches to investigating gene-environment interactions based on these hypotheses.

show abstract

Section: Empirical Approaches To Unraveling Gene-environment-time Intmentioning

confidence: 99%

Gene-Environment-Time Interactions in Neurodegenerative Diseases: Hypotheses and Research Approaches

et al. 2018

View full text Add to dashboard Cite

show abstract

“…As these genetic variants mostly only affect the naturally occurring pathogen of D. melanogaster , our results suggest that not only is selection by pathogens increasing the genetic variance but it is also altering the genetic architecture of the trait by introducing major-effect variants into the population. One explanation for this observation is that most quantitative traits are under stabilising selection, so major effect variants will tend to be deleterious and removed by selection [57]. In contrast, selection by pathogens likely changes through time and populations may be far from their optimal level of resistance.…”

Section: Discussionmentioning

confidence: 99%

Host-pathogen coevolution increases genetic variation in susceptibility to infection

Duxbury

Day

Vespasiani

et al. 2018

Preprint

View full text Add to dashboard Cite

35It is common to find considerable genetic variation in susceptibility to infection in natural 36 populations. We have investigated whether natural selection increases this variation by 37 testing whether host populations show more genetic variation in susceptibility to pathogens 38 that they naturally encounter than novel pathogens. In a large cross-infection experiment 39 involving four species of Drosophila and four host-specific viruses, we always found greater 40 genetic variation in susceptibility to viruses that had coevolved with their host. We went on 41 to examine the genetic architecture of resistance in one host species, finding that there are 42 more major-effect genetic variants in coevolved host-parasite interactions. We conclude 43 that selection by pathogens increases genetic variation in host susceptibility, and much of 44 this effect is caused by the occurrence of major-effect resistance polymorphisms within 45 populations.46 47 53 to infection. Insect populations, like those of other organisms, typically contain considerable 54 genetic variation in susceptibility to infection [2, 4, 9, 10], and provide a convenient 55 laboratory model in which to investigate basic questions about how this variation is 56 maintained [11]. Within vector species like mosquitoes, resistant genotypes are less likely to 57 transmit parasites, and this has the potential to reduce disease in vertebrate populations 58 [12]. Where pathogens are contributing the decline of beneficial species like pollinators, high 59 levels of genetic variation may allow populations to recover [13]. Understanding the origins 60 of genetic variation in susceptibility is therefore a fundamental question in infectious disease 61 biology. 63As pathogens are harmful, natural selection is expected to favour resistant host genotypes. 64Directional selection on standing genetic variation will drive alleles to fixation, removing 65 variants from the population [14-16]. However, as directional selection also increases the 66 frequency of mutations that change the trait in the direction of selection, at equilibrium it is 67 expected to have no effect on levels of standing genetic variation (relative to mutation-drift 68 balance; [17]). However, selection mediated by pathogens may be different. Coevolution 69 with pathogens can result in the maintenance of both resistant and susceptible alleles by 70 negative frequency dependent selection [18, 19]. Similarly, when infection prevalence 71 exhibits geographical or temporal variation, selection can maintain genetic variation,72 especially if pleiotropic costs to resistance provide an advantage to susceptible individuals 73 when infection is rare [20-22]. Even when there is simple directional selection on alleles that 74 increase resistance, the direction of selection by pathogens may frequently change so 75 populations may not be at equilibrium. If selection favours rare alleles -such as new 76 mutations -directional selection can transiently increase genetic variation during their 77 spread through t...

show abstract

“…As expected, the power of GWAS increased with the population size ( fig. 6A) (Gibson, 2018). Interestingly, an E&R study with an optimized regime (90 → 10%) had a higher power to identify QTNs than a GWAS with 8000 individuals (fig.…”

Section: Eandr Versus Gwasmentioning

confidence: 93%

Optimizing the power to identify the genetic basis of complex traits with Evolve and Resequence studies

Vlachos

Kofler

2019

Preprint

View full text Add to dashboard Cite

Evolve and Resequence (E&R) studies are frequently used to dissect the genetic basis of quantitative traits.By subjecting a population to truncating selection for several generations and estimating the allele frequency differences between selected and non-selected populations using Next Generation Sequencing, the loci contributing to the selected trait may be identified. The role of different parameters, such as, the population size or the number of replicate populations have been examined in previous works. However, the influence of the selection regime, i.e. the strength of truncating selection during the experiment, remains little explored.Using whole genome, individual based forward simulations of E&R studies, we found that the power to identify the causative alleles may be maximized by gradually increasing the strength of truncating selection during the experiment. Notably, such an optimal selection regime comes at no or little additional cost in terms of sequencing effort and experimental time. Interestingly, we also found that a selection regime which optimizes the power to identify the causative loci is not necessarily identical to a regime that maximizes the phenotypic response. Finally, our simulations suggest that an E&R study with an optimized selection regime may have a higher power to identify the genetic basis of quantitative traits than a GWAS, highlighting that E&R is a powerful approach for finding the loci underlying complex traits.

show abstract

Population genetics and GWAS: A primer

Abstract: This primer provides some background to help non-specialists understand a new theoretical evolutionary genetics study that helps explain why thousands of variants of small effect contribute to complex traits.

Cited by 38 publications

References 28 publications

Gene-Environment-Time Interactions in Neurodegenerative Diseases: Hypotheses and Research Approaches

Gene-Environment-Time Interactions in Neurodegenerative Diseases: Hypotheses and Research Approaches

Host-pathogen coevolution increases genetic variation in susceptibility to infection

Optimizing the power to identify the genetic basis of complex traits with Evolve and Resequence studies

Contact Info

Product

Resources

About