Explaining additional genetic variation in complex traits

Robinson, Matthew R.; Wray, Naomi R.; Visscher, Peter M.

doi:10.1016/j.tig.2014.02.003

Cited by 132 publications

(113 citation statements)

References 105 publications

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“…While targeting candidate genes (presumed adaptive) or serendipitously finding neutral markers linked to adaptive loci have not been uncommon in the conservation genetics era, given the low level of linkage disequilibrium often seen in natural populations [23] and polygenic nature of many traits [24], screening the entire genome holds considerably more power. Importantly, experimental systems have given us clues as to the signatures adaptive evolution leaves on the genome [25] and the 85 academic interest in using genomics to explore local adaptation in the wild has grown considerably (e.g., [26,27]).…”

Section: Scaling-up: What Can Genomics Do For Conservation Genetics?mentioning

confidence: 99%

Genomics and the challenging translation into conservation practice

Shafer¹,

Wolf²,

Alves³

et al. 2015

Trends in Ecology & Evolution

469

470

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The global loss of biodiversity continues at an alarming rate. Genomic approaches have been suggested as a promising tool for conservation practice, and we discuss how scaling-up to genome-wide inference can benefit traditional conservation genetic approaches and provide qualitatively novel insights. Yet, the generation of genomic data and subsequent analyses and interpretations are still challenging and largely confined to academic research in ecology and 20evolution. This generates a gap between basic research and applicable solutions for conservation managers faced with multifaceted problems. Before the real-world conservation potential of genomic research can be realized, we suggest that current infrastructures need to be modified, methods must mature, analytical pipelines need to be developed, and successful case studies must be disseminated to practitioners. 3 Conservation biology and genomicsLike most of the life sciences, conservation biology is being confronted with the challenge of how to integrate the collection and analysis of large-scale genomic data into its toolbox. Conservation biologists pull from a wide array of disciplines in an effort to preserve biodiversity and ecosystem services [1]. Genetic data have helped in this regard by 30 detecting, for example, population substructure, measuring genetic connectivity, and identifying potential risks associated with demographic change and inbreeding [2]. Traditionally, conservation genetics (see Glossary) has relied on a handful of molecular markers ranging from a few allozymes to dozens of microsatellites [3]. But for close to a decade [4], genomics -broadly defined high-throughput sampling of nucleic acids [5] -has been touted as an important advancement to the field, a panacea of sorts for the unresolved conservation problems typically addressed 35 with genetic data [6,7]. This transition has led to much promise, but also hyperbole, where concrete empirical examples of genomic data having a conservation impact remain rare.Under the premise that assisting conservation of the world's biota is its ultimate purpose, the emerging field of conservation genomics must openly and pragmatically discuss its potential contribution towards this goal. While there 40are prominent examples where genetic approaches have made inroads influencing conservation efforts (e.g., Florida panther augmentation [8,9]) and wildlife enforcement (i.e., detecting illegal harvest [10]), it is not immediately clear that the conservation community and society more broadly have embraced genomics as a useful tool for conservation.Maintaining genetic diversity has largely been an afterthought when it comes to national biodiversity policies [11,12], and attempts to identify areas that might prove to be essential for conserving biological diversity rarely mention 45 genomics (e.g. [13,14]). An obvious reason for this disconnect is that many of the pressing conservation issues (e.g., [15,16]) simply do not need genomics, but instead need political will.The traditional use of gene...

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Section: Scaling-up: What Can Genomics Do For Conservation Genetics?mentioning

confidence: 99%

Genomics and the challenging translation into conservation practice

Shafer¹,

Wolf²,

Alves³

et al. 2015

Trends in Ecology & Evolution

469

470

View full text Add to dashboard Cite

show abstract

“…Rare alleles evolve approximately independently of each other, and hence rather than considering a continuum of alleles, we can assume two alleles per locus. There has been much debate recently over what fraction of variance in complex traits is due to rare versus common alleles (see, for example, Robinson et al, 2014). This is a different question, which asks whether an individual allele that contributes variance is typically at (say) 0.1, 1 or 20%.…”

Section: Stabilising Selectionmentioning

confidence: 99%

How does epistasis influence the response to selection?

Barton

2016

Heredity

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Much of quantitative genetics is based on the 'infinitesimal model', under which selection has a negligible effect on the genetic variance. This is typically justified by assuming a very large number of loci with additive effects. However, it applies even when genes interact, provided that the number of loci is large enough that selection on each of them is weak relative to random drift. In the long term, directional selection will change allele frequencies, but even then, the effects of epistasis on the ultimate change in trait mean due to selection may be modest. Stabilising selection can maintain many traits close to their optima, even when the underlying alleles are weakly selected. However, the number of traits that can be optimised is apparently limited tõ 4N e by the 'drift load', and this is hard to reconcile with the apparent complexity of many organisms. Just as for the mutation load, this limit can be evaded by a particular form of negative epistasis. A more robust limit is set by the variance in reproductive success. This suggests that selection accumulates information most efficiently in the infinitesimal regime, when selection on individual alleles is weak, and comparable with random drift. A review of evidence on selection strength suggests that although most variance in fitness may be because of alleles with large N e s, substantial amounts of adaptation may be because of alleles in the infinitesimal regime, in which epistasis has modest effects.

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“…Large-scale international efforts are underway to dissect the genetics of these common conditions (Cornelis et al 2010;Sullivan 2010;Ehret et al 2011;Rivas et al 2011;Yang et al 2012). However, of the thousands of significant associations between genetic variants and complex traits or disease, most account for only a modest portion of overall trait variability (Visscher et al 2012;Robinson et al 2014). This has rendered it impossible to accurately characterize the genetic architecture of most complex traits (Agarwala et al 2013).…”

Section: Insights From Mouse Modelsmentioning

confidence: 99%

Contrasting genetic architectures in different mouse reference populations used for studying complex traits

Buchner

Nadeau

2015

Genome Res.

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Quantitative trait loci (QTLs) are being used to study genetic networks, protein functions, and systems properties that underlie phenotypic variation and disease risk in humans, model organisms, agricultural species, and natural populations. The challenges are many, beginning with the seemingly simple tasks of mapping QTLs and identifying their underlying genetic determinants. Various specialized resources have been developed to study complex traits in many model organisms. In the mouse, remarkably different pictures of genetic architectures are emerging. Chromosome Substitution Strains (CSSs) reveal many QTLs, large phenotypic effects, pervasive epistasis, and readily identified genetic variants. In contrast, other resources as well as genome-wide association studies (GWAS) in humans and other species reveal genetic architectures dominated with a relatively modest number of QTLs that have small individual and combined phenotypic effects. These contrasting architectures are the result of intrinsic differences in the study designs underlying different resources. The CSSs examine contextdependent phenotypic effects independently among individual genotypes, whereas with GWAS and other mouse resources, the average effect of each QTL is assessed among many individuals with heterogeneous genetic backgrounds. We argue that variation of genetic architectures among individuals is as important as population averages. Each of these important resources has particular merits and specific applications for these individual and population perspectives. Collectively, these resources together with high-throughput genotyping, sequencing and genetic engineering technologies, and information repositories highlight the power of the mouse for genetic, functional, and systems studies of complex traits and disease models.

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Explaining additional genetic variation in complex traits

Cited by 132 publications

References 105 publications

Genomics and the challenging translation into conservation practice

Genomics and the challenging translation into conservation practice

How does epistasis influence the response to selection?

Contrasting genetic architectures in different mouse reference populations used for studying complex traits

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