A century after Fisher: time for a new paradigm in quantitative genetics

Nelson, Ronald M.; Pettersson, Mats E.; Carlborg, Örjan

doi:10.1016/j.tig.2013.09.006

Cited by 117 publications

(115 citation statements)

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“…Population and quantitative genetics use simple and abstract models to explain the evolutionary consequences of this relationship-a bold undertaking. Many have questioned whether this approach can account for the complexities of gene interaction (that is, of epistasis), and have suggested that properly incorporating epistasis will radically change our ability to determine the causes of quantitative variation, and our understanding of evolution (Carlborg and Haley, 2004;Carter et al, 2005;Huang et al, 2012;Hansen, 2013;Nelson et al, 2013).…”

Section: Introductionmentioning

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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Section: Introductionmentioning

confidence: 99%

How does epistasis influence the response to selection?

Barton

2016

Heredity

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“…The magnitudes of these components of the genotypic variance each depend on the frequencies, the effects, and the interactions among the contributing genes (see also Falconer and Mackay 1996;Lynch and Walsh 1998). The actual causal genetic factors are usually not known, but many quantitative genetic analyses, including selection on metric traits, have been applied successfully without such knowledge.Among quantitative geneticists, interest in epistasis continues, both to understand the genetic architecture and as a potential way to improve the genomic predictions of disease and quantitative traits, utilizing some of the unexplained parts of the genetic variation (e.g., Carlborg and Haley 2004;Nelson et al 2013;Mackay 2014). Despite the obvious interactions in the biological system, it has, however, been argued that the proportion of the genotypic variance contributed by epistatic variance expected in outbred populations is small and that data generally support this prediction (Hill et al 2008).…”

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confidence: 99%

“…Further, it has been argued that the magnitude of epistatic variation is essentially irrelevant in evolution (and in animal breeding) (Crow 2010), because rates of evolutionary change depend only on the additive variance, even in epistatic and tightly linked systems (Kimura 1965;Nagylaki 1993). Nevertheless Nelson et al (2013) have recently argued that epistasis has been ignored in quantitative genetics and in its evolutionary studies because of convenience, and consequently statements about its insignificance are misleading. Genomics offers tools that go much deeper, to identify the genes involved and their interactions within the system, perhaps thereby providing an understanding of the causes of specific complex diseases or traits and a route to their improvement.…”

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confidence: 99%

“…Among quantitative geneticists, interest in epistasis continues, both to understand the genetic architecture and as a potential way to improve the genomic predictions of disease and quantitative traits, utilizing some of the unexplained parts of the genetic variation (e.g., Carlborg and Haley 2004;Nelson et al 2013;Mackay 2014). Despite the obvious interactions in the biological system, it has, however, been argued that the proportion of the genotypic variance contributed by epistatic variance expected in outbred populations is small and that data generally support this prediction (Hill et al 2008).…”

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confidence: 99%

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Influence of Gene Interaction on Complex Trait Variation with Multilocus Models

2014

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Although research effort is being expended into determining the importance of epistasis and epistatic variance for complex traits, there is considerable controversy about their importance. Here we undertake an analysis for quantitative traits utilizing a range of multilocus quantitative genetic models and gene frequency distributions, focusing on the potential magnitude of the epistatic variance. All the epistatic terms involving a particular locus appear in its average effect, with the number of two-locus interaction terms increasing in proportion to the square of the number of loci and that of third order as the cube and so on. Hence multilocus epistasis makes substantial contributions to the additive variance and does not, per se, lead to large increases in the nonadditive part of the genotypic variance. Even though this proportion can be high where epistasis is antagonistic to direct effects, it reduces with multiple loci. As the magnitude of the epistatic variance depends critically on the heterozygosity, for models where frequencies are widely dispersed, such as for selectively neutral mutations, contributions of epistatic variance are always small. Epistasis may be important in understanding the genetic architecture, for example, of function or human disease, but that does not imply that loci exhibiting it will contribute much genetic variance. Overall we conclude that theoretical predictions and experimental observations of low amounts of epistatic variance in outbred populations are concordant. It is not a likely source of missing heritability, for example, or major influence on predictions of rates of evolution. EPISTATIC variance in quantitative traits arises from the interaction effects or epistasis between segregating genes at two or more loci that affect these complex traits. Such gene interaction is a common phenomenon because many factors have, for example, a regulatory role in a hierarchical system (Phillips 2008). The statistical theory of quantitative genetics following Fisher (1918) is based on a partition between average effects across loci, which contribute to the additive genetic variance, and to interactions within loci and between loci, which contribute to the dominance and epistatic variance, respectively (Cockerham 1954;Kempthorne 1954). The magnitudes of these components of the genotypic variance each depend on the frequencies, the effects, and the interactions among the contributing genes (see also Falconer and Mackay 1996;Lynch and Walsh 1998). The actual causal genetic factors are usually not known, but many quantitative genetic analyses, including selection on metric traits, have been applied successfully without such knowledge.Among quantitative geneticists, interest in epistasis continues, both to understand the genetic architecture and as a potential way to improve the genomic predictions of disease and quantitative traits, utilizing some of the unexplained parts of the genetic variation (e.g., Carlborg and Haley 2004;Nelson et al. 2013;Mackay 2014). Despite the obviou...

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“…As an example, compared with a heritability of from nearly 50% up to 80% estimated from different twin studies for BMI (Elks et al 2012), thus far, a meta-analysis of multiple GWAS data sets has reported that 97 genomewide significant loci account for ,3% of the variance, while all common single-nucleotide polymorphisms (SNPs) account for $20% of the variance in which age has been adjusted for linearly (Locke et al 2015) and $17 million variants account for around 27% of the variance (Yang et al 2015). While literature has suggested that the fundamental assumption of additive genetics in GWAS might be problematic (Nelson et al 2013), estimates of heritability from the perspective of twin design should also be refined or revised by taking into account moderators such as age. One of the causes for missing heritability probably lies in the G 3 E interactions.…”

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confidence: 99%

Estimating Modifying Effect of Age on Genetic and Environmental Variance Components in Twin Models

Sillanpää

Silventoinen

et al. 2016

Genetics

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Twin studies have been adopted for decades to disentangle the relative genetic and environmental contributions for a wide range of traits. However, heritability estimation based on the classical twin models does not take into account dynamic behavior of the variance components over age. Varying variance of the genetic component over age can imply the existence of geneenvironment (G 3 E) interactions that general genome-wide association studies (GWAS) fail to capture, which may lead to the inconsistency of heritability estimates between twin design and GWAS. Existing parametric G 3 E interaction models for twin studies are limited by assuming a linear or quadratic form of the variance curves with respect to a moderator that can, however, be overly restricted in reality. Here we propose spline-based approaches to explore the variance curves of the genetic and environmental components. We choose the additive genetic, common, and unique environmental variance components (ACE) model as the starting point. We treat the component variances as variance functions with respect to age modeled by B-splines or P-splines. We develop an empirical Bayes method to estimate the variance curves together with their confidence bands and provide an R package for public use. Our simulations demonstrate that the proposed methods accurately capture dynamic behavior of the component variances in terms of mean square errors with a data set of .10,000 twin pairs. Using the proposed methods as an alternative and major extension to the classical twin models, our analyses with a large-scale Finnish twin data set (19,510 MZ twins and 27,312 DZ same-sex twins) discover that the variances of the A, C, and E components for body mass index (BMI) change substantially across life span in different patterns and the heritability of BMI drops to $50% after middle age. The results further indicate that the decline of heritability is due to increasing unique environmental variance, which provides more insights into age-specific heritability of BMI and evidence of G 3 E interactions. These findings highlight the fundamental importance and implication of the proposed models in facilitating twin studies to investigate the heritability specific to age and other modifying factors.KEYWORDS twin models; penalized B-splines; age-specific heritability; empirical Bayes predictor; body mass index T WIN studies have been broadly used as a tool to investigate heritability for decades. A recent comprehensive meta-analysis reported 17,804 heritability estimates for various traits based on twin studies (Polderman et al. 2015).Estimation methods and software such as structural equation modeling (SEM) based on the classical twin models consisting of additive genetic, common, and unshared environmental effects have been well established and extensively adopted (Rijsdijk and Sham 2002). Extensions for longitudinal data or time-to-event phenotypes based on the classical twin models have been proposed (Boomsma et al. 1989;Pitkäniemi et al. 2007). Nevertheless, one of...

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A century after Fisher: time for a new paradigm in quantitative genetics

Cited by 117 publications

References 79 publications

How does epistasis influence the response to selection?

How does epistasis influence the response to selection?

Influence of Gene Interaction on Complex Trait Variation with Multilocus Models

Estimating Modifying Effect of Age on Genetic and Environmental Variance Components in Twin Models

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