Quantitative trait locus analyses and the study of evolutionary process

Erickson, David L.; Fenster, Charles B.; Stenøien, Hans K.; Price, Daniel O.

doi:10.1111/j.1365-294x.2004.02254.x

Cited by 162 publications

(193 citation statements)

References 197 publications

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“…The QTL for hatch timing had a particularly low PEV, explaining only 4.5% of the variation. Sample sizes below 300, as used here, typically have low power to identify QTL of small effect particularly at marker densities less than 15 cM (Erickson et al, 2004). Non-model organisms or outbred populations often have lower density linkage maps that increase the probability of failing to detect small effect QTL, if they exist (Lynch and Walsh, 1998).…”

Section: Discussionmentioning

confidence: 99%

“…Outbred designs have the ability to expose more of the variation present in the originating populations because multiple QTL alleles can segregate in experimental families, thus potentially uncovering more evolutionarily important variation than inbred designs (Erickson et al, 2004). Response to selection on correlated traits in experiments using inbred lines has often differed from the predicted response given the correlations observed between the traits studied (Roff, 2007a).…”

Section: Introductionmentioning

confidence: 99%

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Quantitative trait locus analysis of hatch timing, weight, length and growth rate in coho salmon, Oncorhynchus kisutch

McClelland

Naish

2010

Heredity

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In quantitative genetics, multivariate statistical approaches are increasingly used to describe genetic correlations in natural populations, yet the exact genetic relationship between phenotype and genotype is often unknown. Quantitative trait locus (QTL) analyses can be used to describe the molecular basis of this relationship. In salmonids, growth and development are important fitness traits that are phenotypically correlated with each other and with other life-history traits, and an understanding of the molecular basis of these relationships is valuable for future evolutionary studies. Here, a QTL analysis using an outbred cross was initiated to determine the molecular basis of phenotypic correlations between such growth traits in coho salmon (Oncorhynchus kisutch), an important fish species distributed throughout the North Pacific Ocean. Fifty-three QTL for growth rate, length and weight at eight time periods were located on seven linkage groups (OKI03, OKI06, OKI18, OKI19, OKI23, OKI24 and an unnamed linkage group) or associated with five unlinked markers (Omm1159, Omm1367/ i, Omy325UoG, OmyRGT55TUF and OtsG422UCD). One QTL for hatch timing was associated with the marker, Omm1241. All QTL were of minor effect, explaining no more than 20% of the observed variation in phenotypic value. Several instances of colocalization of QTL weight, length and growth rate were observed, suggesting a genetic basis for phenotypic correlations observed between these traits. This study lays the foundation for future QTL mapping efforts, for detailed examinations of the genetic basis of phenotypic correlations between growth traits, and for exploring the adaptive significance of growth traits in natural populations.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantitative trait locus analysis of hatch timing, weight, length and growth rate in coho salmon, Oncorhynchus kisutch

McClelland

Naish

2010

Heredity

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“…Specifically, LD mapping relies on surveys of genetic polymorphism data from a collection of samples (inbred lines, accessions, individuals and populations) to test for statistical associations between these genetic markers and particular phenotypes, again based on the premise that the marker(s) is in LD with the causal locus, or less likely, is in fact the causal mutation itself (Box 1; see Mackay, 2001;Clark, 2003;Mitchell-Olds and Schmitt, 2006). By contrast, in a QTL mapping approach, statistical analyses of genome-wide molecular markers and phenotypes measured in progeny of controlled crosses are used to identify chromosomal regions contributing to phenotypic differentiation (reviewed in Mackay, 2001;Erickson et al, 2004).…”

Section: New Contributions From Quantitative Geneticsmentioning

confidence: 99%

Combining population genomics and quantitative genetics: finding the genes underlying ecologically important traits

2007

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A central challenge in evolutionary biology is to identify genes underlying ecologically important traits and describe the fitness consequences of naturally occurring variation at these loci. To address this goal, several novel approaches have been developed, including 'population genomics,' where a large number of molecular markers are scored in individuals from different environments with the goal of identifying markers showing unusual patterns of variation, potentially due to selection at linked sites. Such approaches are appealing because of (1) the increasing ease of generating large numbers of genetic markers, (2) the ability to scan the genome without measuring phenotypes and (3) the simplicity of sampling individuals without knowledge of their breeding history. Although such approaches are inherently applicable to non-model systems, to date these studies have been limited in their ability to uncover functionally relevant genes. By contrast, quantitative genetics has a rich history, and more recently, quantitative trait locus (QTL) mapping has had some success in identifying genes underlying ecologically relevant variation even in novel systems. QTL mapping, however, requires (1) genetic markers that specifically differentiate parental forms, (2) a focus on a particular measurable phenotype and (3) controlled breeding and maintenance of large numbers of progeny. Here we present current advances and suggest future directions that take advantage of population genomics and quantitative genetic approaches -in both model and non-model systems. Specifically, we discuss advantages and limitations of each method and argue that a combination of the two provides a powerful approach to uncovering the molecular mechanisms responsible for adaptation.

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“…These statistics serve to illustrate the dynamics more clearly and highlight that surviving deleterious mutants may, on average, occupy more patches and have higher total abundance than an average surviving neutral mutant (Table 1, Figure 3). As a greater range of methods are developed to describe empirical patterns of quantitative genetic variation (for example, Mauricio, 2001;Erickson et al, 2004), there will be considerable potential to use these, together with data on neutral markers, to improve inferences regarding past range expansions. These empirical data can only give us a picture of mutations that have survived until today, and simulations that can produce expected patterns of surviving neutral and non-neutral genetic diversity will be needed if we are to best use the additional information that new quantitative genetic methods will provide.…”

Section: Oj Burton and Jmj Travismentioning

confidence: 99%

Landscape structure and boundary effects determine the fate of mutations occurring during range expansions

Burton

Travis

2008

Heredity

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The interplay between the spatial dynamics of range expansion and evolutionary processes is receiving considerable attention. Recent theory has demonstrated that mutations occurring towards the front of a spatially expanding population can sometimes 'surf' to high frequency and spatial extent. Here, we extend this work to consider how the fate of a novel mutation is influenced by where and when it occurs. Specifically, we are interested in establishing how the origin of a mutation relative to a habitat edge influences its dynamics, and in understanding how this is mediated by the behaviour of individuals at those boundaries. Using a coupled-map lattice model, we demonstrate that the survival probability, abundance and spatial extent of surviving mutants can depend on their origin. An edge effect is often observed and can be quite different both qualitatively and quantitatively depending on the behavioural rules assumed. Mutations, especially those that are deleterious, that arise at a habitat edge with reflective boundary conditions can be many more times likely to survive for substantial periods of time than those that arise away from the edge. Conversely, with absorbing boundary conditions, their survival is greater when they arise well away from the edge. Our results clearly illustrate that landscape structure, habitat edges and boundary conditions have a considerable influence on the likely fate of mutations that occur during a period of range expansion.

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Quantitative trait locus analyses and the study of evolutionary process

Cited by 162 publications

References 197 publications

Quantitative trait locus analysis of hatch timing, weight, length and growth rate in coho salmon, Oncorhynchus kisutch

Quantitative trait locus analysis of hatch timing, weight, length and growth rate in coho salmon, Oncorhynchus kisutch

Combining population genomics and quantitative genetics: finding the genes underlying ecologically important traits

Landscape structure and boundary effects determine the fate of mutations occurring during range expansions

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