A comparison of methods to estimate cross-environment genetic correlations

Astles, Philip A; Moore, Allen J.; Preziosi, Richard F.

doi:10.1111/j.1420-9101.2005.00997.x

Cited by 57 publications

(64 citation statements)

References 47 publications

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“…Standard errors for intrasexual genetic correlations were calculated using the jackknife because there is considerable debate and no consensus over the best estimates for standard errors and statistical significance associated with genetic correlations (40,42). There is no agreed upon or suggested solution for standard error associated with our estimates of intersexual correlations (42,43). However, as is usual with studies of genetic correlations (42,43), our goal here is to describe general patterns of covariance rather than to make point estimates.…”

Section: Methodsmentioning

confidence: 99%

“…with variances computed as in Astles et al (43). We calculated standard errors for heritabilities as suggested by Lynch and Walsh (42) for unbalanced designs.…”

Section: Methodsmentioning

confidence: 99%

“…We obtained these estimates by a single restricted maximum likelihood ANOVA with sire and dam nested within sire as random effects, sex as a fixed effect, sire by sex, and dam nested within sire by sex as factors (42,43). We estimated the intersex genetic correlation as:…”

Section: Methodsmentioning

confidence: 99%

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The quantitative genetics of sex differences in parenting

Walling

Stamper

Smiseth

et al. 2008

Proc. Natl. Acad. Sci. U.S.A.

137

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Sex differences in parenting are common in species where both males and females provide care. Although there is a considerable body of game and optimality theory for why the sexes should differ in parental care, genetics can also play a role, and no study has examined how genetic influences might influence differences in parenting. We investigated the extent that genetic variation influenced differences in parenting, whether the evolution of differences could be constrained by shared genetic influences, and how sex-specific patterns of genetic variation underlying parental care might dictate which behaviors are free to evolve in the burying beetle Nicrophorus vespilloides. Females provided more direct care than males but did not differ in levels of indirect care or the number of offspring they were willing to rear. We found low to moderate levels of heritability and evolvability for all 3 parenting traits in both sexes. Intralocus sexual conflict was indicated by moderately strong intersex genetic correlations, but these were not so strong as to represent an absolute constraint to the evolution of sexual dimorphism in care behavior. Instead, the pattern of genetic correlations between parental behaviors showed sexspecific tradeoffs. Thus, differences in the genetic correlations between parental traits within a sex create sex-specific lines of least evolutionary resistance, which in turn produce the specific patterns of sex differences in parental care. Our results therefore suggest a mechanism for the evolution of behavioral specialization during biparental care if uniparental and biparental care behaviors share the same genetic influences.behavior genetics ͉ genetic architecture ͉ lines of least evolutionary resistance ͉ parental care ͉ sexual dimorphism

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Section: Methodsmentioning

confidence: 99%

“…with variances computed as in Astles et al (43). We calculated standard errors for heritabilities as suggested by Lynch and Walsh (42) for unbalanced designs.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

The quantitative genetics of sex differences in parenting

Walling

Stamper

Smiseth

et al. 2008

Proc. Natl. Acad. Sci. U.S.A.

137

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“…Furthermore, genetic relationships between tolerance and other traits are often estimated using correlations between family means for each trait. Familyxmean correlations are typically downward-biased estimates of the true genetic correlations (Astles et al, 2006). Thus, these approaches tend to produce biased estimates of genetic variances and genetic correlations for tolerance.…”

Section: Introductionmentioning

confidence: 99%

Genetic analysis of tolerance to infections using random regressions: a simulation study

Kause

2011

Genet. Res.

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SummaryTolerance to infections is the ability of a host to limit the impact of a given pathogen burden on host performance. This simulation study demonstrated the merit of using random regressions to estimate unbiased genetic variances for tolerance slope and its genetic correlations with other traits, which could not be obtained using the previously implemented statistical methods. Genetic variance in tolerance was estimated as genetic variance in regression slopes of host performance along an increasing pathogen burden level. Random regressions combined with covariance functions allowed genetic variance for host performance to be estimated at any point along the pathogen burden trajectory, providing a novel means to analyse infection-induced changes in genetic variation of host performance. Yet, the results implied that decreasing family size as well as a non-zero environmental or genetic correlation between initial host performance before infection and pathogen burden led to biased estimates for tolerance genetic variance. In both cases, genetic correlation between tolerance slope and host performance in a pathogen-free environment became artificially negative, implying a genetic trade-off when it did not exist. Moreover, recording a normally distributed pathogen burden as a threshold trait is not a realistic way of obtaining unbiased estimates for tolerance genetic variance. The results show that random regressions are suitable for the genetic analysis of tolerance, given suitable data structure collected either under field or experimental conditions.

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“…A sire-mean correlation is an approximation of the true genetic correlation, and more conservative (i.e. produces a weaker genetic correlation) than Restricted Maximum Likelihood (REML) (Roff, 1997 ;Astles et al, 2006).…”

Section: (Vi) Linear Model Genetic Analysesmentioning

confidence: 99%

Genetic architecture of rainbow trout survival from egg to adult

Vehviläinen

Kause

Koskinen³

et al. 2010

Genet. Res.

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SummarySurvival from birth to a reproductive adult is a challenge that only robust individuals resistant to a variety of mortality factors will overcome. To assess whether survival traits share genetic architecture throughout the life cycle, we estimated genetic correlations for survival within fingerling stage, and across egg, fingerling and grow-out stages in farmed rainbow trout. Genetic parameters of survival at three life cycle stages were estimated for 249 166 individuals originating from ten year classes of a pedigreed population. Despite being an important fitness component, survival traits harboured significant but modest amount of genetic variation (h 2 =0 . 07-0 . 27). Weak associations between survival during egg-fry and fingerling periods, between early and late fingerling periods (r G =0 . 30) and generally low genetic correlations between fingerling and grow-out survival (mean r G =0 . 06) suggested that life-stage specific survival traits are best regarded as separate traits. However, in the sub-set of data with detailed time of death records, positive genetic correlations between early and late fingerling survival (r G =0 . 89) showed that during certain years the best genotypes in the early period were also among the best in the late period. That survival across fingerling period can be genetically the same, trait was indicated also by only slightly higher heritability (h 2 =0 . 15) estimated with the survival analysis of time to death during fingerling period compared to the analysis treating fingerling survival as a binary character (h 2 =0 . 11). The results imply that (1) inherited resistance against unknown mortality factors exists, but (2) ranking of genotypes changes across life stages.

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A comparison of methods to estimate cross-environment genetic correlations

Cited by 57 publications

References 47 publications

The quantitative genetics of sex differences in parenting

The quantitative genetics of sex differences in parenting

Genetic analysis of tolerance to infections using random regressions: a simulation study

Genetic architecture of rainbow trout survival from egg to adult

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