Estimation of genetic variance for macro- and micro-environmental sensitivity using double hierarchical generalized linear models

Mulder, H.A.; Rönnegård, Lars; Fikse, W.F.; Veerkamp, R.F.; Strandberg, E.

doi:10.1186/1297-9686-45-23

Cited by 55 publications

(106 citation statements)

References 47 publications

(82 reference statements)

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“…Approaches in this case range from the use of twin studies to family-based analyses (34). In systems where controlled crosses can be carried out, a wider range of options is possible (2,12,13,40). These approaches have been particularly effective in breeding programs, where intragenotypic variability is not desirable (2).…”

Section: Discussionmentioning

confidence: 99%

Behavioral idiosyncrasy reveals genetic control of phenotypic variability

Ayroles

Buchanan²,

O’Leary

et al. 2015

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

178

207

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Quantitative genetics has primarily focused on describing genetic effects on trait means and largely ignored the effect of alternative alleles on trait variability, potentially missing an important axis of genetic variation contributing to phenotypic differences among individuals. To study the genetic effects on individual-to-individual phenotypic variability (or intragenotypic variability), we used Drosophila inbred lines and measured the spontaneous locomotor behavior of flies walking individually in Y-shaped mazes, focusing on variability in locomotor handedness, an assay optimized to measure variability. We discovered that some lines had consistently high levels of intragenotypic variability among individuals, whereas lines with low variability behaved as although they tossed a coin at each left/right turn decision. We demonstrate that the degree of variability is itself heritable. Using a genome-wide association study (GWAS) for the degree of intragenotypic variability as the phenotype across lines, we identified several genes expressed in the brain that affect variability in handedness without affecting the mean. One of these genes, Ten-a, implicates a neuropil in the central complex of the fly brain as influencing the magnitude of behavioral variability, a brain region involved in sensory integration and locomotor coordination. We validated these results using genetic deficiencies, null alleles, and inducible RNAi transgenes. Our study reveals the constellation of phenotypes that can arise from a single genotype and shows that different genetic backgrounds differ dramatically in their propensity for phenotypic variabililty. Because traditional mean-focused GWASs ignore the contribution of variability to overall phenotypic variation, current methods may miss important links between genotype and phenotype.variability | variance QTL | DGRP | ten-a | personality

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Section: Discussionmentioning

confidence: 99%

Behavioral idiosyncrasy reveals genetic control of phenotypic variability

Ayroles

Buchanan²,

O’Leary

et al. 2015

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

178

207

View full text Add to dashboard Cite

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“…At convergence, the 2 univariate estimations of variance components for both mean and residual variance models were equivalent to a bivariate estimation of variance components for both mean and residual variance models for which correlations were fixed to zero between p and p v and between u and u v . Therefore, the adjusted profile h-likelihood (APHL) can be approximated from the log REML-likelihood (logL) of the bivariate model taking into account the fact that the adjusted squared residuals from the mean model were fitted for the residual variance model (Felleki et al, 2012;Mulder et al, 2013): Akaike, 1973) was estimated based on the APHL :…”

Section: Statistical Modelmentioning

confidence: 99%

Genetic variance in micro-environmental sensitivity for milk and milk quality in Walloon Holstein cattle

Vandenplas

Bastin

Gengler

et al. 2013

Journal of Dairy Science

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Animals that are robust to environmental changes are desirable in the current dairy industry. Genetic differences in micro-environmental sensitivity can be studied through heterogeneity of residual variance between animals. However, residual variance between animals is usually assumed to be homogeneous in traditional genetic evaluations. The aim of this study was to investigate genetic heterogeneity of residual variance by estimating variance components in residual variance for milk yield, somatic cell score, contents in milk (g/ dL) of 2 groups of milk fatty acids (i.e., saturated and unsaturated fatty acids), and the content in milk of one individual fatty acid (i.e., oleic acid, C18:1 cis-9), for first-parity Holstein cows in the Walloon Region of Belgium. A total of 146,027 test-day records from 26,887 cows in 747 herds were available. All cows had at least 3 records and a known sire. These sires had at least 10 cows with records and each herd × testday had at least 5 cows. The 5 traits were analyzed separately based on fixed lactation curve and random regression test-day models for the mean. Estimation of variance components was performed by running iteratively expectation maximization-REML algorithm by the implementation of double hierarchical generalized linear models. Based on fixed lactation curve test-day mean models, heritability for residual variances ranged between 1.01 × 10 −3 and 4.17 × 10 −3 for all traits. The genetic standard deviation in residual variance (i.e., approximately the genetic coefficient of variation of residual variance) ranged between 0.12 and 0.17. Therefore, some genetic variance in micro-environmental sensitivity existed in the Walloon Holstein dairy cattle for the 5 studied traits. The standard deviations due to herd × test-day and permanent environment in residual variance ranged between 0.36 and 0.45 for herd × testday effect and between 0.55 and 0.97 for permanent environmental effect. Therefore, nongenetic effects also contributed substantially to micro-environmental sensitivity. Addition of random regressions to the mean model did not reduce heterogeneity in residual variance and that genetic heterogeneity of residual variance was not simply an effect of an incomplete mean model.

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“…A particular concern is that animals with small V E would not have the capacity to react to unpredictable environments, i.e., have reduced plasticity. There is some evidence that plasticity and V E are positively correlated, i.e., that genotypes with higher V E have higher plasticity (Mulder et al 2013b;Tonsor et al 2013). Whether plastic genotypes with high V E or stable genotypes with small V E are better capable of handling unpredictable environments is under debate.…”

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confidence: 99%

Heritable Environmental Variance Causes Nonlinear Relationships Between Traits: Application to Birth Weight and Stillbirth of Pigs

2015

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There is recent evidence from laboratory experiments and analysis of livestock populations that not only the phenotype itself, but also its environmental variance, is under genetic control. Little is known about the relationships between the environmental variance of one trait and mean levels of other traits, however. A genetic covariance between these is expected to lead to nonlinearity between them, for example between birth weight and survival of piglets, where animals of extreme weights have lower survival. The objectives were to derive this nonlinear relationship analytically using multiple regression and apply it to data on piglet birth weight and survival. This study provides a framework to study such nonlinear relationships caused by genetic covariance of environmental variance of one trait and the mean of the other. It is shown that positions of phenotypic and genetic optima may differ and that genetic relationships are likely to be more curvilinear than phenotypic relationships, dependent mainly on the environmental correlation between these traits. Genetic correlations may change if the population means change relative to the optimal phenotypes. Data of piglet birth weight and survival show that the presence of nonlinearity can be partly explained by the genetic covariance between environmental variance of birth weight and survival. The framework developed can be used to assess effects of artificial and natural selection on means and variances of traits and the statistical method presented can be used to estimate trade-offs between environmental variance of one trait and mean levels of others.KEYWORDS genetics of environmental variance; optimum trait; fitness; complex traits; mass selection I N the classic model of quantitative genetics, the phenotype is assumed to be the sum of an effectively infinite number of genes and environmental effects; i.e., P ¼ G þ E (Falconer and Mackay 1996; Lynch and Walsh 1998). The phenotypic variance is the sum of the genetic (V G ) and environmental (V E ) variance. It is assumed that the environmental variance is homogeneous across genotypes, but recently there is evidence from laboratory experiments and analysis of livestock populations that not only the phenotype itself, but also its V E ; is under genetic control (Hill and Mulder 2010). This might be expressed as genetic heterogeneity in the withinindividual variance of a trait with repeat measurements, such as weight of individual piglets in a litter or total litter weight across parities, or as a difference in within-family variance for traits such as juvenile body weight expressed once.The median genetic coefficient of variation for V E (GCV VE ¼ s Av =V E ; where s Av is additive genetic variance in V E ) is 0.3 based on a review of 14 studies with estimates of genetic variance in V E primarily on livestock populations (Hill and Mulder 2010). This indicates that V E could be increased or decreased by 30% if changed by one genetic standard deviation. These analyses were mainly based on genetic analysis...

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Estimation of genetic variance for macro- and micro-environmental sensitivity using double hierarchical generalized linear models

Cited by 55 publications

References 47 publications

Behavioral idiosyncrasy reveals genetic control of phenotypic variability

Behavioral idiosyncrasy reveals genetic control of phenotypic variability

Genetic variance in micro-environmental sensitivity for milk and milk quality in Walloon Holstein cattle

Heritable Environmental Variance Causes Nonlinear Relationships Between Traits: Application to Birth Weight and Stillbirth of Pigs

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