Sex Modifies Genetic Effects on Residual Variance in Urinary Calcium Excretion in Rat (<i>Rattus norvegicus</i>)

Perry, Guy M. L.; Nehrke, Keith; Bushinsky, David A.; Reid, Robert J.; Lewandowski, Krista L.; Hueber, Paul; Scheinman, Steven J.

doi:10.1534/genetics.112.138909

Cited by 23 publications

(30 citation statements)

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“…Recently, Perry et al [9] did this in their analysis of vQTL in a rat F2, fitting first a linear model accounting for multiple covariates and single locus effects, and then performing a separate regression on the absolute values of residuals from the initial model. Months earlier, Struchalin et al [7] presented a method Squared residual Value Linear Modeling (SVLM), implemented in the VariABEL package (http://www.genabel.org), which fits a linear model to the squared residuals from an ordinary GWAS.…”

Section: Two-stage Approximations To Parametric Modelsmentioning

confidence: 99%

Recent developments in statistical methods for detecting genetic loci affecting phenotypic variability

2012

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A number of recent works have introduced statistical methods for detecting genetic loci that affect phenotypic variability, which we refer to as variability-controlling quantitative trait loci (vQTL). These are genetic variants whose allelic state predicts how much phenotype values will vary about their expected means. Such loci are of great potential interest in both human and non-human genetic studies, one reason being that a detected vQTL could represent a previously undetected interaction with other genes or environmental factors. The simultaneous publication of these new methods in different journals has in many cases precluded opportunity for comparison. We survey some of these methods, the respective trade-offs they imply, and the connections between them. The methods fall into three main groups: classical non-parametric, fully parametric, and semi-parametric two-stage approximations. Choosing between alternatives involves balancing the need for robustness, flexibility, and speed. For each method, we identify important assumptions and limitations, including those of practical importance, such as their scope for including covariates and random effects. We show in simulations that both parametric methods and their semi-parametric approximations can give elevated false positive rates when they ignore mean-variance relationships intrinsic to the data generation process. We conclude that choice of method depends on the trait distribution, the need to include non-genetic covariates, and the population size and structure, coupled with a critical evaluation of how these fit with the assumptions of the statistical model.

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Section: Two-stage Approximations To Parametric Modelsmentioning

confidence: 99%

Recent developments in statistical methods for detecting genetic loci affecting phenotypic variability

2012

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“…However, there is increasing evidence across species for genetic loci that affect the variance of phenotype (Queitsch et al 2002; JimenezGomez et al 2011;Ronnegard and Valdar 2011;Perry et al 2012;Shen et al 2012;Yang et al 2012). Recently we introduced the concept of expression variability QTL, or evQTL (Hulse and Cai 2013).…”

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confidence: 99%

Additive, Epistatic, and Environmental Effects Through the Lens of Expression Variability QTL in a Twin Cohort

et al. 2014

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The expression of a gene can vary across individuals in the general population, as well as between monozygotic twins. This variable expression is assumed to be due to the influence of both genetic and nongenetic factors. Yet little evidence supporting this assumption has been obtained from empirical data. In this study, we used expression data from a large twin cohort to investigate the influences of genetic and nongenetic factors on variable gene expression. We focused on a set of expression variability QTL (evQTL)-i.e., genetic loci associated with the variance, as opposed to the mean, of gene expression. We identified evQTL for 99, 56, and 79 genes in lymphoblastoid cell lines, skin, and fat, respectively. The differences in gene expression, measured by the relative mean difference (RMD), tended to be larger between pairs of dizygotic (DZ) twins than between pairs of monozygotic (MZ) twins, showing that genetic background influenced the expression variability. Furthermore, a more profound RMD was observed between pairs of MZ twins whose genotypes were associated with greater expression variability than the RMD found between pairs of MZ twins whose genotypes were associated with smaller expression variability. This suggests that nongenetic (e.g., environmental) factors contribute to the variable expression. Lastly, we demonstrated that the formation of evQTL is likely due to partial linkages between eQTL SNPs that are additively associated with the mean of gene expression; in most cases, no epistatic effect is involved. Our findings have implications for understanding divergent sources of gene expression variability.V ARIATION and variability are central concepts in biology (Hallgrímsson and Hall 2005). Although often used interchangeably in the scientific literature, the two are not synonymous. Variation refers to the differences among individuals, whereas variability refers to the potential of a population to vary (Wagner 1995;Wagner and Altenberg 1996). In many cases, greater phenotypic variability (e.g., transcriptional noise) is disadvantageous (Kemkemer et al. 2002;Bahar et al. 2006;Wang and Zhang 2011) unless it gives rise to greater organismal plasticity-first at the level of an individual organism and eventually at the population level. Genetic factors resulting in more variable phenotypes become favored when they enable a population to more effectively respond to environmental changes (Hill and Zhang 2004;Kaern et al. 2005;Acar et al. 2008;Zhang et al. 2009). Thus, understanding to what extent and in what ways genotypes influence phenotypic variability is of fundamental importance.Much effort has been focused on identifying genetic loci such as expression quantitative trait loci, or eQTL (Stranger et al. 2005(Stranger et al. , 2007Choy et al. 2008;Montgomery et al. 2010;Pickrell et al. 2010;Montgomery and Dermitzakis 2011), that affect the average value of a phenotype, while ignoring those that affect the variance of a phenotype. However, there is increasing evidence across species for genet...

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“…Where the estimated normalized 90% critical interval of hc1 overlapped with hccv1 , hc1 was undetectable; error variance for mean (µ) urinary calcium over the hccv1 area were 2.5% higher than outside the hccv1 area, a significant difference (p < 0.001) [16]. These rats, and a population of F 2 GHS × WKY intercrosses [9] in which we detected four other QTL for residuals in urinary calcium, were reared on a constant, fixed diet. Dietary controls might assist the detection of genes affecting residuals/ CV s; however, other work of ours does indicate moderate heritability for CV Cit in humans (30%) [30].…”

Section: Discussionmentioning

confidence: 99%

“…However, recent empirical works have also found genetic structure in residual variance, that component of phenotypic variance not explained by dependent genetic or non-genetic factors [1], in experimental crosses and selection lines of Drosophila , fish, and rodents [2,3,4,5,6,7,8,9]. …”

Section: Introductionmentioning

confidence: 99%

“…Swedish youths aged 2-18 had coefficients of variation ( CV s) of 30-40% for urinary calcium excretion [15]. Work of ours has identified several genetic loci associated with residual variation and the CV in urinary calcium excretion [9,16]. This phenomenon may be of medical importance; increased intra-individual variation in the volumes of individual glomeruli has been linked to comorbidities for chronic kidney disease [17].…”

Section: Introductionmentioning

confidence: 99%

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Weight, Age and Coefficients of Variation in Renal Solute Excretion

2013

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Background: Homoscedasticity (constant variance over axes or among statistical factors) is an integral assumption of most statistical analyses. However, a number of empirical studies in model organisms and humans demonstrate significant differences in residual variance (that component of phenotype unexplained by known factors) or intra-individual variation among genotypes. Our work suggests that renal traits may be particularly susceptible to randomization by genetic and non-genetic factors, including endogenous variables like age and weight. Methods: We tested associ-ations between age, weight and intra-individual variation in urinary calcium, citrate, chloride, creatinine, potassium, magnesium, sodium, ammonium, oxalate, phosphorus, sulfate, uric acid and urea nitrogen in 9,024 male and 6,758 female kidney stone patients. Coefficients of variation (CVs) were calculated for each individual for each solute from paired 24-hour urines. Analysis of CVs was corrected for inter-measurement collection variance in creatinine and urine volume. CVs for sodium and urea nitrogen were included to correct for dietary salt and protein. Results: Age was positively associated with individual CVs for calcium and negatively associated with CVs for potassium, ammonium and phosphorus (p_FDR < 0.01). Weight was associated with CVs for creatinine, magnesium and uric acid, and negatively associated with CVs for calcium, potassium and oxalate (p_FDR < 0.05). Conclusion: Intra-individual variation changes over age and weight axes for numerous urinary solutes. Changing residual variance over age and weight could cause bias in the detection or estimation of genetic or environmental effects. New methodologies may need to account for such residual unpredictability, especially in diverse collections.

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Sex Modifies Genetic Effects on Residual Variance in Urinary Calcium Excretion in Rat (Rattus norvegicus)

Cited by 23 publications

References 93 publications

Recent developments in statistical methods for detecting genetic loci affecting phenotypic variability

Recent developments in statistical methods for detecting genetic loci affecting phenotypic variability

Additive, Epistatic, and Environmental Effects Through the Lens of Expression Variability QTL in a Twin Cohort

Weight, Age and Coefficients of Variation in Renal Solute Excretion

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