Biology subjects

Valdar, W.

Publications and source records attributed to Valdar, W..

6 recordsLinked to original sources

bioRxiv2018

Mean-Variance QTL Mapping Identifies Novel QTL for Circadian Activity and Exploratory Behavior in Mice

We illustrate, through two case studies, that \"mean-variance QTL mapping\" can discover QTL that traditional interval mapping cannot. Mean-variance QTL mapping is based on the double generalized linear model, which elaborates on the standard linear model by incorporating not only a linear model for the data itself, but also a linear model for the residual variance. Its potential for use in QTL mapping has been described previously, but it remains underutilized, with certain key advantages undemonstrated until now. In the first case study, we use mean-variance QTL mapping to reanalyze a reduced complexity intercross of C57BL/6J and C57BL/6N mice examining circadian behavior and find a mean-controlling QTL for circadian wheel running activity that was not detected by traditional interval mapping. Mean-variance QTL mapping was more powerful than traditional interval mapping at the QTL because it accounted for the fact that mice homozygous for the C57BL/6N allele had less residual variance than the other mice. In the second case study, we reanalyze an intercross between C57BL/6J and C58/J mice examining anxiety-like behaviors, and identify a variance-controlling QTL for rearing behavior. This QTL was not identified in the original analysis because traditional interval mapping does not target variance QTL.

genetics↗

bioRxiv2018

Mean-Variance QTL Mapping on a Background of Variance Heterogeneity

Most QTL mapping approaches seek to identify \"mean QTL\", genetic loci that influence the phenotype mean, after assuming that all individuals in the mapping population have equal residual variance. Recent work has broadened the scope of QTL mapping to identify genetic loci that influence phenotype variance, termed \"variance QTL\", or some combination of mean and variance, which we term \"mean-variance QTL\". Even these approaches, however, fail to address situations where some other factor, be it an environmental factor or a distant genetic locus, influences phenotype variance. We term this situation \"background variance heterogeneity\" (BVH) and used simulation to explore its effects on the power and false positive rate of tests for mean QTL, variance QTL, and mean-variance QTL. Specifically, we compared traditional tests, linear regression for mean QTL and Levenes test for variance QTL, with tests more recently developed, namely Caos tests for all three types of QTL, and tests based on the double generalized linear model (DGLM), which, unlike the other approaches, explicitly models BVH. Simulations showed that, when used in conjunction with a permutation procedure, the DGLM-based tests accurately control false positive rate and are more powerful than the other tests. We also discovered that the rank-based inverse normal transform, often used to corral unruly phenotypes, can be used to mitigate the adverse effects of BVH in some circumstances. We applied the DGLM approach, which we term \"mean-variance QTL mapping\", to publicly available data on a mouse backcross of CAST/Ei into M16i and, after accommodating BVH driven by father, identified a new mean QTL for bodyweight at three weeks of age.

genetics↗

bioRxiv2018

Reciprocal F1 hybrids of two inbred mouse strains reveal parent-of-origin and perinatal diet effects on behavior and expression

Parent-of-origin effects (POEs) in mammals typically arise from maternal effects or from imprinting. Mutations in imprinted genes have been associated with psychiatric disorders, as well as with changes in a handful of animal behaviors. Nonetheless, POEs on complex traits such as behavior remain largely uncharacterized. Furthermore, although perinatal environmental exposures, such as nutrient deficiency, are known to modify both behavior and epigenetic effects generally, the architecture of environment-by-POE is almost completely unexplored. To study POE and environment-by-POE, we employ a relatively neglected but maximally powerful POE-detection system: a reciprocal F1 hybrid population. We exposed female NOD/ShiLtJxC57Bl/6J and C57Bl/6JxNOD/ShiLtJ mice, in utero, to one of four different diets, then after weaning recorded their whole-brain gene expression, as well as a set of behaviors that model psychiatric disease. Microarray expression data revealed an imprinting-enriched set of over a dozen genes subject to POE; the POE on the most significantly affected gene, Carmil1 (a.k.a. Lrrc16a), was validated using qPCR in the same and in a new set of mice. Several behaviors, especially locomotor behaviors, also showed POE. Interestingly, Bayesian mediation analysis suggests Carmil1 expression suppresses behavioral POE, and Airn suppresses POE on Carmil1 expression. A significant diet-by-POE was observed on one behavior, one imprinted gene, and over a dozen non-imprinted genes. Beyond our particular results, our study demonstrates a reciprocal F1 hybrid framework for studying POE and environment-by-POE on behavior.

genetics↗

bioRxiv2017

Diallel analysis reveals Mx1-dependent and Mx1-independent effects on response to influenza A virus in mice

1.Influenza A virus (IAV) is a respiratory pathogen that causes substantial morbidity and mortality during both seasonal and pandemic outbreaks. Infection outcomes in unexposed populations are affected by host genetics, but this host genetic architecture is not well understood. Here we obtain a broad view of how heritable factors affect a mouse model of response to IAV infection using an 8x8 diallel of the eight inbred founder strains of the Collaborative Cross (CC). Expanding on a prior statistical framework for modeling treatment response in diallels, we explore how a range of heritable effects modify acute host response to IAV through 4 days post-infection. Heritable effects in aggregate explained about 57% of the variance in IAV-induced weight loss. Much of this was attributable to a pattern of additive effects that became more prominent through day 4 post-infection and was consistent with previous reports of anti-influenza myxovirus resistance 1 (Mx1) polymorphisms segregating between these strains; the additive effects largely recapitulated haplotype effects observed at the Mx1 locus in a previous study of the incipient CC (pre-CC), and are also replicated here in a CC recombinant intercross (CC-RIX) population. Genetic dominance of protective Mx1 haplotypes was observed to differ by subspecies origin: relative to the domesticus null Mx1 allele, musculus acts dominantly whereas castaneus acts additively. After controlling for Mx1, heritable effects, though less distinct, accounted for about 34% of the phenotypic variance. Implications for future mapping studies are discussed.

genetics↗

bioRxiv2017

vqtl: An R package for Mean-Variance QTL Mapping

We present vqtl, an R package for mean-variance QTL mapping. This QTL mapping approach tests for genetic loci that influence the mean of the phenotype, termed mean QTL, the variance of the phenotype, termed variance QTL, or some combination of the two, termed mean-variance QTL. It is unique in its ability to correct for variance heterogeneity arising not only from the QTL itself but also from nuisance factors, such as sex, batch, or housing. This package provides functions to conduct genome scans, run permutations to assess the statistical significance, and make informative plots to communicate results. Because it is inter-operable with the popular qtl package and uses many of the same data structures and input patterns, it will be straightforward for geneticists to analyze future experiments with vqtl as well as re-analyze past experiments, possibly discovering new QTL.

genetics↗

bioRxiv2017

Dissecting the genetic architecture of shoot growth in carrot (Daucus carota L.) using a diallel mating design

Crop establishment in carrot (Daucus carota L.) is limited by slow seedling growth and delayed canopy closure, resulting in high management costs for weed control. Varieties with improved growth habit (i.e. larger canopy and increased shoot biomass) may help mitigate weed control, but the underlying genetics of these traits in carrot is unknown. This project used a diallel mating design coupled with recent Bayesian analytical methods to determine the genetic basis of carrot shoot growth. Six diverse carrot inbred lines with variable shoot size were crossed in WI in 2014. F1 hybrids, reciprocal crosses, and parental selfs were grown in a randomized complete block design (RCBD) with two blocks in CA (2015, 2016) and in WI (2015). Measurements included canopy height, canopy width, shoot biomass, and root biomass. General and specific combining abilities were estimated using Griffings Model I. In parallel, additive, inbreeding, epistatic, and maternal effects were estimated from a Bayesian linear mixed model, which is more robust to dealing with missing data, outliers, and theoretical constraints than traditional biometric methods. Both additive and non-additive effects significantly influenced shoot traits, with non-additive effects playing a larger role early in the growing season, when weed control is most critical. Results suggest that early season canopy growth and root size express hybrid vigor and can be improved through reciprocal recurrent selection.\n\nArticle SummaryBreeding for improved competitive ability is a priority in carrot, which suffers yield losses under weed pressure. However, improvement and in-depth genetic studies for these traits relies on knowledge of the underlying genetic architecture. This study estimated heritable and non-heritable components of carrot shoot growth from a diallel mating design using a Bayesian mixed model. Results directly contribute to improvement efforts by providing estimates of combining ability, identifying a useful tester line, and characterizing the genetic and non-genetic influences on traits for improved competitive ability in carrot.

genetics↗