Mapping hybrid defects in contact zones between incipient species can identify genomic regions contributing to reproductive isolation and reveal genetic mechanisms of speciation. The house mouse features a rare combination of sophisticated genetic tools and natural hybrid zones between subspecies. Male hybrids often show reduced fertility, a common reproductive barrier between incipient species. Laboratory crosses have identified sterility loci, but each encompasses hundreds of genes. We map genetic determinants of testis weight and testis gene expression using offspring of mice captured in a hybrid zone between M. musculus musculus and M. m. domesticus. Many generations of admixture enables high-resolution mapping of loci contributing to these sterility-related phenotypes. We identify complex interactions among sterility loci, suggesting multiple, non-independent genetic incompatibilities contribute to barriers to gene flow in the hybrid zone.
Behavioral individuality reveals genetic control of phenotypic variability
Julien F Ayroles, Sean M Buchanan, Chelsea Jenney, Kyobi Skutt-Kakaria, Jennifer Grenier, Andrew G Clark, Daniel L Hartl, Benjamin L de Bivort
Variability is ubiquitous in nature and a fundamental feature of complex systems. Few studies, however, have investigated variance itself as a trait under genetic control. By focusing primarily on trait means and ignoring the effect of alternative alleles on trait variability, we may be missing an important axis of genetic variation contributing to phenotypic differences among individuals. To study genetic effects on individual-to-individual phenotypic variability (or intragenotypic variability), we used a panel of Drosophila inbred lines and focused on locomotor handedness, in an assay optimized to measure variability. We discovered that some lines had consistently high levels of intragenotypic variability among individuals while others had low levels. 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 implicated 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 coordination6. We have validated these results using genetic deficiencies, null alleles, and inducible RNAi transgenes. This study reveals the constellation of phenotypes that can arise from a single genotype and it 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.
Jennifer Lachowiec, Xia Shen, Christine Queitsch, Örjan Carlborg
Efforts to identify loci underlying complex traits generally assume that most genetic variance is additive. This is despite the fact that non-additive genetic effects, such as epistatic interactions and developmental noise, are also likely to make important contributions to the phenotypic variability. Analyses beyond additivity require additional care in the design and collection of data, and introduce significant analytical and computational challenges in the statistical analyses. Here, we have conducted a study that, by focusing on a model complex trait that allows precise phenotyping across many replicates and by applying advanced analytical tools capable of capturing epistatic interactions, overcome these challenges. Specifically, we examined the genetic determinants of Arabidopsis thaliana root length, considering both trait mean and variance. Analysis of narrow-and broad-sense heritability of mean root length identified a large contribution of non-additive variation and a low contribution of additive variation. Also, no loci were found to contribute to mean root length using a standard additive model based genome-wide association analysis (GWAS). We could, however, identify one locus regulating developmental noise and seven loci contributing to root-length through epistatic interactions, and four of these were also experimentally confirmed. The candidate locus associated with root length variance contains a candidate gene that, when mutated, appears to decrease developmental noise. This is particularly interesting as most other known noise regulators in multicellular organisms increase noise when mutated. The mutant analysis of candidate genes within the seven epistatic loci implicated four genes in root development, including three without previously described root phenotypes. In summary, we identify several novel genes affecting root development, demonstrate the benefits of advanced analytical tools to study the genetic determinants of complex traits, and show that epistatic interactions can be a major determinant of complex traits in A. thaliana.
Joshua G. Schraiber, Michael J. Landis
When models of quantitative genetic variation are built from population ge- netic first principles, several assumptions are often made. One of the most important assumptions is that traits are controlled by many genes of small effect. This leads to a prediction of a Gaussian trait distribution in the population, via the Central Limit Theorem. Since these biological assumptions are often unknown or untrue, we charac- terized how finite numbers of loci or large mutational effects can impact the sampling distribution of a quantitative trait. To do so, we developed a neutral coalescent-based framework, allowing us to experiment freely with the number of loci and the underlying mutational model. Through both analytical theory and simulation we found the nor- mality assumption was highly sensitive to the details of the mutational process, with the greatest discrepancies arising when the number of loci was small or the mutational kernel was heavy-tailed. In particular, fat-tailed mutational kernels result in multimodal sampling distributions for any number of loci. Since selection models and robust neutral models may produce qualitatively similar sampling distributions, we advise extra caution should be taken when interpreting model-based results for poorly understood systems of quantitative traits.
Annalise Paaby, Amelia White, David Riccardi, Kristin Gunsalus, Fabio Piano, Matthew Rockman
Conditionally functional mutations are an important class of natural genetic variation, yet little is known about their prevalence in natural populations or their contribution to disease risk. Here, we describe a vast reserve of cryptic genetic variation, alleles that are normally silent but which affect phenotype when the function of other genes is perturbed, in the gene networks of C. elegans embryogenesis. We find evidence that cryptic-effect loci are ubiquitous and segregate at intermediate frequencies in the wild. The cryptic alleles demonstrate low developmental pleiotropy, in that specific, rather than general, perturbations are required to reveal them. Our findings underscore the importance of genetic background in characterizing gene function and provide a model for the expression of conditionally functional effects that may be fundamental in basic mechanisms of trait evolution and the genetic basis of disease susceptibility.
Annalise B. Paaby, Alan O. Bergland, Emily L. Behrman, Paul S. Schmidt
Finding the specific nucleotides that underlie adaptive variation is a major goal in evolutionary biology, but polygenic traits pose a challenge because the complex genotype-phenotype relationship can obscure the effects of individual alleles. However, natural selection working in large wild populations can shift allele frequencies and indicate functional regions of the genome. Previously, we showed that the two most common alleles of a complex amino acid insertion-deletion polymorphism in the Drosophila insulin receptor show independent, parallel clines in frequency across the North American and Australian continents. Here, we report that the cline is stable over at least a five-year period and that the polymorphism also demonstrates temporal shifts in allele frequency concurrent with seasonal change. We tested the alleles for effects on levels of insulin signaling, fecundity, development time, body size, stress tolerance, and lifespan. We find that the alleles are associated with predictable differences in these traits, consistent with patterns of Drosophila life history variation across geography that likely reflect adaptation to the heterogeneous climatic environment. These results implicate insulin signaling as a major mediator of life history adaptation in Drosophila, and suggest that life history tradeoffs can be explained by extensive pleiotropy at a single locus.
Stephen D.H. Hsu
(Submitted on 14 Aug 2014)
How do genes affect cognitive ability or other human quantitative traits such as height or disease risk? Progress on this challenging question is likely to be significant in the near future. I begin with a brief review of psychometric measurements of intelligence, introducing the idea of a “general factor” or g score. The main results concern the stability, validity (predictive power), and heritability of adult g. The largest component of genetic variance for both height and intelligence is additive (linear), leading to important simplifications in predictive modeling and statistical estimation. Due mainly to the rapidly decreasing cost of genotyping, it is possible that within the coming decade researchers will identify loci which account for a significant fraction of total g variation. In the case of height analogous efforts are well under way. I describe some unpublished results concerning the genetic architecture of height and cognitive ability, which suggest that roughly 10k moderately rare causal variants of mostly negative effect are responsible for normal population variation. Using results from Compressed Sensing (L1-penalized regression), I estimate the statistical power required to characterize both linear and nonlinear models for quantitative traits. The main unknown parameter s (sparsity) is the number of loci which account for the bulk of the genetic variation. The required sample size is of order 100s, or roughly a million in the case of cognitive ability.