Association mapping reveals the role of mutation-selection balance in the maintenance of genomic variation for gene expression.

Association mapping reveals the role of mutation-selection balance in the maintenance of genomic variation for gene expression.

Emily Josephs , Young Wha Lee , John R. Stinchcombe , Stephen I Wright

The evolutionary forces that maintain genetic variation for quantitative traits within populations remain unknown. One hypothesis suggests that variation is maintained by a balance between new mutations and their removal by selection and drift. Theory predicts that this mutation-selection balance will result in an excess of low-frequency variants and a negative correlation between minor allele frequency and selection coefficients. Here, we test these predictions using the genetic loci associated with total expression variation (‘eQTLs’) and allele-specific expression variation (‘aseQTLs’) mapped within a single population of the plant Capsella grandiflora. In addition to finding eQTLs and aseQTLs for a large fraction of genes, we show that alleles at these loci are rarer than expected and exhibit a negative correlation between effect size and frequency. Overall, our results show that mutation-selection balance is the dominant contributor to genomic variation for expression within a single, outcrossing population.

Extensive de novo mutation rate variation between individuals and across the genome of Chlamydomonas reinhardtii

Extensive de novo mutation rate variation between individuals and across the genome of Chlamydomonas reinhardtii

Rob W Ness , Andrew D Morgan , Radhakrishnan B Vasanthakrishnan , Nick Colegrave , Peter D Keightley

Describing the process of spontaneous mutation is fundamental for understanding the genetic basis of disease, the threat posed by declining population size in conservation biology, and in much evolutionary biology. However, directly studying spontaneous mutation is difficult because of the rarity of de novo mutations. Mutation accumulation (MA) experiments overcome this by allowing mutations to build up over many generations in the near absence of natural selection. In this study, we sequenced the genomes of 85 MA lines derived from six genetically diverse wild strains of the green alga Chlamydomonas reinhardtii. We identified 6,843 spontaneous mutations, more than any other study of spontaneous mutation. We observed seven-fold variation in the mutation rate among strains and that mutator genotypes arose, increasing the mutation rate dramatically in some replicates. We also found evidence for fine-scale heterogeneity in the mutation rate, driven largely by the sequence flanking mutated sites, and by clusters of multiple mutations at closely linked sites. There was little evidence, however, for mutation rate heterogeneity between chromosomes or over large genomic regions of 200Kbp. Using logistic regression, we generated a predictive model of the mutability of sites based on their genomic properties, including local GC content, gene expression level and local sequence context. Our model accurately predicted the average mutation rate and natural levels of genetic diversity of sites across the genome. Notably, trinucleotides vary 17-fold in rate between the most mutable and least mutable sites. Our results uncover a rich heterogeneity in the process of spontaneous mutation both among individuals and across the genome.

Phen-Gen: Combining Phenotype and Genotype to Analyze Rare Disorders

Phen-Gen: Combining Phenotype and Genotype to Analyze Rare Disorders

Asif Javed , Saloni Agrawal , Pauline Ng

We introduce Phen-Gen, a method which combines patient’s disease symptoms and sequencing data with prior domain knowledge to identify the causative gene(s) for rare disorders. Simulations reveal that the causal variant is ranked first in 88% cases when it is coding; which is 52% advantage over a genotype-only approach and outperforms existing methods by 13-58%. If disease etiology is unknown, the causal variant is assigned top-rank in 71% of simulations.

Catch me if you can: Adaptation from standing genetic variation to a moving phenotypic optimum

Catch me if you can: Adaptation from standing genetic variation to a moving phenotypic optimum

Sebastian Matuszewski , Joachim Hermisson , Michael Kopp
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Adaptation lies at the heart of Darwinian evolution. Accordingly, numerous studies have tried to provide a formal framework for the description of the adaptive process. Out of these, two complementary modelling approaches have emerged: While so-called adaptive-walk models consider adaptation from the successive fixation of de-novo mutations only, quantitative genetic models assume that adaptation proceeds exclusively from pre-existing standing genetic variation. The latter approach, however, has focused on short-term evolution of population means and variances rather than on the statistical properties of adaptive substitutions. Our aim is to combine these two approaches by describing the ecological and genetic factors that determine the genetic basis of adaptation from standing genetic variation in terms of the effect-size distribution of individual alleles. Specifically, we consider the evolution of a quantitative trait to a gradually changing environment. By means of analytical approximations, we derive the distribution of adaptive substitutions from standing genetic variation, that is, the distribution of the phenotypic effects of those alleles from the standing variation that become fixed during adaptation. Our results are checked against individual-based simulations. We find that, compared to adaptation from de-novo mutations, (i) adaptation from standing variation proceeds by the fixation of more alleles of small effect; (ii) populations that adapt from standing genetic variation can traverse larger distances in phenotype space and, thus, have a higher potential for adaptation if the rate of environmental change is fast rather than slow.

Quality assessment for different haplotyping methods and GWAS sensitivity to phasing errors

Quality assessment for different haplotyping methods and GWAS sensitivity to phasing errors

Giovanni Busonera , Marco Cogoni , Gianluigi Zanetti

In this report we present a multimarker association tool (Flash) based on a novel algorithm to generate haplotypes from raw genotype data. It belongs to the entropy minimization class of methods and is composed of a two stage deterministic – heuristic part and of a optional stochastic optimization. This algorithm is able to scale up well to handle huge datasets with faster performance than the competing technologies such as BEAGLE and MACH while maintaining a comparable accuracy. A quality assessment of the results is carried out by comparing the switch error. Finally, the haplotypes are used to perform a haplotype-based Genome-wide Association Study (GWAS). The association results are compared with a multimarker and a single SNP association test performed with Plink. Our experiments confirm that the multimarker association test can be more powerful than the single SNP one as stated in the literature. Moreover, Flash and Plink show similar results for the multimarker association test but Flash speeds up the computation time of about an order of magnitude using 5 SNP size haplotypes.

Pervasive adaptation of gene expression in Drosophila

Pervasive adaptation of gene expression in Drosophila

Armita Nourmohammad, Joachim Rambeau, Torsten Held, Johannes Berg, Michael Lassig
(Submitted on 23 Feb 2015)

Gene expression levels are important molecular quantitative traits that link genotypes to molecular functions and fitness. In Drosophila, population-genetic studies in recent years have revealed substantial adaptive evolution at the genomic level. However, the evolutionary modes of gene expression have remained controversial. Here we present evidence that adaptation dominates the evolution of gene expression levels in flies. We show that 64% of the observed expression divergence across seven Drosophila species are adaptive changes driven by directional selection. Our results are derived from the variation of expression within species and the time-resolved divergence across a family of related species, using a new inference method for selection. We identify functional classes of adaptively regulated genes, as well as sex-specific adaptation occurring predominantly in males. Our analysis opens a new avenue to map system-wide selection on molecular quantitative traits independently of their genetic basis.

Calibrating the Human Mutation Rate via Ancestral Recombination Density in Diploid Genomes

Calibrating the Human Mutation Rate via Ancestral Recombination Density in Diploid Genomes

Mark Lipson , Po-Ru Loh , Sriram Sankararaman , Nick Patterson , Bonnie Berger , David Reich

The human mutation rate is an essential parameter for studying the evolution of our species, interpreting present-day genetic variation, and understanding the incidence of genetic disease. Nevertheless, our current estimates of the rate are uncertain. Classical methods based on sequence divergence have yielded significantly larger values than more recent approaches based on counting de novo mutations in family pedigrees. Here, we propose a new method that uses the fine-scale human recombination map to calibrate the rate of accumulation of mutations. By comparing local heterozygosity levels in diploid genomes to the genetic distance scale over which these levels change, we are able to estimate a long-term mutation rate averaged over hundreds or thousands of generations. We infer a rate of 1.65 +/- 0.10 x 10^(-8) mutations per base per generation, which falls in between phylogenetic and pedigree-based estimates, and we suggest possible mechanisms to reconcile our estimate with previous studies. Our results support intermediate-age divergences among human populations and between humans and other great apes.