Kevin Bullaughey

Revisiting an Old Riddle: What Determines Genetic Diversity Levels within Species?

With the recent revolution in sequencing, we revisit the unresolved question of what influences the range and values of genetic diversity across taxa.

The Timing of Selection at the Human FOXP2 Gene

Krause J, Lalueza-Fox C, Orlando L, et al. recently examined patterns of genetic variation at FOXP2 in 2 Neanderthals. This gene is of particular interest because it is involved in speech and language and was previously shown to harbor the signature of recent positive selection. The authors found the same 2 amino acid substitutions in Neanderthals as in modern humans. Assuming that these sites were the targets of selection and no interbreeding between the 2 groups, they concluded that selection at FOXP2 occurred before the populations split, over 300 thousand years ago. Here, we show that the data are unlikely under this scenario but may instead be consistent with low rates of gene flow between modern humans and Neanderthals. We also collect additional data and introduce a modeling framework to estimate levels of modern human contamination of the Neanderthal samples. We find that, depending on the assumptions, additional control experiments may be needed to rule out contamination at FOXP2.

Shifts in the intensity of purifying selection: An analysis of genome-wide polymorphism data from two closely related yeast species

How much does the intensity of purifying selection vary among populations and species? How uniform are the shifts in selective pressures across the genome? To address these questions, we took advantage of a recent, whole-genome polymorphism data set from two closely related species of yeast, Saccharomyces cerevisiae and S. paradoxus, paying close attention to the population structure within these species. We found that the average intensity of purifying selection on amino acid sites varies markedly among populations and between species. As expected in the presence of extensive weakly deleterious mutations, the effect of purifying selection is substantially weaker on single nucleotide polymorphisms (SNPs) segregating within populations than on SNPs fixed between population samples. Also in accordance with a Nearly Neutral model, the variation in the intensity of purifying selection across populations corresponds almost perfectly to simple measures of their effective size. As a first step toward understanding the processes generating these patterns, we sought to tease apart the relative importance of systematic, genome-wide changes in the efficacy of selection, such as those expected from demographic processes and of gene-specific changes, which may be expected after a shift in selective pressures. For that purpose, we developed a new model for the evolution of purifying selection between populations and inferred its parameters from the genome-wide data using a likelihood approach. We found that most, but not all changes seem to be explained by systematic shifts in the efficacy of selection. One population, the sake-derived strains of S. cerevisiae, however, also shows extensive gene-specific changes, plausibly associated with domestication. These findings have important implications for our understanding of purifying selection as well as for estimates of the rate of molecular adaptation in yeast and in other species.

Expression quantitative trait loci detected in cell lines are often present in primary tissues

Expression quantitative trait loci (eQTL) mapping is a powerful tool for identifying genetic regulatory variation. However, at present, most eQTLs in humans were identified using gene expression data from cell lines, and it remains unknown whether these eQTLs also have a regulatory function in other expression contexts, such as human primary tissues. Here we investigate this question using a targeted strategy. Specifically, we selected a subset of large-effect eQTLs identified in the HapMap lymphoblastoid cell lines, and examined the association of these eQTLs with gene expression levels across individuals in five human primary tissues (heart, kidney, liver, lung and testes). We show that genotypes at the eQTLs we selected are often predictive of variation in gene expression levels in one or more of the five primary tissues. The genotype effects in the primary tissues are consistently in the same direction as the effects inferred in the cell lines. Additionally, a number of the eQTLs we tested are found in more than one of the tissues. Our results indicate that functional studies in cell lines may uncover a substantial amount of genetic variation that affects gene expression levels in human primary tissues.

No effect of recombination on the efficacy of natural selection in primates

Population genetic theory suggests that natural selection should be less effective in regions of low recombination, potentially leading to differences in rates of adaptation among recombination environments. To date, this prediction has mainly been tested in Drosophila, with somewhat conflicting results. We investigated the association between human recombination rates and adaptation in primates, by considering rates of protein evolution (measured by d(N)/d(S)) between human, chimpanzee, and rhesus macaque. We found no correlation between either broad- or fine-scale rates of recombination and rates of protein evolution, once GC content is taken into account. Moreover, genes in regions of very low recombination, which are expected to show the most pronounced reduction in the efficacy of selection, do not evolve at a different rate than other genes. Thus, there is no evidence for differences in the efficacy of selection across recombinational environments. An interesting implication is that indirect selection for recombination modifiers has probably been a weak force in primate evolution.

Nonlinear Fitness Consequences of Variation in Expression Level of a Eukaryotic Gene

Levels of gene expression show considerable variation in eukaryotes, but no fine-scale maps have been made of the fitness consequences of such variation in controlled genetic backgrounds and environments. To address this, we assayed fitness at many levels of up- and down-regulated expression of a single essential gene, LCB2, involved in sphingolipid synthesis in budding yeast Saccharomyces cerevisiae. Reduced LCB2 expression rapidly decreases cellular fitness, yet increased expression has little effect. The wild-type expression level is therefore perched on the edge of a nonlinear fitness cliff. LCB2 is upregulated when cells are exposed to osmotic stress; consistent with this, the entire fitness curve is shifted upward to higher expression under osmotic stress, illustrating the selective force behind gene regulation. Expression levels of LCB2 are lower in wild yeast strains than in the experimental lab strain, suggesting that higher levels in the lab strain may be idiosyncratic. Reports indicate that the effect sizes of alleles contributing to variation in complex phenotypes differ among environments and genetic backgrounds; our results suggest that such differences may be explained as simple shifts in the position of nonlinear fitness curves.

A population genetic interpretation of GWAS findings for human quantitative traits

Human genome-wide association studies (GWASs) are revealing the genetic architecture of anthropomorphic and biomedical traits, i.e., the frequencies and effect sizes of variants that contribute to heritable variation in a trait. To interpret these findings, we need to understand how genetic architecture is shaped by basic population genetics processes—notably, by mutation, natural selection, and genetic drift. Because many quantitative traits are subject to stabilizing selection and because genetic variation that affects one trait often affects many others, we model the genetic architecture of a focal trait that arises under stabilizing selection in a multidimensional trait space. We solve the model for the phenotypic distribution and allelic dynamics at steady state and derive robust, closed-form solutions for summary statistics of the genetic architecture. Our results provide a simple interpretation for missing heritability and why it varies among traits. They predict that the distribution of variances contributed by loci identified in GWASs is well approximated by a simple functional form that depends on a single parameter: the expected contribution to genetic variance of a strongly selected site affecting the trait. We test this prediction against the results of GWASs for height and body mass index (BMI) and find that it fits the data well, allowing us to make inferences about the degree of pleiotropy and mutational target size for these traits. Our findings help to explain why the GWAS for height explains more of the heritable variance than the similarly sized GWAS for BMI and to predict the increase in explained heritability with study sample size. Considering the demographic history of European populations, in which these GWASs were performed, we further find that most of the associations they identified likely involve mutations that arose shortly before or during the Out-of-Africa bottleneck at sites with selection coefficients around s = 10−3.Genome-wide association studies (GWAS) in humans are revealing the genetic architecture of biomedical, life history and anthropomorphic traits, i.e., the frequencies and effect sizes of variants contributing to heritable variation in a trait. To interpret these findings, we need to understand how genetic architecture is shaped by basic population genetics processes - notably, by mutation, natural selection and genetic drift. Because many quantitative traits are subject to stabilizing selection and genetic variation that affects one trait often affects many others, we model the genetic architecture of a focal trait that arises under stabilizing selection in a multi-dimensional trait space. We solve the model for the phenotypic distribution and allelic dynamics at steady state and derive robust, closed form solutions for summaries of genetic architecture. Our results suggest that the distribution of genetic variance among the loci discovered in GWAS take a simple form that depends on one evolutionary parameter, and provide a simple interpretation for missing heritability and why it varies among traits. We test our predictions against the results of GWAS for height and body mass index (BMI) and find that they fit the data well, allowing us to make inferences about the degree of pleiotropy and the mutational target size. Our findings help to understand why GWAS for height explain more of the heritable variance than similarly-sized GWAS for BMI, and to predict how future increases in sample size will translate into explained heritability.

MULTIDIMENSIONAL ADAPTIVE EVOLUTION OF A FEED‐FORWARD NETWORK AND THE ILLUSION OF COMPENSATION

When multiple substitutions affect a trait in opposing ways, they are often assumed to be compensatory, not only with respect to the trait, but also with respect to fitness. This type of compensatory evolution has been suggested to underlie the evolution of protein structures and interactions, RNA secondary structures, and gene regulatory modules and networks. The possibility for compensatory evolution results from epistasis. Yet if epistasis is widespread, then it is also possible that the opposing substitutions are individually adaptive. I term this possibility an adaptive reversal. Although possible for arbitrary phenotype‐fitness mappings, it has not yet been investigated whether such epistasis is prevalent in a biologically realistic setting. I investigate a particular regulatory circuit, the type I coherent feed‐forward loop, which is ubiquitous in natural systems and is accurately described by a simple mathematical model. I show that such reversals are common during adaptive evolution, can result solely from the topology of the fitness landscape, and can occur even when adaptation follows a modest environmental change and the network was well adapted to the original environment. The possibility of adaptive reversals warrants a systems perspective when interpreting substitution patterns in gene regulatory networks.

Contribution of Transcription Factor Binding Site Motif Variants to Condition-Specific Gene Expression Patterns in Budding Yeast

It is now experimentally well known that variant sequences of a cis transcription factor binding site motif can contribute to differential regulation of genes. We characterize the relationship between motif variants and gene expression by analyzing expression microarray data and binding site predictions. To accomplish this, we statistically detect motif variants with effects that differ among environments. Such environmental specificity may be due to either affinity differences between variants or, more likely, differential interactions of TFs bound to these variants with cofactors, and with differential presence of cofactors across environments. We examine conservation of functional variants across four Saccharomyces species, and find that about a third of transcription factors have target genes that are differentially expressed in a condition-specific manner that is correlated with the nucleotide at variant motif positions. We find good correspondence between our results and some cases in the experimental literature (Reb1, Sum1, Mcm1, and Rap1). These results and growing consensus in the literature indicates that motif variants may often be functionally distinct, that this may be observed in genomic data, and that variants play an important role in condition-specific gene regulation.