The evolutionary and ecological forces that govern microbial biogeography remain poorly characterized. We examined the biogeography of Streptomyces at regional spatial scales to identify factors that govern extant patterns of microbial diversity within the genus. Streptomyces are spore forming filamentous bacteria that are widespread in soil and are a predominant source of antibiotics. We applied population genetic approaches to analyze geographic and genetic population structure in six phylogroups of Streptomyces identified from a geographically explicit culture collection. Streptomyces strains were isolated from soils associated with perennial grass habitats sampled across a spatial scale of more than 5,000 km. We find that Streptomyces allelic diversity correlates with geographic distance and that gene flow between sites is constrained by latitude. In addition, we find that phylogroup nucleotide diversity is negatively correlated with latitude. These observations are consistent with the hypothesis that historical demographic processes have influenced the contemporary biogeography of Streptomyces.

We have previously established two lines of rat for studying the functional basis of aerobic exercise capacity (AEC) and its impact on metabolic health. The two lines, high capacity runners (HCR) and low capacity runners (LCR), have been selectively bred for high and low intrinsic AEC, respectively. They were started from the same genetically heterogeneous population and have now diverged in both AEC and many other physiological measures, including weight, body composition, blood pressure, body mass index, lung capacity, lipid and glucose metabolism, and natural life span. In order to exploit this rat model to understand the genomic regions under differential selection within the two lines, we used SNP genotype and whole genome pooled sequencing data to identify signatures of selection using three different statistics: runs of homozygosity, fixation index, and aberrant allele frequency spectrum, and developed a composite score that combined the three signals. We found that several pathways (ATP transport and fatty acid metabolism) are enriched in regions under differential selection. The candidate genes and pathways under selection will be integrated with the previous mRNA expression data and future F2 QTL results for a multi-omics approach to understanding the biological basis of AEC and metabolic traits.

Recent advances in Web technology and information sciences, especially the development of knowledge representation systems—ontology languages (Web Ontology Language) and syntaxes (Manchester syntax)—are now infiltrating the world of insect biodiversity research. Data generated from taxonomic revisions, comparative morphology studies, and other enterprises now have the potential to be shared broadly and to be computed across—i.e., they are rendered semantic—in order to address questions relevant to multiple domains in the life sciences. In this chapter we describe the philosophy behind these new tools, the mechanisms by which they operate, and the real and future benefits of the semantic representation of phenotype data (e.g., standardization of terminology). We provide examples using real data and describe some limitations of semantic phenotype annotations.

While identifying routes of transmission during an infectious disease outbreak was traditionally conducted through exhaustive contact tracing efforts, the increasing availability of pathogen sequencing has provided a new resource with which one can identify plausible routes of infection. However, while transmission clusters can be identified using single genome sequences, individual transmission routes remain relatively uncertain. Deep sequence data may provide additional information where single genomes lack sufficient resolution – presence of shared minor variants can suggest epidemiological linkage when observed between multiple hosts. In this study we formalize shared variant methods to reconstruct the transmission tree in an outbreak, and using simulated outbreak data, we quantify the improved accuracy when compared with analogous single genome approaches. Furthermore we propose a hybrid approach, drawing information from both deep sequence and single genome data. Our simulation studies demonstrate the superior performance of transmission tree identification methods using shared variants in most settings. Application of these methods to deep sequence data collected during the 2014 Sierra Leone Ebola epidemic demonstrates the ability to identify plausible transmission routes without any additional data. The methods we describe should become a common step in outbreak investigations and epidemiological analyses once the collection of deep sequence data becomes increasingly widespread.

In all populations, as the time runs, crossovers break apart ancestor haplotypes, forming smaller blocks at each generation. Some blocks, and eventually all of them, become identical by descent because of the genetic drift. We have in this paper developed and benchmarked a theoretical prediction of the mean length of such blocks and used it to study a simple population model assuming panmixia, no selfing and drift as the only evolutionary pressure. Besides, we have on the one hand derived, for any user defined error threshold, the range of the parameters this prediction is reliable for, and on the other hand shown that the mean length remains constant over time in ideally large populations.

Genome-wide Association Studies (GWAS) result in millions of summary statistics (“z-scores”) for single nucleotide polymorphism (SNP) associations with phenotypes. These rich datasets afford deep insights into the nature and extent of genetic contributions to complex phenotypes such as psychiatric disorders, which are understood to have substantial genetic components that arise from very large numbers of SNPs. The complexity of the datasets, however, poses a significant challenge to maximizing their utility. This is reflected in a need for better understanding the landscape of z-scores, as such knowledge would enhance causal SNP and gene discovery, help elucidate mechanistic pathways, and inform future study design. Here we present a parsimonious methodology for modeling effect sizes and replication probabilities that does not require raw genotype data, relying only on summary statistics from GWAS substudies, and a scheme allowing for direct empirical validation. We show that modeling z-scores as a mixture of Gaussians is conceptually appropriate, in particular taking into account ubiquitous non-null effects that are likely in the datasets due to weak linkage disequilibrium with causal SNPs. The four-parameter model allows for estimating the degree of polygenicity of the phenotype — the proportion of SNPs (after uniform pruning, so that large LD blocks are not over-represented) likely to be in strong LD with causal/mechanistically associated SNPs — and predicting the proportion of chip heritability explainable by genome wide significant SNPs in future studies with larger sample sizes. We apply the model to recent GWAS of schizophrenia (N=82,315) and additionally, for purposes of illustration, putamen volume (N=12,596), with approximately 9.3 million SNP z-scores in both cases. We show that, over a broad range of z-scores and sample sizes, the model accurately predicts expectation estimates of true effect sizes and replication probabilities in multistage GWAS designs. We estimate the degree to which effect sizes are over-estimated when based on linear regression association coefficients. We estimate the polygenicity of schizophrenia to be 0.037 and the putamen to be 0.001, while the respective sample sizes required to approach fully explaining the chip heritability are 10⁶ and 10⁵. The model can be extended to incorporate prior knowledge such as pleiotropy and SNP annotation. The current findings suggest that the model is applicable to a broad array of complex phenotypes and will enhance understanding of their genetic architectures.

Estimation of heritability is fundamental in genetic studies. In recent years, heritability estimation using linear mixed models (LMMs) has gained popularity, because these estimates can be obtained from unrelated individuals collected in genome wide association studies. Typically, heritability estimation under LMMs uses either the maximum likelihood (ML) or the restricted maximum likelihood (REML) approach. Existing methods for the construction of confidence intervals and estimators of standard errors for both ML and REML rely on asymptotic properties. However, these assumptions are often violated due to the bounded parameter space, statistical dependencies, and limited sample size, leading to biased estimates, and inflated or deflated confidence intervals. Here, we show that often the probability that the genetic component is estimated as zero is high even when the true heritability is bounded away from zero, emphasizing the need for accurate confidence intervals. We further show that the estimation of confidence intervals by state-of-the-art methods is highly inaccurate, especially when the true heritability is either relatively low or relatively high. Such biases are present, for example, in estimates of heritability of gene expression in the GTEx study, and of lipid profiles in the LURIC study. We propose a computationally efficient method, Accurate LMM-Based confidence Intervals (ALBI), for the estimation of the distribution of the heritability estimator, and for the construction of accurate confidence intervals. Our method can be used as an add-on to existing methods for heritability and variance components estimation, such as GCTA, FaST-LMM, GEMMA, or EMMA. ALBI is available at http://www.cs.tau.ac.il/~heran/cozygene/software/albi.html.

Methods that infer site-specific dN/dS, the ratio of nonsynonymous to synonymous substi- tution rates, from coding data have been developed primarily to identify positively selected sites (dN/dS > 1). As a consequence, it is largely unknown how well different inference methods can infer dN/dS point estimates at individual sites. In particular, dN/dS may be estimated using either a one-rate approach, where dN/dS is parameterized as a single parameter, or a two-rate approach, in which dN and dS are estimated separately. While some have suggested that the two-rate paradigm may be preferred for positive-selection inference, the relative merits of these two paradigms for site-specific dN/dS estimation remain largely untested. Here, we systematically assess how accurately several popular inference frameworks infer site-specific dN/dS values using alignments simulated within a mutation-selection framework rather than within a dN/dS-based framework. As mutation-selection models describe long-term evolutionary constraints, our simulation approach further allows us to study under what conditions inferred dN/dS captures the underlying equilibrium evolutionary process. We find that one-rate inference models universally outperform two-rate models. Surprisingly, we recover this result even for data simulated with codon bias (i.e., dS varies among sites). Therefore, even when extensive dS variation exists, modeling this variation substantially reduces accuracy. We additionally find that high levels of divergence among sequences, rather than the number of sequences in the alignment, are more critical for obtaining precise point estimates. We conclude that inference methods which model dN/dS with a single parameter are the preferred choice for estimating reliable site-specific dN/dS ratios.

Sexual antagonism (SA) arises when male and female phenotypes are under opposing selection, yet genetically correlated. Until resolved, antagonism limits evolution towards optimal sex-specific phenotypes. Despite its importance for sex-specific adaptation and existing theory, the dynamics of SA resolution are not well understood empirically. Here, we present data from Drosophila melanogaster, compatible with a resolution of SA. We compared two independent replicates of the ‘LHM’ population in which SA had previously been described. Both had been maintained under identical, controlled conditions, and separated for <250 generations. Although heritabilities of male and female fitness were similar, the inter-sexual genetic correlation differed significantly, being negative in one replicate (indicating SA) but close to zero in the other. Using population sequencing, we show that phenotypic differences were associated with population divergence in allele frequencies at non-random loci across the genome. Large frequency changes were more prevalent in the population without SA and were enriched at loci mapping to genes previously shown to have a sexually antagonistic relationships between expression and fitness. Our data suggest that rapid evolution towards SA resolution has occurred in one of the populations and open avenues towards studying the genetics of SA and its resolution.

Recently, LD Score regression1 has been proposed as a computationally fast method to contrast confounding biases with polygenicity and to quantify their contribution to the inflation of test statistics in GWAS. In this communication, we extend the LD Score regression approach by applying the generalized estimation equations (GEE) framework, which is capable of incorporating more external information from reference panels about the correlation structure of test statistics. We apply our GEE approach and LD Score regression to simulated and real data to compare their performance. We show that our proposed methodology obtains more efficient estimates while preserving the robustness and desired properties of LD Score regression.