Genomic haplotype blocks may not accurately reflect spatial variation in historic recombination intensity

Accounting for long-range correlations in genome-wide simulations of large cohorts

Coalescent simulations are widely used to examine the effects of evolution and demographic history on the genetic makeup of populations. Thanks to recent progress in algorithms and data structures, simulators such as the widely-used msprime now provide genome-wide simulations for millions of individuals. However, this software relies on classic coalescent theory and its assumptions that sample sizes are small and that the region being simulated is short. Here we show that coalescent simulations of long regions of the genome exhibit large biases in identity-by-descent (IBD), long-range linkage disequilibrium (LD), and ancestry patterns, particularly when the sample size is large. We present a Wright-Fisher extension to msprime, and show that it produces more realistic distributions of IBD, LD, and ancestry proportions, while also addressing more subtle biases of the coalescent. Further, these extensions are more computationally efficient than state-of-the-art coalescent simulations when simulating long regions, including whole-genome data. For shorter regions, efficiency can be maintained via a hybrid model which simulates the recent past under the Wright-Fisher model and uses coalescent simulations in the distant past.

Coalescent theory has provided deep theoretical insight into patterns of human diversity. Implementations of coalescent models in simulation software such as ms have further provided tools to interpret thousands of genomic studies. Recent technical progress has allowed for a dramatic increase in the scale at which genomes can be both measured and simulated, opening up opportunities for a finer understanding of evolutionary biology. However, we show that coalescent simulations of long regions of the genome exhibit large biases in sample relatedness, distorting haplotype sharing and ancestry patterns in simulated cohorts. We trace these biases to basic assumptions of the coalescent model, and show how the assumptions can be relaxed to provide a better description of the observed patterns of genetic polymorphism at a fraction of the computational cost.

Potential associations between severity of infection and the presence of virulence-associated genes in clinical strains of Staphylococcus aureus

Background

The clinical spectrum of Staphylococcus aureus infection ranges from asymptomatic nasal carriage to osteomyelitis, infective endocarditis (IE) and death. In this study, we evaluate potential association between the presence of specific genes in a collection of prospectively characterized S. aureus clinical isolates and clinical outcome.

Methodology/Principal Findings

Two hundred thirty-nine S. aureus isolates (121 methicillin-resistant S. aureus [MRSA] and 118 methicillin-susceptible S. aureus [MSSA]) were screened by array comparative genomic hybridization (aCGH) to identify genes implicated in complicated infections. After adjustment for multiple tests, 226 genes were significantly associated with severity of infection. Of these 226 genes, 185 were not in the SCCmec element. Within the 185 non-SCCmec genes, 171 were less common and 14 more common in the complicated infection group. Among the 41 genes in the SCCmec element, 37 were more common and 4 were less common in the complicated group. A total of 51 of the 2014 sequences evaluated, 14 non-SCCmec and 37 SCCmec, were identified as genes of interest.

Conclusions/Significance

Of the 171 genes less common in complicated infections, 152 are of unknown function and may contribute to attenuation of virulence. The 14 non-SCCmec genes more common in complicated infections include bacteriophage-encoded genes such as regulatory factors and autolysins with potential roles in tissue adhesion or biofilm formation.

Epistasis: obstacle or advantage for mapping complex traits?

Identification of genetic loci in complex traits has focused largely on one-dimensional genome scans to search for associations between single markers and the phenotype. There is mounting evidence that locus interactions, or epistasis, are a crucial component of the genetic architecture of biologically relevant traits. However, epistasis is often viewed as a nuisance factor that reduces power for locus detection. Counter to expectations, recent work shows that fitting full models, instead of testing marker main effect and interaction components separately, in exhaustive multi-locus genome scans can have higher power to detect loci when epistasis is present than single-locus scans, and improvement that comes despite a much larger multiple testing alpha-adjustment in such searches. We demonstrate, both theoretically and via simulation, that the expected power to detect loci when fitting full models is often larger when these loci act epistatically than when they act additively. Additionally, we show that the power for single locus detection may be improved in cases of epistasis compared to the additive model. Our exploration of a two step model selection procedure shows that identifying the true model is difficult. However, this difficulty is certainly not exacerbated by the presence of epistasis, on the contrary, in some cases the presence of epistasis can aid in model selection. The impact of allele frequencies on both power and model selection is dramatic.

Understanding the evolution of defense metabolites in Arabidopsis thaliana using genome-wide association mapping

With the improvement and decline in cost of high-throughput genotyping and phenotyping technologies, genome-wide association (GWA) studies are fast becoming a preferred approach for dissecting complex quantitative traits. Glucosinolate (GSL) secondary metabolites within Arabidopsis spp. can serve as a model system to understand the genomic architecture of quantitative traits. GSLs are key defenses against insects in the wild and the relatively large number of cloned quantitative trait locus (QTL) controlling GSL traits allows comparison of GWA to previous QTL analyses. To better understand the specieswide genomic architecture controlling plant-insect interactions and the relative strengths of GWA and QTL studies, we conducted a GWA mapping study using 96 A. thaliana accessions, 43 GSL phenotypes, and approximately 230,000 SNPs. Our GWA analysis identified the two major polymorphic loci controlling GSL variation (AOP and MAM) in natural populations within large blocks of positive associations encompassing dozens of genes. These blocks of positive associations showed extended linkage disequilibrium (LD) that we hypothesize to have arisen from balancing or fluctuating selective sweeps at both the AOP and MAM loci. These potential sweep blocks are likely linked with the formation of new defensive chemistries that alter plant fitness in natural environments. Interestingly, this GWA analysis did not identify the majority of previously identified QTL even though these polymorphisms were present in the GWA population. This may be partly explained by a nonrandom distribution of phenotypic variation across population subgroups that links population structure and GSL variation, suggesting that natural selection can hinder the detection of phenotype-genotype associations in natural populations.

Association of poly-purine/poly-pyrimidine sequences with meiotic recombination hot spots

Background

Meiotic recombination events have been found to concentrate in 1–2.5 kilo base regions, but these recombination hot spots do not share a consensus sequence and why they occur at specific sites is not fully understood. Some previous evidence suggests that poly-purine/poly-pyrimidine (poly-pu/py) tracts (PPTs), a class of sequence with distinctive biochemical properties, could be involved in recombination, but no general association of PPTs with meiotic recombination hot spots has previously been reported.

Results

We used computational methods to investigate in detail the relationship between PPTs and hot spots. We show statistical associations of PPT frequency with hot spots of meiotic recombination initiating lesions, double-strand breaks, in the genome of the yeast S. cerevisiae and with experimentally well characterized human meiotic recombination hot spots. Supporting a possible role of poly-pu/py-rich sequences in hot spot recombination, we also found that all three single nucleotide polymorphisms previously shown to be associated with human hot spot activity changes occur within sequence contexts of 14 bp or longer that are 85% or more poly-pu/py and at least 70% G/C. These polymorphisms are all close to the hot spot mid points. Comparing the sequences of experimentally characterized human hot spots with the orthologous regions of the chimpanzee genome previously shown not to contain hot spots, we found that in all five cases in which comparisons for the hot spot central regions are possible with publicly available sequence data, there are differences near the human hot spot mid points within sequences 14 bp or longer consisting of more than 80% poly-pu/py and at least 50% G/C.

Conclusion

Our results, along with previous evidence for the unique biochemical properties and recombination-stimulating potential of poly-pu/py-rich sequences, suggest that the possible functional involvement of this type of sequence in meiotic recombination hot spots deserves further experimental exploration.