A B73 x Palomero Toluqueño mapping population reveals local adaptation in Mexican highland maize

Generations of farmer selection in the central Mexican highlands have produced unique maize varieties adapted to the challenges of the local environment. In addition to possessing great agronomic and cultural value, Mexican highland maize represents a good system for the study of local adaptation and acquisition of adaptive phenotypes under cultivation. In this study we characterize a recombinant inbred line population derived from the B73 reference line and the Mexican highland maize variety Palomero Toluqueño. B73 and Palomero Toluqueño showed classic rank-changing differences in performance between lowland and highland field sites, indicative of local adaptation. Quantitative trait mapping identified genomic regions linked to effects on yield components that were conditionally expressed depending on the environment. For the principal genomic regions associated with ear weight and total kernel number, the Palomero Toluqueño allele conferred an advantage specifically in the highland site, consistent with local adaptation. We identified Palomero Toluqueño alleles associated with expression of characteristic highland traits, including reduced tassel branching, increased sheath pigmentation and the presence of sheath macrohairs. The oligogenic architecture of these three morphological traits supports their role in adaptation, suggesting they have arisen from consistent directional selection acting at distinct points across the genome. We discuss these results in the context of the origin of phenotypic novelty during selection, commenting on the role of de novo mutation and the acquisition of adaptive variation by gene flow from endemic wild relatives.

Gene-level quantitative trait mapping in C. elegans

The Caenorhabditis elegans multiparental experimental evolution (CeMEE) panel is a collection of genome-sequenced, cryopreserved recombinant inbred lines useful for mapping the evolution and genetic basis of quantitative traits. We have expanded the resource with new lines and new populations, and here report the genotype and haplotype composition of CeMEE version 2, including a large set of putative de novo mutations, and updated additive and epistatic mapping simulations. Additive quantitative trait loci explaining 4% of trait variance are detected with > 80% power, and the median detection interval approaches single gene resolution on the highly recombinant chromosome arms. Although CeMEE populations are derived from a long-term evolution experiment, genetic structure is dominated by variation present in the ancestral population.

Gene-level quantitative trait mapping in an expandedC. elegansmultiparent experimental evolution panel

ABSTRACTTheCaenorhabditis elegansmultiparental experimental evolution (CeMEE) panel is a collection of genome-sequenced, cryopreserved recombinant inbred lines useful for mapping the genetic basis and evolution of quantitative traits. We have expanded the resource with new lines and new populations, and here report updated additive and epistatic mapping simulations and the genetic and haplotypic composition of CeMEE version 2. Additive QTL explaining 3% of trait variance are detected with >80% power, and the median detection interval is around the length of a single gene on the highly recombinant chromosome arms. Although CeMEE populations are derived from a long-term evolution experiment, genetic structure is dominated by variation present in the ancestral population and is not obviously associated with phenotypic differentiation.C. elegansprovides exceptional experimental advantages for the study of phenotypic evolution.

Tree-based quantitative trait mapping in the presence of external covariates

A central goal in biological and biomedical sciences is to identify the molecular basis of variation in morphological and behavioral traits. Over the last decade, improvements in sequencing technologies coupled with the active development of association mapping methods have made it possible to link single nucleotide polymorphisms (SNPs) and quantitative traits. However, a major limitation of existing methods is that they are often unable to consider complex, but biologically-realistic, scenarios. Previous work showed that association mapping method performance can be improved by using the evolutionary history within each SNP to estimate the covariance structure among randomly-sampled individuals. Here, we propose a method that can be used to analyze a variety of data types, such as data including external covariates, while considering the evolutionary history among SNPs, providing an advantage over existing methods. Existing methods either do so at a computational cost, or fail to model these relationships altogether. By considering the broad-scale relationships among SNPs, the proposed approach is both computationally-feasible and informed by the evolutionary history among SNPs. We show that incorporating an approximate covariance structure during analysis of complex data sets increases performance in quantitative trait mapping, and apply the proposed method to deer mice data.

Recombination in diverse maize is stable, predictable, and associated with genetic load

Among the fundamental evolutionary forces, recombination arguably has the largest impact on the practical work of plant breeders. Varying over 1,000-fold across the maize genome, the local meiotic recombination rate limits the resolving power of quantitative trait mapping and the precision of favorable allele introgression. The consequences of low recombination also theoretically extend to the species-wide scale by decreasing the power of selection relative to genetic drift, and thereby hindering the purging of deleterious mutations. In this study, we used genotyping-by-sequencing (GBS) to identify 136,000 recombination breakpoints at high resolution within US and Chinese maize nested association mapping populations. We find that the pattern of cross-overs is highly predictable on the broad scale, following the distribution of gene density and CpG methylation. Several large inversions also suppress recombination in distinct regions of several families. We also identify recombination hotspots ranging in size from 1 kb to 30 kb. We find these hotspots to be historically stable and, compared with similar regions with low recombination, to have strongly differentiated patterns of DNA methylation and GC content. We also provide evidence for the historical action of GC-biased gene conversion in recombination hotspots. Finally, using genomic evolutionary rate profiling (GERP) to identify putative deleterious polymorphisms, we find evidence for reduced genetic load in hotspot regions, a phenomenon that may have considerable practical importance for breeding programs worldwide.