The effects of artificial selection on the maize genome

Domestication promotes rapid phenotypic evolution through artificial selection. We investigated the genetic history by which the wild grass teosinte (Zea mays ssp. parviglumis) was domesticated into modern maize (Z. mays ssp. mays). Analysis of single-nucleotide polymorphisms in 774 genes indicates that 2 to 4% of these genes experienced artificial selection. The remaining genes retain evidence of a population bottleneck associated with domestication. Candidate selected genes with putative function in plant growth are clustered near quantitative trait loci that contribute to phenotypic differences between maize and teosinte. If we assume that our sample of genes is representative, ∼1200 genes throughout the maize genome have been affected by artificial selection

Fifteen-year trends of long-term disability and sick leaves in ankylosing spondylitis

Analysis of fine scale genetic structure in continuous populations of outcrossing plant species has traditionally been limited by the availability of sufficient markers. We used a set of 468 SNPs to characterize fine-scale genetic structure within and between two dense stands of the wild ancestor of maize, teosinte (Zea mays ssp. parviglumis). Our analyses confirmed that teosinte is highly outcrossing and showed little population structure over short distances. We found that the two populations were clearly genetically differentiated, although the actual level of differentiation was low. Spatial autocorrelation of relatedness was observed within both sites but was somewhat stronger in one of the populations. Using principal component analysis, we found evidence for significant local differentiation in the population with stronger spatial autocorrelation. This differentiation was associated with pronounced shifts in the first two principal components along the field. These shifts corresponded to changes in allele frequencies, potentially due to local topographical features. There was little evidence for selection at individual loci as a contributing factor to differentiation. Our results demonstrate that significant local differentiation may, but need not, co-occur with spatial autocorrelation of relatedness. The present study represents one of the most detailed analyses of local genetic structure to date and provides a benchmark for future studies dealing with fine scale patterns of genetic diversity in natural plant populations. " 2010 Blackwell Publishing Ltd.",,,,,,"10.1111/j.1365-294X.2010.04559.x",,,"http://hdl.handle.net/20.500.12104/41510","http://www.scopus.com/inward/record.url?eid=2-s2.0-77950292826&partnerID=40&md5=095b598a34a7094c855c5ccf81686a4f",,,,,,"6",,"Molecular Ecology",,"116

A unified mixed model method for association mapping that accounts for multiple levels of relatedness

As population structure can result in spurious associations, it has constrained the use of association studies in human and plant genetics. Association mapping, however, holds great promise if true signals of functional association can be separated from the vast number of false signals generated by population structure1,2. We have developed a unified mixed-model approach to account for multiple levels of relatedness simultaneously as detected by random genetic markers. We applied this new approach to two samples: a family-based sample of 14 human families, for quantitative gene expression dissection, and a sample of 277 diverse maize inbred lines with complex familial relationships and population structure, for quantitative trait dissection. Our method demonstrates improved control of both type I and type II error rates over other methods. As this new method crosses the boundary between family-based and structured association samples, it provides a powerful complement to currently available methods for association mapping

The effect of natural selection on phylogeny reconstruction algorithms

Abstract. We study the effect of natural selection on the performance of phylogeny reconstruction algorithms using Avida, a software platform that maintains a population of digital organisms (self-replicating computer programs) that evolve subject to natural selection, mutation, and drift. We compare the performance of neighbor-joining and maximum parsimony algorithms on these Avida populations to the performance of the same algorithms on randomly generated data that evolve subject only to mutation and drift. Our results show that natural selection has several specific effects on the sequences of the resulting populations, and that these effects lead to improved performance for neighbor-joining and maximum parsimony in some settings. We then show that the effects of natural selection can be partially achieved by using a non-uniform probability distribution for the location of mutations in randomly generated genomes.