32,934 research outputs found
The Population Genetic Signature of Polygenic Local Adaptation
Adaptation in response to selection on polygenic phenotypes may occur via
subtle allele frequencies shifts at many loci. Current population genomic
techniques are not well posed to identify such signals. In the past decade,
detailed knowledge about the specific loci underlying polygenic traits has
begun to emerge from genome-wide association studies (GWAS). Here we combine
this knowledge from GWAS with robust population genetic modeling to identify
traits that may have been influenced by local adaptation. We exploit the fact
that GWAS provide an estimate of the additive effect size of many loci to
estimate the mean additive genetic value for a given phenotype across many
populations as simple weighted sums of allele frequencies. We first describe a
general model of neutral genetic value drift for an arbitrary number of
populations with an arbitrary relatedness structure. Based on this model we
develop methods for detecting unusually strong correlations between genetic
values and specific environmental variables, as well as a generalization of
comparisons to test for over-dispersion of genetic values among
populations. Finally we lay out a framework to identify the individual
populations or groups of populations that contribute to the signal of
overdispersion. These tests have considerably greater power than their single
locus equivalents due to the fact that they look for positive covariance
between like effect alleles, and also significantly outperform methods that do
not account for population structure. We apply our tests to the Human Genome
Diversity Panel (HGDP) dataset using GWAS data for height, skin pigmentation,
type 2 diabetes, body mass index, and two inflammatory bowel disease datasets.
This analysis uncovers a number of putative signals of local adaptation, and we
discuss the biological interpretation and caveats of these results.Comment: 42 pages including 8 figures and 3 tables; supplementary figures and
tables not included on this upload, but are mostly unchanged from v
Statistical Modeling of Epistasis and Linkage Decay using Logic Regression
Logic regression has been recognized as a tool that can identify and model non-additive genetic interactions using Boolean logic groups. Logic regression, TASSEL-GLM and SAS-GLM were compared for analytical precision using a previously characterized model system to identify the best genetic model explaining epistatic interaction of vernalization-sensitivity in barley. A genetic model containing two molecular markers identified in vernalization response in barley was selected using logic regression while both TASSEL-GLM and SAS-GLM included spurious associations in their models. The results also suggest the logic regression can be used to identify dominant/recessive relationships between epistatic alleles through its use of conjugate
operators
Multi-environment QTL mixed models for drought stress adaptation in wheat
Many quantitative trait loci (QTL) detection methods ignore QTL-by-environment interaction (QEI) and are limited in accommodation of error and environment-specific variance. This paper outlines a mixed model approach using a recombinant inbred spring wheat population grown in six drought stress trials. Genotype estimates for yield, anthesis date and height were calculated using the best design and spatial effects model for each trial. Parsimonious factor analytic models best captured the variance-covariance structure, including genetic correlations, among environments. The 1RS.1BL rye chromosome translocation (from one parent) which decreased progeny yield by 13.8 g m(-2) was explicitly included in the QTL model. Simple interval mapping (SIM) was used in a genome-wide scan for significant QTL, where QTL effects were fitted as fixed environment-specific effects. All significant environment-specific QTL were subsequently included in a multi-QTL model and evaluated for main and QEI effects with non-significant QEI effects being dropped. QTL effects (either consistent or environment-specific) included eight yield, four anthesis, and six height QTL. One yield QTL co-located (or was linked) to an anthesis QTL, while another co-located with a height QTL. In the final multi-QTL model, only one QTL for yield (6 g m(-2)) was consistent across environments (no QEI), while the remaining QTL had significant QEI effects (average size per environment of 5.1 g m(-2)). Compared to single trial analyses, the described framework allowed explicit modelling and detection of QEI effects and incorporation of additional classification information about genotypes
Multiple-line inference of selection on quantitative traits
Trait differences between species may be attributable to natural selection.
However, quantifying the strength of evidence for selection acting on a
particular trait is a difficult task. Here we develop a population-genetic test
for selection acting on a quantitative trait which is based on multiple-line
crosses. We show that using multiple lines increases both the power and the
scope of selection inference. First, a test based on three or more lines
detects selection with strongly increased statistical significance, and we show
explicitly how the sensitivity of the test depends on the number of lines.
Second, a multiple-line test allows to distinguish different lineage-specific
selection scenarios. Our analytical results are complemented by extensive
numerical simulations. We then apply the multiple-line test to QTL data on
floral character traits in plant species of the Mimulus genus and on
photoperiodic traits in different maize strains, where we find a signatures of
lineage-specific selection not seen in a two-line test.Comment: 21 pages, 11 figures; to appear in Genetic
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