Polygenic Architectures Research Articles

Evolutionary genomics of gypsy moth populations sampled along a latitudinal gradient.

The European gypsy moth (Lymantria dispar L.) was first introduced to Massachusetts in 1869 and within 150years has spread throughout eastern North America. This large-scale invasion across a heterogeneous landscape allows examination of the genetic signatures of adaptation potentially associated with rapid geographical spread. We tested the hypothesis that spatially divergent natural selection has driven observed changes in three developmental traits that were measured in a common garden for 165 adult moths sampled from six populations across a latitudinal gradient covering the entirety of the range. We generated genotype data for 91,468 single nucleotide polymorphisms based on double digest restriction-site associated DNA sequencing and used these data to discover genome-wide associations for each trait, as well as to test for signatures of selection on the discovered architectures. Genetic structure across the introduced range of gypsy moth was low in magnitude (FST =0.069), with signatures of bottlenecks and spatial expansion apparent in the rare portion of the allele frequency spectrum. Results from applications of Bayesian sparse linear mixed models were consistent with the presumed polygenic architectures of each trait. Further analyses indicated spatially divergent natural selection acting on larval development time and pupal mass, with the linkage disequilibrium component of this test acting as the main driver of observed patterns. The populations most important for these signals were two range-edge populations established less than 30 generations ago. We discuss the importance of rapid polygenic adaptation to the ability of non-native species to invade novel environments.

DNA sequence-level analyses reveal potential phenotypic modifiers in a large family with psychiatric disorders

Psychiatric disorders are a group of genetically related diseases with highly polygenic architectures. Genome-wide association analyses have made substantial progress towards understanding the genetic architecture of these disorders. More recently, exome- and whole-genome sequencing of cases and families have identified rare, high penetrant variants that provide direct functional insight. There remains, however, a gap in the heritability explained by these complementary approaches. To understand how multiple genetic variants combine to modify both severity and penetrance of a highly penetrant variant, we sequenced 48 whole genomes from a family with a high loading of psychiatric disorder linked to a balanced chromosomal translocation. The (1;11)(q42;q14.3) translocation directly disrupts three genes: DISC1, DISC2, DISC1FP and has been linked to multiple brain imaging and neurocognitive outcomes in the family. Using DNA sequence-level linkage analysis, functional annotation and population-based association, we identified common and rare variants in GRM5 (minor allele frequency (MAF) > 0.05), PDE4D (MAF > 0.2) and CNTN5 (MAF < 0.01) that may help explain the individual differences in phenotypic expression in the family. We suggest that whole-genome sequencing in large families will improve the understanding of the combined effects of the rare and common sequence variation underlying psychiatric phenotypes.

Empirical Bayes Estimation of Semi-parametric Hierarchical Mixture Models for Unbiased Characterization of Polygenic Disease Architectures.

Genome-wide association studies (GWAS) suggest that the genetic architecture of complex diseases consists of unexpectedly numerous variants with small effect sizes. However, the polygenic architectures of many diseases have not been well characterized due to lack of simple and fast methods for unbiased estimation of the underlying proportion of disease-associated variants and their effect-size distribution. Applying empirical Bayes estimation of semi-parametric hierarchical mixture models to GWAS summary statistics, we confirmed that schizophrenia was extremely polygenic [~40% of independent genome-wide SNPs are risk variants, most within odds ratio (OR = 1.03)], whereas rheumatoid arthritis was less polygenic (~4 to 8% risk variants, significant portion reaching OR = 1.05 to 1.1). For rheumatoid arthritis, stratified estimations revealed that expression quantitative loci in blood explained large genetic variance, and low- and high-frequency derived alleles were prone to be risk and protective, respectively, suggesting a predominance of deleterious-risk and advantageous-protective mutations. Despite genetic correlation, effect-size distributions for schizophrenia and bipolar disorder differed across allele frequency. These analyses distinguished disease polygenic architectures and provided clues for etiological differences in complex diseases.

The genomics of local adaptation in trees: are we out of the woods yet?

There is substantial interest in uncovering the genetic basis of the traits underlying adaptive responses in tree species, as this information will ultimately aid conservation and industrial endeavors across populations, generations, and environments. Fundamentally, the characterization of such genetic bases is within the context of a genetic architecture, which describes the mutlidimensional relationship between genotype and phenotype through the identification of causative variants, their relative location within a genome, expression, pleiotropic effect, environmental influence, and degree of dominance, epistasis, and additivity. Here, we review theory related to polygenic local adaptation and contextualize these expectations with methods often used to uncover the genetic basis of traits important to tree conservation and industry. A broad literature survey suggests that most tree traits generally exhibit considerable heritability, that underlying quantitative genetic variation (QST) is structured more so across populations than neutral expectations (FST) in 69% of comparisons across the literature, and that single-locus associations often exhibit small estimated per-locus effects. Together, these results suggest differential selection across populations often acts on tree phenotypes underlain by polygenic architectures consisting of numerous small to moderate effect loci. Using this synthesis, we highlight the limits of using solely single-locus approaches to describe underlying genetic architectures and close by addressing hurdles and promising alternatives towards such goals, remark upon the current state of tree genomics, and identify future directions for this field. Importantly, we argue, the success of future endeavors should not be predicated on the shortcomings of past studies and will instead be dependent upon the application of theory to empiricism, standardized reporting, centralized open-access databases, and continual input and review of the community’s research.

Probing the Association between Early Evolutionary Markers and Schizophrenia

Schizophrenia is suggested to be a by-product of the evolution in humans, a compromise for our language, creative thinking and cognitive abilities, and thus, essentially, a human disorder. The time of its origin during the course of human evolution remains unclear. Here we investigate several markers of early human evolution and their relationship to the genetic risk of schizophrenia. We tested the schizophrenia evolutionary hypothesis by analyzing genome-wide association studies of schizophrenia and other human phenotypes in a statistical framework suited for polygenic architectures. We analyzed evolutionary proxy measures: human accelerated regions, segmental duplications, and ohnologs, representing various time periods of human evolution for overlap with the human genomic loci associated with schizophrenia. Polygenic enrichment plots suggest a higher prevalence of schizophrenia associations in human accelerated regions, segmental duplications and ohnologs. However, the enrichment is mostly accounted for by linkage disequilibrium, especially with functional elements like introns and untranslated regions. Our results did not provide clear evidence that markers of early human evolution are more likely associated with schizophrenia. While SNPs associated with schizophrenia are enriched in HAR, Ohno and SD regions, the enrichment seems to be mediated by affiliation to known genomic enrichment categories. Taken together with previous results, these findings suggest that schizophrenia risk may have mainly developed more recently in human evolution.

Quantitative Trait Locus and Genetical Genomics Analysis Identifies Putatively Causal Genes for Fecundity and Brooding in the Chicken.

Life history traits such as fecundity are important to evolution because they make up components of lifetime fitness. Due to their polygenic architectures, such traits are difficult to investigate with genetic mapping. Therefore, little is known about their molecular basis. One possible way toward finding the underlying genes is to map intermediary molecular phenotypes, such as gene expression traits. We set out to map candidate quantitative trait genes for egg fecundity in the chicken by combining quantitative trait locus mapping in an advanced intercross of wild by domestic chickens with expression quantitative trait locus mapping in the same birds. We measured individual egg fecundity in 232 intercross chickens in two consecutive trials, the second one aimed at measuring brooding. We found 12 loci for different aspects of egg fecundity. We then combined the genomic confidence intervals of these loci with expression quantitative trait loci from bone and hypothalamus in the same intercross. Overlaps between egg loci and expression loci, and trait–gene expression correlations identify 29 candidates from bone and five from hypothalamus. The candidate quantitative trait genes include fibroblast growth factor 1, and mitochondrial ribosomal proteins L42 and L32. In summary, we found putative quantitative trait genes for egg traits in the chicken that may have been affected by regulatory variants under chicken domestication. These represent, to the best of our knowledge, some of the first candidate genes identified by genome-wide mapping for life history traits in an avian species.

Estimating genetic architectures from artificial-selection responses: A random-effect framework

Artificial-selection experiments on plants and animals generate large datasets reporting phenotypic changes in the course of time. The dynamics of the changes reflect the underlying genetic architecture, but only simple statistical tools have so far been available to analyze such time series. This manuscript describes a general statistical framework based on random-effect models aiming at estimating key parameters of genetic architectures from artificial-selection responses. We derive explicit Mendelian models (in which the genetic architecture relies on one or two large-effect loci), and compare them with classical polygenic models. With simulations, we show that the models are accurate and powerful enough to provide useful estimates from realistic experimental designs, and we demonstrate that model selection is effective in picking few-locus vs. polygenic genetic architectures even from medium-quality artificial-selection data. The method is illustrated by the analysis of a historical selection experiment, carried on color pattern in rats by Castle et al.

Canalization and adaptation in a landscape of sources and sinks

Landscapes are often spatially heterogeneous, and many species frequently confront novel environments to which they are not adapted. Whether a species becomes adapted to a novel environment, and thus undergoes niche evolution, may depend not only on the genetic architecture of the traits under selection, but also on the structure of the ecological landscape. Different models of gene architecture are used to show that complex genetic architectures tends to produce genetic canalization that slows adaptation to novel environments compared to simpler additive polygenic architectures, but that the topology of the landscape interacts with genetic architecture to influence the probability of adaptation. This interaction can lead to unexpected results, such as a greater probability of adaptation to a novel environment for a population of more highly canalized individuals than a population of less canalized individuals. The interplay between landscape structure and genetic architecture may influence the balance of evolutionary forces acting on a population, and thus whether a species is likely to adapt to the novel environments it confronts.

Are the polygenic architectures of resistance to Phytophthora capsici and P. parasitica independent in pepper?

The pepper accession Criollo de Morelos 334 is the most efficient source of resistance currently known to Phytophthora capsici and P. parasitica. To investigate whether genetic controls of resistance to two Phytophthora species are independent, we compared the genetic architecture of resistance of CM334 to both Phytophthora species. The RIL population F5YC used to construct the high-resolution genetic linkage map of pepper was assessed for resistance to one isolate of each Phytophthora species. Inheritance of the P. capsici and P. parasitica resistance was polygenic. Twelve additive QTLs involved in the P. capsici resistance and 14 additive QTLs involved in the P. parasitica resistance were detected. The QTLs identified in this progeny were specific to these Phytophthora species. Comparative mapping analysis with literature data identified three colocations between resistance QTLs to P. parasitica and P. capsici in pepper. Whereas this result suggests presence of common resistance factors to the two Phytophthora species in pepper, which possibly derive from common ancestral genes, calculation of the colocation probability indicates that these colocations could occur by chance.