An explanation for the prevalence of XY over ZW sex determination in species derived from hermaphroditism.

The many independent transitions from hermaphroditism to separate sexes (dioecy) in flowering plants and some animal clades must often have involved the emergence of a heterogametic sex-determining locus, the basis of XY and ZW sex determination (i.e., male and female heterogamety). Current estimates indicate that XY sex determination is much more frequent than ZW, but the reasons for this asymmetry are unclear. One proposition is that separate sexes evolve through the invasion of sterility mutations at closely linked loci, in which case XY sex determination evolves if the initial male sterility mutation is fully recessive. Alternatively, dioecy may evolve via the gradual divergence of male and female phenotypes, but the genetic basis of such divergence and its connection to XY and ZW systems remain poorly understood. Using mathematical modeling, we show how dioecy with XY or ZW sex determination can emerge from the joint evolution of resource allocation to male and female function with its genetic architecture. Our model reveals that whether XY or ZW sex determination evolves depends on the trade-off between allocation to male and female function, and on the mating system of the ancestral hermaphrodites, with selection for female specialization or inbreeding avoidance both favoring XY sex determination. Together, our results cast light on an important but poorly understood path from hermaphroditism to dioecy, and provide an adaptive hypothesis for the preponderance of XY systems. Beyond sex and sex determination, our model shows how ecology can influence the way selection shapes the genetic architecture of polymorphic traits.

Tree-based QTL mapping with expected local genetic relatedness matrices.

Understanding the genetic basis of complex phenotypes is a central pursuit of genetics. Genome-wide association studies (GWASs) are a powerful way to find genetic loci associated with phenotypes. GWASs are widely and successfully used, but they face challenges related to the fact that variants are tested for association with a phenotype independently, whereas in reality variants at different sites are correlated because of their shared evolutionary history. One way to model this shared history is through the ancestral recombination graph (ARG), which encodes a series of local coalescent trees. Recent computational and methodological breakthroughs have made it feasible to estimate approximate ARGs from large-scale samples. Here, we explore the potential of an ARG-based approach to quantitative-trait locus (QTL) mapping, echoing existing variance-components approaches. We propose a framework that relies on the conditional expectation of a local genetic relatedness matrix (local eGRM) given the ARG. Simulations show that our method is especially beneficial for finding QTLs in the presence of allelic heterogeneity. By framing QTL mapping in terms of the estimated ARG, we can also facilitate the detection of QTLs in understudied populations. We use local eGRM to analyze two chromosomes containing known body size loci in a sample of Native Hawaiians. Our investigations can provide intuition about the benefits of using estimated ARGs in population- and statistical-genetic methods in general.

Analysis of MRI-derived spleen iron in the UK Biobank identifies genetic variation linked to iron homeostasis and hemolysis.

The spleen plays a key role in iron homeostasis. It is the largest filter of the blood and performs iron reuptake from old or damaged erythrocytes. Despite this role, spleen iron concentration has not been measured in a large, population-based cohort. In this study, we quantify spleen iron in 41,764 participants of the UK Biobank by using magnetic resonance imaging and provide a reference range for spleen iron in an unselected population. Through genome-wide association study, we identify associations between spleen iron and regulatory variation at two hereditary spherocytosis genes, ANK1 and SPTA1. Spherocytosis-causing coding mutations in these genes are associated with lower reticulocyte volume and increased reticulocyte percentage, while these common alleles are associated with increased expression of ANK1 and SPTA1 in blood and with larger reticulocyte volume and reduced reticulocyte percentage. As genetic modifiers, these common alleles may explain mild spherocytosis phenotypes that have been observed clinically. Our genetic study also identifies a signal that co-localizes with a splicing quantitative trait locus for MS4A7, and we show this gene is abundantly expressed in the spleen and in macrophages. The combination of deep learning and efficient image processing enables non-invasive measurement of spleen iron and, in turn, characterization of genetic factors related to the lytic phase of the erythrocyte life cycle and iron reuptake in the spleen.

Landscape genomics reveals regions associated with adaptive phenotypic and genetic variation in Ethiopian indigenous chickens.

Climate change is a threat to sustainable livestock production and livelihoods in the tropics. It has adverse impacts on feed and water availability, disease prevalence, production, environmental temperature, and biodiversity. Unravelling the drivers of local adaptation and understanding the underlying genetic variation in random mating indigenous livestock populations informs the design of genetic improvement programmes that aim to increase productivity and resilience. In the present study, we combined environmental, genomic, and phenotypic information of Ethiopian indigenous chickens to investigate their environmental adaptability. Through a hybrid sampling strategy, we captured wide biological and ecological variabilities across the country. Our environmental dataset comprised mean values of 34 climatic, vegetation and soil variables collected over a thirty-year period for 260 geolocations. Our biological dataset included whole genome sequences and quantitative measurements (on eight traits) from 513 individuals, representing 26 chicken populations spread along 4 elevational gradients (6-7 populations per gradient). We performed signatures of selection analyses ([Formula: see text] and XP-EHH) to detect footprints of natural selection, and redundancy analyses (RDA) to determine genotype-environment and genotype-phenotype-associations. RDA identified 1909 outlier SNPs linked with six environmental predictors, which have the highest contributions as ecological drivers of adaptive phenotypic variation. The same method detected 2430 outlier SNPs that are associated with five traits. A large overlap has been observed between signatures of selection identified by[Formula: see text]and XP-EHH showing that both methods target similar selective sweep regions. Average genetic differences measured by [Formula: see text] are low between gradients, but XP-EHH signals are the strongest between agroecologies. Genes in the calcium signalling pathway, those associated with the hypoxia-inducible factor (HIF) transcription factors, and sports performance (GALNTL6) are under selection in high-altitude populations. Our study underscores the relevance of landscape genomics as a powerful interdisciplinary approach to dissect adaptive phenotypic and genetic variation in random mating indigenous livestock populations.

Polygenic Adaptation: Integrating Population Genetics and Gene Regulatory Networks.

The adaptation of populations to local environments often relies on the selection of optimal values for polygenic traits. Here, we first summarize the results obtained from different quantitative genetics and population genetics models, about the genetic architecture of polygenic traits and their response to directional selection. We then highlight the contribution of systems biology to the understanding of the molecular bases of polygenic traits and the evolution of gene regulatory networks involved in these traits. Finally, we discuss the need for a unifying framework merging the fields of population genetics, quantitative genetics and systems biology to better understand the molecular bases of polygenic traits adaptation.

Unifying quantification methods for sexual selection and assortative mating using information theory.

Sexual selection plays a crucial role in modern evolutionary theory, offering valuable insight into evolutionary patterns and species diversity. Recently, a comprehensive definition of sexual selection has been proposed, defining it as any selection that arises from fitness differences associated with nonrandom success in the competition for access to gametes for fertilization. Previous research on discrete traits demonstrated that non-random mating can be effectively quantified using Jeffreys (or symmetrized Kullback-Leibler) divergence, capturing information acquired through mating influenced by mutual mating propensities instead of random occurrences. This novel theoretical framework allows for detecting and assessing the strength of sexual selection and assortative mating. In this study, we aim to achieve two primary objectives. Firstly, we demonstrate the seamless alignment of the previous theoretical development, rooted in information theory and mutual mating propensity, with the aforementioned definition of sexual selection. Secondly, we extend the theory to encompass quantitative traits. Our findings reveal that sexual selection and assortative mating can be quantified effectively for quantitative traits by measuring the information gain relative to the random mating pattern. The connection of the information indices of sexual selection with the classical measures of sexual selection is established. Additionally, if mating traits are normally distributed, the measure capturing the underlying information of assortative mating is a function of the square of the correlation coefficient, taking values within the non-negative real number set [0, +∞). It is worth noting that the same divergence measure captures information acquired through mating for both discrete and quantitative traits. This is interesting as it provides a common context and can help simplify the study of sexual selection patterns.

Robust phylogenetic regression.

Modern comparative biology owes much to phylogenetic regression. At its conception, this technique sparked a revolution that armed biologists with phylogenetic comparative methods (PCMs) for disentangling evolutionary correlations from those arising from hierarchical phylogenetic relationships. Over the past few decades, the phylogenetic regression framework has become a paradigm of modern comparative biology that has been widely embraced as a remedy for shared ancestry. However, recent evidence has sown doubt over the efficacy of phylogenetic regression, and PCMs more generally, with the suggestion that many of these methods fail to provide an adequate defense against unreplicated evolution-the primary justification for using them in the first place. Importantly, some of the most compelling examples of biological innovation in nature result from abrupt lineage-specific evolutionary shifts, which current regression models are largely ill-equipped to deal with. Here we explore a solution to this problem by applying robust linear regression to comparative trait data. We formally introduce robust phylogenetic regression to the PCM toolkit with linear estimators that are less sensitive to model violations than the standard least-squares estimator, while still retaining high power to detect true trait associations. Our analyses also highlight an ingenuity of the original algorithm for phylogenetic regression based on independent contrasts, whereby robust estimators are particularly effective. Collectively, we find that robust estimators hold promise for improving tests of trait associations and offer a path forward in scenarios where classical approaches may fail. Our study joins recent arguments for increased vigilance against unreplicated evolution and a better understanding of evolutionary model performance in challenging-yet biologically important-settings.

The Cauchy Process on Phylogenies: A Tractable Model for Pulsed Evolution.

Phylogenetic comparative methods use random processes, such as the Brownian Motion, to model the evolution of continuous traits on phylogenetic trees. Growing evidence for non-gradual evolution motivated the development of complex models, often based on Lévy processes. However, their statistical inference is computationally intensive and currently relies on approximations, high-dimensional sampling, or numerical integration. We consider here the Cauchy Process (CP), a particular pure-jump Lévy process in which the trait increment along each branch follows a centered Cauchy distribution with a dispersion proportional to its length. In this work, we derive an exact algorithm to compute both the joint probability density of the tip trait values of a phylogeny under a CP and the ancestral trait values and branch increments posterior densities in quadratic time. A simulation study shows that the CP generates patterns in comparative data that are distinct from any Gaussian process, and that restricted maximum likelihood parameter estimates and root trait reconstruction are unbiased and accurate for trees with 200 tips or less. The CP has only two parameters but is rich enough to capture complex-pulsed evolution. It can reconstruct posterior ancestral trait distributions that are multimodal, reflecting the uncertainty associated with the inference of the evolutionary history of a trait from extant taxa only. Applied on empirical datasets taken from the Evolutionary Ecology and Virology literature, the CP suggests nuanced scenarios for the body size evolution of Greater Antilles Lizards and for the geographical spread of the West Nile Virus epidemics in North America, both consistent with previous studies using more complex models. The method is efficiently implemented in C with an R interface in package cauphy, which is open source and freely available online.

Genetic Analysis of Stripe Rust Resistance in the Chinese Wheat Cultivar Luomai 163.

Stripe or yellow rust (YR) caused by Puccinia striiformis tritici (Pst) is an important foliar disease affecting wheat production globally. Resistant varieties are the most economically and environmentally effective way to manage this disease. The common winter wheat (Triticum aestivum L.) cultivar Luomai 163 exhibited resistance to Pst races CYR32 and CYR33 at the seedling stage and showed a high level adult plant resistance in the field. To understand the genetic basis of YR resistance in this cultivar, 142 F5 recombinant inbred lines (RILs) derived from cross Apav#1 × LM163 and both parents were genotyped with the 16K SNP array and bulked segregant analysis sequencing (BSA-Seq). The analysis detected a major gene, YrLM163, at the seedling stage associated with the 1BL.1RS translocation. Additionally, three genes for resistance at the adult plant stage were detected on chromosome arms 1BL (Lr46/Yr29/Pm39/Sr58), 6BS and 6BL in Luomai 163, whereas Apav#1 contributed resistance at a QTL on 2BL. These QTL explained YR disease severity variations ranging from 6.9 to 54.8%. KASP markers KASP-2BL, KASP-6BS and KASP-6BL for three novel loci QYr.hzau-2BL, QYr.hzau-6BS and QYr.hzau-6BL were developed and validated. QYr.hzau-1BL, QYr.hzau-2BL and QYr.hzau-6BS showed varying degrees of resistance to YR when present individually or in combination based on genotype and phenotype analysis of a panel of 570 wheat accessions. Six RILs combining resistance alleles of all QTL, showing higher resistance to YR in the field than Luomai 163 with disease severities of 10.7-16.0%, are important germplasm resources for breeding programs to develop YR resistant wheat varieties with good agronomic traits.

Engineering homoeologs provide a fine scale for quantitative traits in polyploid.

Numerous staple crops exhibit polyploidy and are difficult to genetically modify. However, recent advances in genome sequencing and editing have enabled polyploid genome engineering. The hexaploid black nightshade species Solanum nigrum has immense potential as a beneficial food supplement. We assembled its genome at the scaffold level. After functional annotations, we identified homoeologous gene sets, with similar sequence and expression profiles, based on comparative analyses of orthologous genes with close diploid relatives Solanum americanum and S. lycopersicum. Using CRISPR-Cas9-mediated mutagenesis, we generated various mutation combinations in homoeologous genes. Multiple mutants showed quantitative phenotypic changes based on the genotype, resulting in a broad-spectrum effect on the quantitative traits of hexaploid S. nigrum. Furthermore, we successfully improved the fruit productivity of Boranong, an orphan cultivar of S. nigrum suggesting that engineering homoeologous genes could be useful for agricultural improvement of polyploid crops.

Genomic architecture of phenotypic extremes in a wild cervid.

Identifying the genes underlying fitness-related traits such as body size and male ornamentation can provide tools for conservation and management and are often subject to various selective pressures. Here we performed high-depth whole genome re-sequencing of pools of individuals representing the phenotypic extremes for antler and body size in white-tailed deer (Odocoileus virginianus). Samples were selected from a tissue repository containing phenotypic data for 4,466 male white-tailed deer from Anticosti Island, Quebec, with four pools representing the extreme phenotypes for antler and body size after controlling for age. Our results revealed a largely homogenous population but detected highly divergent windows between pools for both traits, with the mean allele frequency difference of 14% for and 13% for antler and body SNPs in outlier windows, respectively. Genes in outlier antler windows were enriched for pathways associated with cell death and protein metabolism and some of the most differentiated windows included genes associated with oncogenic pathways and reproduction, processes consistent with antler evolution and growth. Genes associated with body size were more nuanced, suggestive of a highly complex trait. Overall, this study revealed the complex genomic make-up of both antler morphology and body size in free-ranging white-tailed deer and identified target loci for additional analyses.

Genomic correlates for migratory direction in a free-ranging cervid.

Animal migrations are some of the most ubiquitous and one of the most threatened ecological processes globally. A wide range of migratory behaviours occur in nature, and this behaviour is not uniform among and within species, where even individuals in the same population can exhibit differences. While the environment largely drives migratory behaviour, it is necessary to understand the genetic mechanisms influencing migration to elucidate the potential of migratory species to cope with novel conditions and adapt to environmental change. In this study, we identified genes associated with a migratory trait by undertaking pooled genome-wide scans on a natural population of migrating mule deer. We identified genomic regions associated with variation in migratory direction, including FITM1, a gene linked to the formation of lipids, and DPPA3, a gene linked to epigenetic modifications of the maternal line. Such a genetic basis for a migratory trait contributes to the adaptive potential of the species and might affect the flexibility of individuals to change their behaviour in the face of changes in their environment.

Within-family influences on dimensional neurobehavioral traits in a high-risk genetic model.

BACKGROUND: Genotype-first and within-family studies can elucidate factors that contribute to psychiatric illness. Combining these approaches, we investigated the patterns of influence of parental scores, a high-impact variant, and schizophrenia on dimensional neurobehavioral phenotypes implicated in major psychiatric disorders. METHODS: We quantitatively assessed cognitive (FSIQ, VIQ, PIQ), social, and motor functioning in 82 adult individuals with a de novo 22q11.2 deletion (22 with schizophrenia), and 148 of their unaffected parents. We calculated within-family correlations and effect sizes of the 22q11.2 deletion and schizophrenia, and used linear regressions to assess contributions to neurobehavioral measures. RESULTS: Proband-parent intra-class correlations (ICC) were significant for cognitive measures (e.g. FSIQ ICC = 0.549, p < 0.0001), but not for social or motor measures. Compared to biparental scores, the 22q11.2 deletion conferred significant impairments for all phenotypes assessed (effect sizes -1.39 to -2.07 s.d.), strongest for PIQ. There were further decrements in those with schizophrenia. Regression models explained up to 37.7% of the variance in IQ and indicated that for proband IQ, parental IQ had larger effects than schizophrenia. CONCLUSIONS: This study, for the first time, disentangles the impact of a high-impact variant from the modifying effects of parental scores and schizophrenia on relevant neurobehavioral phenotypes. The robust proband-parent correlations for cognitive measures, independent of the impact of the 22q11.2 deletion and of schizophrenia, suggest that, for certain phenotypes, shared genetic variation plays a significant role in expression. Molecular genetic and predictor studies are needed to elucidate shared factors and their contribution to psychiatric illness in this and other high-risk groups.

On the Fourier transform of a quantitative trait: Implications for compressive sensing.

This paper explores the genotype-phenotype relationship. It outlines conditions under which the dependence of a quantitative trait on the genome might be predictable, based on measurement of a limited subset of genotypes. It uses the theory of real-valued Boolean functions in a systematic way to translate trait data into the Fourier domain. Important trait features, such as the roughness of the trait landscape or the modularity of a trait have a simple Fourier interpretation. Ruggedness at a gene location corresponds to high sensitivity to mutation, while a modular organization of gene activity reduces such sensitivity. Traits where rugged loci are rare will naturally compress gene data in the Fourier domain, leading to a sparse representation of trait data, concentrated in identifiable, low-level coefficients. This Fourier representation of a trait organizes epistasis in a form which is isometric to the trait data. As Fourier matrices are known to be maximally incoherent with the standard basis, this permits employing compressive sensing techniques to work from data sets that are relatively small-sometimes even of polynomial size-compared to the exponentially large sets of possible genomes. This theory provides a theoretical underpinning for systematic use of Boolean function machinery to dissect the dependency of a trait on the genome and environment.

Plastic and quantitative genetic divergence mirror environmental gradients among wild, fragmented populations of Impatiens capensis.

PREMISE: Habitat fragmentation generates molecular genetic divergence among isolated populations, but few studies have assessed phenotypic divergence and fitness in populations where the genetic consequences of habitat fragmentation are known. Phenotypic divergence could reflect plasticity, local adaptation, and/or genetic drift. METHODS: We examined patterns and potential drivers of phenotypic divergence among 12 populations of jewelweed (Impatiens capensis) that show strong molecular genetic signals of isolation and drift among fragmented habitats. We measured morphological and reproductive traits in both maternal plants within natural populations and their self-fertilized progeny grown together in a common garden. We also quantified environmental divergence between home sites and the common garden. RESULTS: Populations with less molecular genetic variation expressed less maternal phenotypic variation. Progeny in the common garden converged in phenotypes relative to their wild mothers but retained among-population differences in morphology, survival, and reproduction. Among-population phenotypic variance was 3-10× greater in home sites than in the common garden for 6 of 7 morphological traits measured. Patterns of phenotypic divergence paralleled environmental gradients in ways suggestive of adaptation. Progeny resembled their mothers less as the environmental distance between their home site and the common garden increased. CONCLUSIONS: Despite strong molecular signatures of isolation and drift, phenotypic differences among these Impatiens populations appear to reflect both adaptive quantitative genetic divergence and plasticity. Quantifying the extent of local adaptation and plasticity and how these covary with molecular and phenotypic variation help us predict when populations may lose their adaptive capacity.

Quantifying the contribution of sequence variants with regulatory and evolutionary significance to 34 bovine complex traits.

Many genome variants shaping mammalian phenotype are hypothesized to regulate gene transcription and/or to be under selection. However, most of the evidence to support this hypothesis comes from human studies. Systematic evidence for regulatory and evolutionary signals contributing to complex traits in a different mammalian model is needed. Sequence variants associated with gene expression (expression quantitative trait loci [eQTLs]) and concentration of metabolites (metabolic quantitative trait loci [mQTLs]) and under histone-modification marks in several tissues were discovered from multiomics data of over 400 cattle. Variants under selection and evolutionary constraint were identified using genome databases of multiple species. These analyses defined 30 sets of variants, and for each set, we estimated the genetic variance the set explained across 34 complex traits in 11,923 bulls and 32,347 cows with 17,669,372 imputed variants. The per-variant trait heritability of these sets across traits was highly consistent (r > 0.94) between bulls and cows. Based on the per-variant heritability, conserved sites across 100 vertebrate species and mQTLs ranked the highest, followed by eQTLs, young variants, those under histone-modification marks, and selection signatures. From these results, we defined a Functional-And-Evolutionary Trait Heritability (FAETH) score indicating the functionality and predicted heritability of each variant. In additional 7,551 cattle, the high FAETH-ranking variants had significantly increased genetic variances and genomic prediction accuracies in 3 production traits compared to the low FAETH-ranking variants. The FAETH framework combines the information of gene regulation, evolution, and trait heritability to rank variants, and the publicly available FAETH data provide a set of biological priors for cattle genomic selection worldwide.

Automatic generation of evolutionary hypotheses using mixed Gaussian phylogenetic models.

Phylogenetic comparative methods are widely used to understand and quantify the evolution of phenotypic traits, based on phylogenetic trees and trait measurements of extant species. Such analyses depend crucially on the underlying model. Gaussian phylogenetic models like Brownian motion and Ornstein-Uhlenbeck processes are the workhorses of modeling continuous-trait evolution. However, these models fit poorly to big trees, because they neglect the heterogeneity of the evolutionary process in different lineages of the tree. Previous works have addressed this issue by introducing shifts in the evolutionary model occurring at inferred points in the tree. However, for computational reasons, in all current implementations, these shifts are "intramodel," meaning that they allow jumps in 1 or 2 model parameters, keeping all other parameters "global" for the entire tree. There is no biological reason to restrict a shift to a single model parameter or, even, to a single type of model. Mixed Gaussian phylogenetic models (MGPMs) incorporate the idea of jointly inferring different types of Gaussian models associated with different parts of the tree. Here, we propose an approximate maximum-likelihood method for fitting MGPMs to comparative data comprising possibly incomplete measurements for several traits from extant and extinct phylogenetically linked species. We applied the method to the largest published tree of mammal species with body- and brain-mass measurements, showing strong statistical support for an MGPM with 12 distinct evolutionary regimes. Based on this result, we state a hypothesis for the evolution of the brain-body-mass allometry over the past 160 million y.

Characterization and exploring genetic potential of landraces from Odisha with special emphasis on grain micronutrient content for benefaction of biofortification in rice.

Assessing genetic variability of micronutrient content in association with qualitative and quantitative traits in germplasm is prerequisite for effective biofortification programme. Odisha, a state of eastern India is considered as one of the most potential hot spot of diversity of cultivated rice for grain yield and nutritional traits. Significant variability for most of the qualitative and quantitative traits including Fe and Zn content was observed in a set of 293 germplasm with varying kernel colour encompassing 14 districts of Odisha. Mostly these landraces were low yielding with some exception (Haldigundi: AC 36454, 50.08 g/plant). These landraces were mostly represented by medium Fe (10-20 ppm)-medium Zn group (20-30 ppm). Fe and Zn content had positive association with each other and also with grain size. Landraces with red kernel colour were observed to have slightly higher average Zn content (26.30 ppm) as compared to white (25.87 ppm) grains. Diversity analysis of 14 districts revealed that Nayagarh, a south-eastern district was rich in Fe content while Deogarh, Keonjhar and Mayurbhanj, all north-western districts were rich in Zn content. This study identified 10 superior micronutrient dense genotypes with medium to high Fe and Zn content. This set of donors for micronutrient content was validated in another year. Champeisiali (AC 43368) and Gedemalati (AC 34306) with highest Fe (44.1 ppm) and Zn (40.48 ppm) content, respectively were detected over the environments. Identified donors and associated traits could be utilized in biofortificaion programme using appropriate breeding methodologies for enhancing micronutrients in high yielding background. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12298-021-01119-7.

Genetic Regulatory Mechanisms of Smooth Muscle Cells Map to Coronary Artery Disease Risk Loci.

Coronary artery disease (CAD) is the leading cause of death globally. Genome-wide association studies (GWASs) have identified more than 95 independent loci that influence CAD risk, most of which reside in non-coding regions of the genome. To interpret these loci, we generated transcriptome and whole-genome datasets using human coronary artery smooth muscle cells (HCASMCs) from 52 unrelated donors, as well as epigenomic datasets using ATAC-seq on a subset of 8 donors. Through systematic comparison with publicly available datasets from GTEx and ENCODE projects, we identified transcriptomic, epigenetic, and genetic regulatory mechanisms specific to HCASMCs. We assessed the relevance of HCASMCs to CAD risk using transcriptomic and epigenomic level analyses. By jointly modeling eQTL and GWAS datasets, we identified five genes (SIPA1, TCF21, SMAD3, FES, and PDGFRA) that may modulate CAD risk through HCASMCs, all of which have relevant functional roles in vascular remodeling. Comparison with GTEx data suggests that SIPA1 and PDGFRA influence CAD risk predominantly through HCASMCs, while other annotated genes may have multiple cell and tissue targets. Together, these results provide tissue-specific and mechanistic insights into the regulation of a critical vascular cell type associated with CAD in human populations.

Ecological Consequences of Intraspecific Variation in Coevolutionary Systems.

AbstractThe patterns and outcomes of coevolution are expected to depend on intraspecific trait variation. Various evolutionary factors can change this variation in time. As a result, modeling coevolutionary processes solely in terms of mean trait values may not be sufficient; one may need to study the dynamics of the whole trait distribution. Here, we develop a theoretical framework for studying the effects of evolving intraspecific variation in two-species coevolutionary systems. In particular, we build and study mathematical models of competition, exploiter-victim interactions, and mutualism in which the strength of within- and between-species interactions depends on the difference in continuously varying traits between individuals reproducing asexually. We use analytical approximations based on the invasion analysis and supplement them with numerical results. We find that intraspecific variation can be maintained if stabilizing selection is weak in at least one species. When intraspecific variation is maintained under competition or mutualism, coexistence in a stable equilibrium is promoted when between-species interactions mostly happen between individuals similar in trait values. In contrast, in exploiter-victim systems coexistence typically requires strong interactions between dissimilar exploiters and victims. We show that trait distributions can become multimodal. Our approach and results contribute to the understanding of the ecological consequences of intraspecific variation in coevolutionary systems by exploring its effects on population densities and trait distributions.