Polygenic Selection Research Articles

A path integral approach for allele frequency dynamics under polygenic selection

Abstract Many phenotypic traits have a polygenic genetic basis, making it challenging to learn their genetic architectures and predict individual phenotypes. One promising avenue to resolve the genetic basis of complex traits is through evolve-and-resequence experiments, in which laboratory populations are exposed to some selective pressure and trait-contributing loci are identified by extreme frequency changes over the course of the experiment. However, small laboratory populations will experience substantial random genetic drift, and it is difficult to determine whether selection played a role in a given allele frequency change. Predicting allele frequency changes under drift and selection, even for alleles contributing to simple, monogenic traits, has remained a challenging problem. Recently, there have been efforts to apply the path integral, a method borrowed from physics, to solve this problem. So far, this approach has been limited to genic selection, and is therefore inadequate to capture the complexity of quantitative, highly polygenic traits that are commonly studied. Here we extend one of these path integral methods, the perturbation approximation, to selection scenarios that are of interest to quantitative genetics. We derive analytic expressions for the transition probability (i.e., the probability that an allele will change in frequency from x to y in time t) of an allele contributing to a trait subject to stabilizing selection, as well as that of an allele contributing to a trait rapidly adapting to a new phenotypic optimum. We use these expressions to characterize the use of allele frequency change to test for selection, as well as explore optimal design choices for evolve-and-resequence experiments to uncover the genetic architecture of polygenic traits under selection.

Polygenic selection to a changing optimum under self-fertilisation.

Many traits are polygenic, affected by multiple genetic variants throughout the genome. Selection acting on these traits involves co-ordinated allele-frequency changes at these underlying variants, and this process has been extensively studied in random-mating populations. Yet many species self-fertilise to some degree, which incurs changes to genetic diversity, recombination and genome segregation. These factors cumulatively influence how polygenic selection is realised in nature. Here, we use analytical modelling and stochastic simulations to investigate to what extent self-fertilisation affects polygenic adaptation to a new environment. Our analytical solutions show that while selfing can increase adaptation to an optimum, it incurs linkage disequilibrium that can slow down the initial spread of favoured mutations due to selection interference, and favours the fixation of alleles with opposing trait effects. Simulations show that while selection interference is present, high levels of selfing (at least 90%) aids adaptation to a new optimum, showing a higher long-term fitness. If mutations are pleiotropic then only a few major-effect variants fix along with many neutral hitchhikers, with a transient increase in linkage disequilibrium. These results show potential advantages to self-fertilisation when adapting to a new environment, and how the mating system affects the genetic composition of polygenic selection.

A Path Integral Approach for Allele Frequency Dynamics Under Polygenic Selection.

Many phenotypic traits have a polygenic genetic basis, making it challenging to learn their genetic architectures and predict individual phenotypes. One promising avenue to resolve the genetic basis of complex traits is through evolve-and-resequence experiments, in which laboratory populations are exposed to some selective pressure and trait-contributing loci are identified by extreme frequency changes over the course of the experiment. However, small laboratory populations will experience substantial random genetic drift, and it is difficult to determine whether selection played a roll in a given allele frequency change. Predicting how much allele frequencies change under drift and selection had remained an open problem well into the 21st century, even those contributing to simple, monogenic traits. Recently, there have been efforts to apply the path integral, a method borrowed from physics, to solve this problem. So far, this approach has been limited to genic selection, and is therefore inadequate to capture the complexity of quantitative, highly polygenic traits that are commonly studied. Here we extend one of these path integral methods, the perturbation approximation, to selection scenarios that are of interest to quantitative genetics. In particular, we derive analytic expressions for the transition probability (i.e., the probability that an allele will change in frequency from , to in time ) of an allele contributing to a trait subject to stabilizing selection, as well as that of an allele contributing to a trait rapidly adapting to a new phenotypic optimum. We use these expressions to characterize the use of allele frequency change to test for selection, as well as explore optimal design choices for evolve-and-resequence experiments to uncover the genetic architecture of polygenic traits under selection.

Agricultural insect pests as models for studying stress-induced evolutionary processes.

Agricultural insect pests (AIPs) are widely successful in adapting to natural and anthropogenic stressors, repeatedly overcoming population bottlenecks and acquiring resistance to intensive management practices. Although they have been largely overlooked in evolutionary studies, AIPs are ideal systems for understanding rapid adaptation under novel environmental conditions. Researchers have identified several genomic mechanisms that likely contribute to adaptive stress responses, including positive selection on de novo mutations, polygenic selection on standing allelic variation and phenotypic plasticity (e.g., hormesis). However, new theory suggests that stress itself may induce epigenetic modifications, which may confer heritable physiological changes (i.e., stress-resistant phenotypes). In this perspective, we discuss how environmental stress from agricultural management generates the epigenetic and genetic modifications that are associated with rapid adaptation in AIPs. We summarise existing evidence for stress-induced evolutionary processes in the context of insecticide resistance. Ultimately, we propose that studying AIPs offers new opportunities and resources for advancing our knowledge of stress-induced evolution.

Abstract 7322: Polygenic adaptation and co-regulatory dynamics in Chernobyl wolves: Unveiling immune and oncogenic stress interactions with implications for human cancer resilience

Abstract Wolves living in the Chernobyl Exclusion Zone (CEZ) for at least six generations have adapted to high radiation exposure and are a unique model for studying genetic selection under extreme conditions of oncogenic stress. The objective of this study was to build off previously described signatures of adaptation in CEZ wolves, identify networks of genes associated with immune adaptation in CEZ wolves, and determine prognostic significance of these genes in human cancer datasets to provide insight into immune adaptation and cancer immunity. RNA from blood samples of wolves in CEZ, Belarus (BLR), and Yellowstone (YLS) were sequenced previously. Here we analyzed these data by mapping wolf transcripts to human orthologs. Radiation exposure (in CEZ and BLR) was quantified by intramuscular Cesium-137. Differential gene expression (DESeq2), network co-expression analysis (lmQCM), and gene set variation analysis (GSVA) assessed transcriptomic changes between populations. Germline single nucleotide polymorphism (SNP) for all three populations were inferred from RNA-seq data. Genes differentially regulated between populations, including gene modules showing signatures of polygenic selection through increased rates of genetic divergence (p< 0.05) in CEZ wolf populations, were deemed as outlier genes. Those n=3,180 CEZ outlier genes were then investigated in human cancer data from The Cancer Genome Atlas (TCGA) to determine prognostic impact in 10 tumor types highly conserved between human and canines. GSVA confirmed activation of neutrophil and macrophage signatures in CEZ wolves and suppression of several adaptive immune-related signatures (p < 0.05 vs BLR and YLS). A signature including immune checkpoint targets was also significantly differentially expressed in CEZ wolves. In TCGA, Cox PH analysis identified 23 genes over-expressed in CEZ wolves with significant positive prognostic impact in ≥ 2 human tumor types. PTPN6, previously identified to have positive prognostic value in bladder cancer1, was also prognostic in three additional tumor types. In human bladder cancer, genes identified as differentially regulated in CEZ wolves were disproportionately associated with improved survival. As predicted, gene signature variations in CEZ wolves reveal a distinct immune profile, likely shaped by prolonged radiation exposure. These findings, along with evidence of polygenic selection, suggest adaptation to multigenerational radiation exposure (an oncogenic stress). Notably, the enrichment of genes with positive prognosis in human cancer overexpressed in CEZ wolves present a valuable model to explore genetic underpinnings of cancer immunity and advance our understanding of cancer resilience in humans. Reference: 1Shen C. et al. The Analysis of PTPN6 for Bladder Cancer: An Exploratory Study Based on TCGA. Dis Markers. 2020. Citation Format: Christina Y. Yu, Cara N. Love, Merzu Belete, Hareesh Chandrupatla, Lauren K. Brady, Sriram Sridhar, Shane C. Campbell-Staton. Polygenic adaptation and co-regulatory dynamics in Chernobyl wolves: Unveiling immune and oncogenic stress interactions with implications for human cancer resilience [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7322.

Polygenic architecture of flowering time and its relationship with local environments in the grass Brachypodium distachyon.

Synchronizing the timing of reproduction with the environment is crucial in the wild. Among the multiple mechanisms, annual plants evolved to sense their environment, the requirement of cold-mediated vernalization is a major process that prevents individuals from flowering during winter. In many annual plants including crops, both a long and short vernalization requirement can be observed within species, resulting in so-called early-(spring) and late-(winter) flowering genotypes. Here, using the grass model Brachypodium distachyon, we explored the link between flowering-time-related traits (vernalization requirement and flowering time), environmental variation, and diversity at flowering-time genes by combining measurements under greenhouse and outdoor conditions. These experiments confirmed that B. distachyon natural accessions display large differences regarding vernalization requirements and ultimately flowering time. We underline significant, albeit quantitative effects of current environmental conditions on flowering-time-related traits. While disentangling the confounding effects of population structure on flowering-time-related traits remains challenging, population genomics analyses indicate that well-characterized flowering-time genes may contribute significantly to flowering-time variation and display signs of polygenic selection. Flowering-time genes, however, do not colocalize with genome-wide association peaks obtained with outdoor measurements, suggesting that additional genetic factors contribute to flowering-time variation in the wild. Altogether, our study fosters our understanding of the polygenic architecture of flowering time in a natural grass system and opens new avenues of research to investigate the gene-by-environment interaction at play for this trait.

Uncovering the architecture of selection in two Bos taurus cattle breeds.

Directional selection alters the genome via hard sweeps, soft sweeps, and polygenic selection. However, mapping polygenic selection is difficult because it does not leave clear signatures on the genome like a selective sweep. In populations with temporally stratified genotypes, the Generation Proxy Selection Mapping (GPSM) method identifies variants associated with generation number (or appropriate proxy) and thus variants undergoing directional allele frequency changes. Here, we use GPSM on two large datasets of beef cattle to detect associations between an animal's generation and 11 million imputed SNPs. Using these datasets with high power and dense mapping resolution, GPSM detected a total of 294 unique loci actively under selection in two cattle breeds. We observed that GPSM has a high power to detect selection in the very recent past (<10 years), even when allele frequency changes are small. Variants identified by GPSM reside in genomic regions associated with known breed-specific selection objectives, such as fertility and maternal ability in Red Angus, and carcass merit and coat color in Simmental. Over 60% of the selected loci reside in or near (<50 kb) annotated genes. Using haplotype-based and composite selective sweep statistics, we identify hundreds of putative selective sweeps that likely occurred earlier in the evolution of these breeds; however, these sweeps have little overlap with recent polygenic selection. This makes GPSM a complementary approach to sweep detection methods when temporal genotype data are available. The selected loci that we identify across methods demonstrate the complex architecture of selection in domesticated cattle.

Analysis of polygenic selection in purebred and crossbred pig genomes using generation proxy selection mapping

BackgroundArtificial selection on quantitative traits using breeding values and selection indices in commercial livestock breeding populations causes changes in allele frequency over time at hundreds or thousands of causal loci and the surrounding genomic regions. In population genetics, this type of selection is called polygenic selection. Researchers and managers of pig breeding programs are motivated to understand the genetic basis of phenotypic diversity across genetic lines, breeds, and populations using selection mapping analyses. Here, we applied generation proxy selection mapping (GPSM), a genome-wide association analysis of single nucleotide polymorphism (SNP) genotypes (38,294–46,458 markers) of birth date, in four pig populations (15,457, 15,772, 16,595 and 8447 pigs per population) to identify loci responding to artificial selection over a period of five to ten years. Gene-drop simulation analyses were conducted to provide context for the GPSM results. Selected loci within and across each population of pigs were compared in the context of swine breeding objectives.ResultsThe GPSM identified 49 to 854 loci as under selection (Q-values less than 0.10) across 15 subsets of pigs based on combinations of populations. The number of significant associations increased when data were pooled across populations. In addition, several significant associations were identified in more than one population. These results indicate concurrent selection objectives, similar genetic architectures, and shared causal variants responding to selection across these pig populations. Negligible error rates (less than or equal to 0.02%) of false-positive associations were found when testing GPSM on gene-drop simulated genotypes, suggesting that GPSM distinguishes selection from random genetic drift in actual pig populations.ConclusionsThis work confirms the efficacy and the negligible error rates of the GPSM method in detecting selected loci in commercial pig populations. Our results suggest shared selection objectives and genetic architectures across swine populations. The identified polygenic selection highlights loci that are important to swine production.

A gene-level test for directional selection on gene expression.

Most variants identified in human genome-wide association studies and scans for selection are noncoding. Interpretation of their effects and the way in which they contribute to phenotypic variation and adaptation in human populations is therefore limited by our understanding of gene regulation and the difficulty of confidently linking noncoding variants to genes. To overcome this, we developed a gene-wise test for population-specific selection based on combinations of regulatory variants. Specifically, we use the QX statistic to test for polygenic selection on cis-regulatory variants based on whether the variance across populations in the predicted expression of a particular gene is higher than expected under neutrality. We then applied this approach to human data, testing for selection on 17,388 protein-coding genes in 26 populations from the Thousand Genomes Project. We identified 45 genes with significant evidence (FDR<0.1) for selection, including FADS1, KHK, SULT1A2, ITGAM, and several genes in the HLA region. We further confirm that these signals correspond to plausible population-level differences in predicted expression. While the small number of significant genes (0.2%) is consistent with most cis-regulatory variation evolving under genetic drift or stabilizing selection, it remains possible that there are effects not captured in this study. Our gene-level QX score is independent of standard genomic tests for selection, and may therefore be useful in combination with traditional selection scans to specifically identify selection on regulatory variation. Overall, our results demonstrate the utility of combining population-level genomic data with functional data to understand the evolution of gene expression.

Genome-wide parallelism underlies contemporary adaptation in urban lizards

Urbanization drastically transforms landscapes, resulting in fragmentation, degradation, and the loss of local biodiversity. Yet, urban environments also offer opportunities to observe rapid evolutionary change in wild populations that survive and even thrive in these novel habitats. In many ways, cities represent replicated "natural experiments" in which geographically separated populations adaptively respond to similar selection pressures over rapid evolutionary timescales. Little is known, however, about the genetic basis of adaptive phenotypic differentiation in urban populations nor the extent to which phenotypic parallelism is reflected at the genomic level with signatures of parallel selection. Here, we analyzed the genomic underpinnings of parallel urban-associated phenotypic change in Anolis cristatellus, a small-bodied neotropical lizard found abundantly in both urbanized and forested environments. We show that phenotypic parallelism in response to parallel urban environmental change is underlain by genomic parallelism and identify candidate loci across the Anolis genome associated with this adaptive morphological divergence. Our findings point to polygenic selection on standing genetic variation as a key process to effectuate rapid morphological adaptation. Identified candidate loci represent several functions associated with skeletomuscular development, morphology, and human disease. Taken together, these results shed light on the genomic basis of complex morphological adaptations, provide insight into the role of contingency and determinism in adaptation to novel environments, and underscore the value of urban environments to address fundamental evolutionary questions.

Natural selection of immune and metabolic genes associated with health in two lowland Bolivian populations

A growing body of work has addressed human adaptations to diverse environments using genomic data, but few studies have connected putatively selected alleles to phenotypes, much less among underrepresented populations such as Amerindians. Studies of natural selection and genotype-phenotype relationships in underrepresented populations hold potential to uncover previously undescribed loci underlying evolutionarily and biomedically relevant traits. Here, we worked with the Tsimane and the Moseten, two Amerindian populations inhabiting the Bolivian lowlands. We focused most intensively on the Tsimane, because long-term anthropological work with this group has shown that they have a high burden of both macro and microparasites, as well as minimal cardiometabolic disease or dementia. We therefore generated genome-wide genotype data for Tsimane individuals to study natural selection, and paired this with blood mRNA-seq as well as cardiometabolic and immune biomarker data generated from a larger sample that included both populations. In the Tsimane, we identified 21 regions that are candidates for selective sweeps, as well as 5 immune traits that show evidence for polygenic selection (e.g., C-reactive proteinlevels and the response to coronaviruses). Genes overlapping candidate regions were strongly enriched for known involvement in immune-related traits, such as abundance of lymphocytes and eosinophils. Importantly, we were also able to draw on extensive phenotype information for the Tsimane and Moseten and link five regions (containing PSD4, MUC21 and MUC22, TOX2, ANXA6, and ABCA1) with biomarkers of immune and metabolic function. Together, our work highlights the utility of pairing evolutionary analyses with anthropological and biomedical data to gain insight into the genetic basis of health-related traits.

Ghat: an R package for identifying adaptive polygenic traits.

Identifying selection on polygenic complex traits in crops and livestock is important for understanding evolution and helps prioritize important characteristics for breeding. Quantitative trait loci (QTL) that contribute to polygenic trait variation often exhibit small or infinitesimal effects. This hinders the ability to detect QTL-controlling polygenic traits because enormously high statistical power is needed for their detection. Recently, we circumvented this challenge by introducing a method to identify selection on complex traits by evaluating the relationship between genome-wide changes in allele frequency and estimates of effect size. The approach involves calculating a composite statistic across all markers that capture this relationship, followed by implementing a linkage disequilibrium-aware permutation test to evaluate if the observed pattern differs from that expected due to drift during evolution and population stratification. In this manuscript, we describe "Ghat," an R package developed to implement this method to test for selection on polygenic traits. We demonstrate the package by applying it to test for polygenic selection on 15 published European wheat traits including yield, biomass, quality, morphological characteristics, and disease resistance traits. Moreover, we applied Ghat to different simulated populations with different breeding histories and genetic architectures. The results highlight the power of Ghat to identify selection on complex traits. The Ghat package is accessible on CRAN, the Comprehensive R Archival Network, and on GitHub.

Direct detection of natural selection in Bronze Age Britain.

We developed a novel method for efficiently estimating time-varying selection coefficients from genome-wide ancient DNA data. In simulations, our method accurately recovers selective trajectories and is robust to misspecification of population size. We applied it to a large data set of ancient and present-day human genomes from Britain and identified seven loci with genome-wide significant evidence of selection in the past 4500 yr. Almost all of them can be related to increased vitamin D or calcium levels, suggesting strong selective pressure on these or related phenotypes. However, the strength of selection on individual loci varied substantially over time, suggesting that cultural or environmental factors moderated the genetic response. Of 28 complex anthropometric and metabolic traits, skin pigmentation was the only one with significant evidence of polygenic selection, further underscoring the importance of phenotypes related to vitamin D. Our approach illustrates the power of ancient DNA to characterize selection in human populations and illuminates the recent evolutionary history of Britain.

Using neuroimaging genomics to investigate the evolution of human brain structure

Alterations in brain size and organization represent some of the most distinctive changes in the emergence of our species. Yet, there is limited understanding of how genetic factors contributed to altered neuroanatomy during human evolution. Here, we analyze neuroimaging and genetic data from up to 30,000 people in the UK Biobank and integrate with genomic annotations for different aspects of human evolution, including those based on ancient DNA and comparative genomics. We show that previously reported signals of recent polygenic selection for cortical anatomy are not replicable in a more ancestrally homogeneous sample. We then investigate relationships between evolutionary annotations and common genetic variants shaping cortical surface area and white-matter connectivity for each hemisphere. Our analyses identify single-nucleotide polymorphism heritability enrichment in human-gained regulatory elements that are active in early brain development, affecting surface areas of several parts of the cortex, including left-hemispheric speech-associated regions. We also detect heritability depletion in genomic regions with Neanderthal ancestry for connectivity of the uncinate fasciculus; this is a white-matter tract involved in memory, language, and socioemotional processing with relevance to neuropsychiatric disorders. Finally, we show that common genetic loci associated with left-hemispheric pars triangularis surface area overlap with a human-gained enhancer and affect regulation of ZIC4, a gene implicated in neurogenesis. This work demonstrates how genomic investigations of present-day neuroanatomical variation can help shed light on the complexities of our evolutionary past.

29 Mapping Recent and Ongoing Polygenic Selection in Beef Cattle Populations

Abstract Understanding the underlying effects of ongoing selection is of interest to livestock breeding programs. Commercial livestock genotyping has generated datasets well-suited to explore how strong artificial selection has impacted the genome over very short time periods. We have developed a method for mapping ongoing polygenic selection in populations with temporally stratified genomic data called Generation Proxy Selection Mapping (GPSM). GPSM utilizes a genome-wide linear mixed model to identify allelic associations with an animal's generation number or some proxy (i.e., birth date). We applied GPSM to two large contemporary beef cattle datasets from Red Angus (n = 46,454) and Simmental (n = 90,580) breeds, with 11,759,568 high-quality imputed variants per animal. This analysis identified 294 distinct loci actively under selection. Due to the high power of these datasets, we found that GPSM could detect very small and recent(&amp;lt; 10 years) allele frequency changes consistent with polygenic selection. These analyses identified variants within genomic regions associated with known breed characteristics, like fertility and maternal ability in Red Angus and carcass merit and coat color in Simmental. Over 60% of GPSM loci resided in or near (&amp;lt; 50kb) annotated genes. We leveraged the resolution of our imputed sequence variants to overlay known epigenetic marks and possibly functional regions. 36% of GPSM loci overlapped these putatively functional regions genomic intervals. In addition to GPSM, we used two other more traditional selective sweep methods, the number of segregating loci (nSL) and Raised Accuracy in Sweep Detecting (RAiSD), to detect selection in this data. We observed minimal overlap (&amp;lt; 10% of loci) between traditional selective sweep mapping and GPSM. This suggests that selection mapping with GPSM can complement traditional sweep mapping methods when temporal genomic data exists. In addition to better understanding the biological processes and features that underlie artificial selection, GPSM signatures might serve as important genomic annotations.

Insights into the architecture of human-induced polygenic selection in Duroc pigs

BackgroundAs one of the most utilized commercial composite boar lines, Duroc pigs have been introduced to China and undergone strongly human-induced selection over the past decades. However, the efficiencies and limitations of previous breeding of Chinese Duroc pigs are largely understudied. The objective of this study was to uncover directional polygenic selection in the Duroc pig genome, and investigate points overlooked in the past breeding process.ResultsHere, we utilized the Generation Proxy Selection Mapping (GPSM) on a dataset of 1067 Duroc pigs with 8,766,074 imputed SNPs. GPSM detected a total of 5649 putative SNPs actively under selection in the Chinese Duroc pig population, and the potential functions of the selection regions were mainly related to production, meat and carcass traits. Meanwhile, we observed that the allele frequency of variants related to teat number (NT) relevant traits was also changed, which might be influenced by genes that had pleiotropic effects. First, we identified the direction of selection on NT traits by hat{G}, and further pinpointed large-effect genomic regions associated with NT relevant traits by selection signature and GWAS. Combining results of NT relevant traits-specific selection signatures and GWAS, we found three common genome regions, which were overlapped with QTLs related to production, meat and carcass traits besides “teat number” QTLs. This implied that there were some pleiotropic variants underlying NT and economic traits. We further found that rs346331089 has pleiotropic effects on NT and economic traits, e.g., litter size at weaning (LSW), litter weight at weaning (LWW), days to 100 kg (D100), backfat thickness at 100 kg (B100), and loin muscle area at 100 kg (L100) traits.ConclusionsThe selected loci that we identified across methods displayed the past breeding process of Chinese Duroc pigs, and our findings could be used to inform future breeding decision.

Repeated Genetic Targets of Natural Selection Underlying Adaptation of Fishes to Changing Salinity

Ecological transitions across salinity boundaries have led to some of the most important diversification events in the animal kingdom, especially among fishes. Adaptations accompanying such transitions include changes in morphology, diet, whole-organism performance, and osmoregulatory function, which may be particularly prominent since divergent salinity regimes make opposing demands on systems that maintain ion and water balance. Research in the last decade has focused on the genetic targets underlying such adaptations, most notably by comparing populations of species that are distributed across salinity boundaries. Here, we synthesize research on the targets of natural selection using whole-genome approaches, with a particular emphasis on the osmoregulatory system. Given the complex, integrated and polygenic nature of this system, we expected that signatures of natural selection would span numerous genes across functional levels of osmoregulation, especially salinity sensing, hormonal control, and cellular ion exchange mechanisms. We find support for this prediction: genes coding for V-type, Ca2+, and Na+/K+-ATPases, which are key cellular ion exchange enzymes, are especially common targets of selection in species from six orders of fishes. This indicates that while polygenic selection contributes to adaptation across salinity boundaries, changes in ATPase enzymes may be of particular importance in supporting such transitions.

Genome-wide association study of eigenvectors provides genetic insights into selective breeding for tomato metabolites

BackgroundLong-term domestication and intensive breeding of crop plants aim to establish traits desirable for human needs, and characteristics related to yield, disease resistance, and postharvest storage have traditionally received considerable attention. These processes have led also to negative consequences, as is the case of loss of variants controlling fruit quality, for instance in tomato. Tomato fruit quality is directly associated to metabolite content profiles; however, a full understanding of the genetics affecting metabolite content during tomato domestication and improvement has not been reached due to limitations of the single detection methods previously employed. Here, we aim to reach a broad understanding of changes in metabolite content using a genome-wide association study (GWAS) with eigenvector decomposition (EigenGWAS) on tomato accessions.ResultsAn EigenGWAS was performed on 331 tomato accessions using the first eigenvector generated from the genomic data as a “phenotype” to understand the changes in fruit metabolite content during breeding. Two independent gene sets were identified that affected fruit metabolites during domestication and improvement in consumer-preferred tomatoes. Furthermore, 57 candidate genes related to polyphenol and polyamine biosynthesis were discovered, and a major candidate gene chlorogenate: glucarate caffeoyltransferase (SlCGT) was identified, which affected the quality and diseases resistance of tomato fruit, revealing the domestication mechanism of polyphenols.ConclusionsWe identified gene sets that contributed to consumer liking during domestication and improvement of tomato. Our study reports novel evidence of selective sweeps and key metabolites controlled by multiple genes, increasing our understanding of the mechanisms of metabolites variation during those processes. It also supports a polygenic selection model for the application of tomato breeding.

Future-proofing the koala: Synergising genomic and environmental data for effective species management.

Climatic and evolutionary processes are inextricably linked to conservation. Avoiding extinction in rapidly changing environments often depends upon a species' capacity to adapt in the face of extreme selective pressures. Here, we employed exon capture and high-throughput next-generation sequencing to investigate the mechanisms underlying population structure and adaptive genetic variation in the koala (Phascolarctos cinereus), an iconic Australian marsupial that represents a unique conservation challenge because it is not uniformly threatened across its range. An examination of 250specimens representing 91 wild source locations revealed that five major genetic clusters currently exist on a continental scale. The initial divergence of these clusters appears to have been concordant with the Mid-Brunhes Transition (~430 to 300kya), a major climatic reorganisation that increased the amplitude of Pleistocene glacial-interglacial cycles. While signatures of polygenic selection and environmental adaptation were detected, strong evidence for repeated, climate-associated range contractions and demographic bottleneck events suggests that geographically isolated refugia may have played a more significant role in the survival of the koala through the Pleistocene glaciation than in situ adaptation. Consequently, the conservation of genome-wide genetic variation must be aligned with the protection of core koala habitat to increase the resilience of vulnerable populations to accelerating anthropogenic threats. Finally, we propose that the five major genetic clusters identified in this study should be accounted for in future koala conservation efforts (e.g., guiding translocations), as existing management divisions in the states of Queensland and New South Wales do not reflect historic or contemporary population structure.

Understanding signatures of positive natural selection in human zinc transporter genes

Zinc is an essential micronutrient with a tightly regulated systemic and cellular homeostasis. In humans, some zinc transporter genes (ZTGs) have been previously reported as candidates for strong geographically restricted selective sweeps. However, since zinc homeostasis is maintained by the joint action of 24 ZTGs, other more subtle modes of selection could have also facilitated human adaptation to zinc availability. Here, we studied whether the complete set of ZTGs are enriched for signals of positive selection in worldwide populations and population groups from South Asia. ZTGs showed higher levels of genetic differentiation between African and non-African populations than would be randomly expected, as well as other signals of polygenic selection outside Africa. Moreover, in several South Asian population groups, ZTGs were significantly enriched for SNPs with unusually extended haplotypes and displayed SNP genotype-environmental correlations when considering zinc deficiency levels in soil in that geographical area. Our study replicated some well-characterized targets for positive selection in East Asia and sub-Saharan Africa, and proposes new candidates for follow-up in South Asia (SLC39A5) and Africa (SLC39A7). Finally, we identified candidate variants for adaptation in ZTGs that could contribute to different disease susceptibilities and zinc-related human health traits.