Indian-origin Gir and Ongole cattle are international transboundary breeds that are reared in Brazil, The United States, Mexico, Malaysia, Panama, and other nations to provide meat and dairy products. These breeds have shown substantial genetic diversity in recent years and well suited to the ecological niche in Brazil. 90 cattle samples of Indian Gir (n = 15), Ongole (n = 17), Brazilian Gyr (n = 27), and Nellore (n = 31) breeds were genotyped using Illumina BovineHD BeadChip. Samples were analyzed to identify selection signatures using two complementing approaches: Integrated Haplotype Score (iHS) and Fixation Index (FST). Gir versus Gyr and Ongole versus Nellore revealed Pairwise FST differences of 2.85 % and 2.35 %, respectively. Using integrated haplotype score (iHS) method, 4004, 3322, 3437, and 3485 genes were found in Gir, Gyr, Ongole, and Nellore, respectively, underlying top 1 % of selected regions. Under top 1 % of selected regions, FST based method identified1897 genes for the Ongole-Nellore pair and 1966 genes for the Gir-Gyr pair. Runs of homozygosity (ROH) analysis revealed that both recent as well as ancient inbreeding in these breeds were in range of 2.6-4.5 % indicating populations to be less inbred. Numerous candidate genes, including IER5, MILR1 (immunity related traits) in Gir; and FGF12, SV2C, JMY (average daily gain, body size, reproduction related traits) in Ongole, were found under the top-selected regions. Nellore breed had carcass/growth traits (PARP2, and KCNJ11) and genes linked to mammary gland development, udder size, and carcass (MYO16, MYO1B) were found in Gyr. Present findings reveals that Brazilian cattle population (Gyr and Nellore) is more selected for carcass and growth traits along with milk production traits, whereas in Indian cattle population (Gir and Ongole) selection signature related to immunity and adaptation were more prominent. Further, sufficient genetic diversity exist within these cattle breeds for their genetic improvement.

Bolivian Creole cattle populations evolved under low levels of breeding management and, during more than 500 years of natural selection, became adapted to various environments such as the contrasting highland and subtropical environments. Recently, highland Creole cattle were crossbred with Holstein to improve dairy production. The aim of this research was to evaluate the divergent adaptation through selection footprints of Bolivian Creole cattle from Andean highland and tropical lowlands, and to evaluate the effect of Holstein introgression in highland Creole. For this purpose, 130 Creole cattle (75 highland, 55 lowland) and 88 Holstein were genotyped using a microarray. The database was used to determine population structure and admixture and detect selection sweeps using FST, Rsb, XP-EHH, and ROH. Ancestry inference suggested that selection peaks were not due to Holstein introgression. The NCBI database was used to retrieve genes from the common regions and then perform gene ontology analysis. The most prominent selection peaks were on BTA20 and BTA23 and included the PRLR (slick phenotype) and Class I and IIa BoLA genes. Other windows contained candidate genes for hypoxia (ANXA2, NDUFA4L2), angiogenesis and haematological parameters (ANXA2, CPLANE1, NRP1, NRP2), immune response (IL7R, IL6ST, IL31RA, C6, C7, STAT6, NKG2A, IRAK4, KLR, CLEC), oxidative stress (GSTA, HSD17B6) and morphological traits (PLAG1, CHCHD7, CAP2, ARL15). GO analysis revealed enrichment terms and pathways related to immune response, glutathione and retinol metabolism and reported QTLs for coat characteristics, immune response and tick resistance. The results suggest the complex mechanism in the adaptation of Bolivian Creole cattle to the contrasting highland and subtropical environments.

Herbicide resistance in agricultural weeds has become one of the greatest challenges for sustainable crop production. The repeated evolution of herbicide resistance provides an excellent opportunity to study the genetic and physiological basis of the resistance phenotype and the evolutionary responses to human-mediated selection pressures. Lolium multiflorum is a ubiquitous weed that has evolved herbicide resistance repeatedly around the world in various cropping systems. We assembled and annotated a chromosome-scale genome for L. multiflorum and elucidated the genetic architecture of paraquat resistance by performing quantitative trait locus analysis, genome-wide association studies, genetic divergence analysis and transcriptome analyses from paraquat-resistant and -susceptible L. multiflorum plants. We identified two regions on chromosome 5 that were associated with paraquat resistance. These regions both showed evidence for positive selection among the resistant populations we sampled, but the effects of this selection on the genome differed, implying a complex evolutionary history. In addition, these regions contained candidate genes that encoded cellular transport functions, including a novel multidrug and toxin extrusion (MATE) protein and a cation transporter previously shown to interact with polyamines. Given that L. multiflorum is a weed and a cultivated crop species, the genomic resources generated will prove valuable to a wide spectrum of the plant science community. Our work contributes to a growing body of knowledge on the underlying evolutionary and ecological dynamics of rapid adaptation to strong anthropogenic selection pressure that could help initiate efforts to improve weed management practices in the long term for a more sustainable agriculture.

Although depression significantly impacts fitness, some hypotheses suggest that it may offer a survival benefit. However, there has been limited systematic investigation into the selection pressures acting on genes associated with depression at the genomic level. Here, we conducted comparative genomic analyses and computational molecular evolutionary analyses on 320 depression-associated genes at two levels, i.e., across the primate phylogeny (long timescale selection) and in modern human populations (recent selection). We identified seven genes under positive selection in the human lineage, and 46 genes under positive selection in modern human populations. Most positively selected variants in modern human populations were at UTR regions and non-coding exons, indicating the importance of gene expression regulation in the evolution of depression-associated genes. Positively selected genes are not only related to immune responses, but also function in reproduction and dietary adaptation. Notably, the proportion of depression-associated genes under positive selection was significantly higher than the positively selected genes at the genome-wide average level in African, East Asian, and South Asian populations. We also identified two positively selected loci that happened to be associated with depression in the South Asian population. Our study revealed that depression-associated genes are subject to varying selection pressures across different populations. We suggest that, in precision medicine-particularly in gene therapy-it is crucial to consider the specific functions of genes within distinct populations.

Natural selection leaves detectable patterns of altered spatial diversity within genomes, and identifying affected regions is crucial for understanding species evolution. Recently, machine learning approaches applied to raw population genomic data have been developed to uncover these adaptive signatures. Convolutional neural networks (CNNs) are particularly effective for this task, as they handle large data arrays while maintaining element correlations. However, shallow CNNs may miss complex patterns due to their limited capacity, while deep CNNs can capture these patterns but require extensive data and computational power. Transfer learning addresses these challenges by utilizing a deep CNN pretrained on a large dataset as a feature extraction tool for downstream classification and evolutionary parameter prediction. This approach reduces extensive training data generation requirements and computational needs while maintaining high performance. In this study, we developed TrIdent, a tool that uses transfer learning to enhance detection of adaptive genomic regions from image representations of multilocus variation. We evaluated TrIdent across various genetic, demographic, and adaptive settings, in addition to unphased data and other confounding factors. TrIdent demonstrated improved detection of adaptive regions compared to recent methods using similar data representations. We further explored model interpretability through class activation maps and adapted TrIdent to infer selection parameters for identified adaptive candidates. Using whole-genome haplotype data from European and African populations, TrIdent effectively recapitulated known sweep candidates and identified novel cancer, and other disease-associated genes as potential sweeps.

As organisms adapt to environmental changes, natural selection modifies the frequency of non-neutral alleles. For beneficial mutations, the outcome of this process may be a selective sweep, in which an allele rapidly increases in frequency and perhaps reaches fixation within a population. Selective sweeps have well-studied effects on patterns of local genetic variation in panmictic populations, but much less is known about the dynamics of sweeps in continuous space. In particular, because limited movement across a landscape leads to unique patterns of population structure, spatial dynamics may influence the trajectory of selected mutations. Here, we use forward-in-time, individual-based simulations in continuous space to study the impact of space on beneficial mutations as they sweep through a population. In particular, we show that selection changes the joint distribution of allele frequency and geographic range occupied by a focal allele and demonstrate that this signal can be used to identify selective sweeps. We then leverage this signal to identify in-progress selective sweeps within the malaria vector Anopheles gambiae , a species under strong selection pressure from vector control measures. By considering space, we identify multiple previously undescribed variants with potential phenotypic consequences, including mutations impacting known IR-associated genes and altering protein structure and properties. Our results demonstrate a novel signal for detecting selection in spatial population genetic data that may have implications for genomic surveillance and understanding geographic patterns of genetic variation.

Oligodendrocyte maturation arrest in hypoxia-induced white matter injury (WMI) results in long-term neurofunctional disabilities of preterm infants. Although neurons are closely linked to myelination regulation, how neurons respond to the above process remains elusive. Here, we identify a compensatory role of neuronal Slit1-dependent signaling in protecting against hypoxia-induced hypomyelination and ameliorating motor and cognitive disabilities. Conditional ablation of Slit1 in neurons exacerbates hypoxia-induced hypomyelination but is negligible for developmental myelination. Secreted Slit1 from hypoxic neurons directly targets oligodendrocyte, acting through Robo2-srGAP1-RhoA signaling. Pharmacological inhibition of RhoA restores myelination and promotes neurofunctional recovery in adolescent mice. Notably, natural selection analysis and functional validation indicate an adaptive variant with higher Slit1 gene expression in the Tibetan population, which has low oxygen availability. Collectively, these findings show a neuronal Slit1-dependent program of OL differentiation and suggest that targeting the Slit1-Robo2 signaling axis may have therapeutic potential for treatment of preterm infants with hypoxic WMI.

Domestication and artificial selection for desirable traits have driven significant phenotypic changes and left detectable genomic footprints in farmed animals. Since the 1960s, intensive breeding has led to the rapid domestication of Atlantic salmon (Salmo salar), with multiple independent events that make it a valuable model for studying early domestication stages and the parallel evolution of populations of different origins subjected to similar selection pressures. Some aquatic species, including Atlantic salmon, have undergone whole-genome duplication (WGD), raising the possibility that genetic redundancy resulting from WGD has contributed to adaptation in captive environments, as seen in plants. Here, we examined the genomic responses to domestication in Atlantic salmon, focusing on potential signatures of parallel selection, including those associated with WGD. Candidate genomic regions under selection were identified by comparing whole-genome sequences from aquaculture and wild populations across 2 independently domesticated lineages (Western Norway and North America) using a genome-wide scan that combined 3 statistical methods: allele frequencies (FST), site frequency (Tajima's D), and haplotype differentiation (XP-EHH). These analyses revealed shared selective sweeps on identical SNPs in major histocompatibility complex (MHC) genes across aquaculture populations. This suggests that a combination of long-term balancing selection and recent human-induced selection has shaped MHC gene evolution in domesticated salmon. Additionally, we observed selective sweeps on a small number of gene pairs in homeologous regions originating from WGD, offering insights into how historical genome duplication events may intersect with recent selection pressures in aquaculture species.

Genetic variability in livestock driven by selection leaves distinct signatures within the genome. However, the comprehensive landscape of the selection responses for milk production traits in the Chinese buffalo population remains elusive. This study employed an integrated haplotype score (iHS) and runs of homozygosity (ROH) analyses of whole-genome sequence data from 100 Chinese buffaloes to decipher selection signatures. Using iHS and ROH, we identified 1 046 and 1 045 significant genomic regions, containing 717 and 263 candidate genes, respectively. The integration of iHS and ROH revealed 258 candidate regions and 108 overlapping genes, representing true selection signatures. Additionally, 94 candidate regions overlapped with 672 previously reported quantitative trait loci associated with key economically important traits. Annotation of the genomic regions highlighted candidate genes linked to milk production traits, including SNORD42, COX18, ANKRD17, ALB, RASSF6, CXCL8, TMEM232, ARHGAP26, and NR3C1. Transcriptome-wide association analysis supported ANKRD17 and CEP41 as potential candidates for affecting milk traits. This study unveils a comprehensive selection signature profile for the Chinese buffalo population by integrating iHS and ROH methods. The findings have broad implications for improving milk production traits in buffalo populations globally, contributing to more sustainable livestock systems. The identified candidate genes shed light on the selection response for milk production traits, offering crucial insights into optimising the breeding strategies for Chinese buffaloes.

Positive selection at the 2q21.3 enhancer region for lactase gene (LCT) expression in Europeans and Africans has long been attributed to selection for lactase persistence (LP), the capacity of adults to digest lactose in milk, presumably because of the benefits associated with milk consumption. While considered a classic example of gene-culture coevolution, recently doubts have been raised about the link between selection at 2q21.3 and LP. Analysis of additional populations could shed further light; here, we demonstrate that a haplotype spanning ~467 kb at the 2q21.3 locus has risen to high frequency in East Asians (~25%) but is absent from Africans and Europeans. This haplotype likely derived from Neanderthals and has been under positive selection in East Asians. The East Asian-specific haplotype is associated with alterations in LCT expression and promoter methylation in certain cell types, similar to what is observed with LP-associated haplotypes in Europeans. Moreover, its frequency is comparable to that of LP in East Asians, suggesting a potential association with LP in East Asians. However, it is highly unlikely that selection in East Asians was related to milk-drinking habits. We find that this haplotype impacts the expression of UBXN4, DARS1, and DARS1-AS1 in immune cells and is associated with neutrophil and white blood cell counts. Hence, the selection might be linked to certain aspects of immune function. This implies that selection on 2q21.3 has thus either occurred for different reasons in different populations or the selection observed in other populations is also not due to LP.

The architecture underpinning genomic divergence is still a largely uncharted territory and likely case-dependent. Here, we investigated genome-wide variation in Ballan wrasse, a northeastern Atlantic fish species that displays two sympatric colour morphs, spotty and plain, that have been suggested to represent subspecies. We produced a chromosome-level reference genome and thereafter investigated genomic divergence among 152 individuals including both morphs, from two localities in Spain and Norway each and one in France. Differences between morphs dominated in Spain in accordance with sympatric divergence, whereas in Norway allopatric differentiation was prominent and repeated genomic signals of local divergence were found. Chromosomes had large low-recombining areas shared across all populations. Within the Spanish morphs, these areas contained large islands of divergence, totalling ~11% of the genome, and showed high morph specificity and strong selection. The same regions showed frequent admixture in the French morphs and no differentiation in Norway. In contrast, divergent regions observed between sampling localities in Norway were shorter and found throughout the genome. High inbreeding and lower diversity were observed in the Norwegian samples, consistent with the proposed recolonisation bottleneck and subsequent drift. Several genomic regions were significantly associated with morphs and contained tens of genes of diverse functions, suggesting that colouration is unlikely to be the sole driver of divergence. Our results do not support the hypothesis of shared larger genomic features underlying intraspecific colour divergence. Instead, we observe gradual accumulation of differences into low-recombining regions, likely when additional factors like assortative mating and/or lack of gene flow favour their development.

Natural selection leaves detectable patterns of altered spatial diversity within genomes, and identifying affected regions is crucial for understanding species evolution. Recently, machine learning approaches applied to raw population genomic data have been developed to uncover these adaptive signatures. Convolutional neural networks (CNNs) are particularly effective for this task, as they handle large data arrays while maintaining element correlations. However, shallow CNNs may miss complex patterns due to their limited capacity, while deep CNNs can capture these patterns but require extensive data and computational power. Transfer learning addresses these challenges by utilizing a deep CNN pre-trained on a large dataset as a feature extraction tool for downstream classification and evolutionary parameter prediction. This approach reduces extensive training data generation requirements and computational needs while maintaining high performance. In this study, we developed TrIdent, a tool that uses transfer learning to enhance detection of adaptive genomic regions from image representations of multilocus variation. We evaluated TrIdent across various genetic, demographic, and adaptive settings, in addition to unphased data and other confounding factors. TrIdent demonstrated improved detection of adaptive regions compared to recent methods using similar data representations. We further explored model interpretability through class activation maps and adapted TrIdent to infer selection parameters for identified adaptive candidates. Using whole-genome haplotype data from European and African populations, TrIdent effectively recapitulated known sweep candidates and identified novel cancer, and other disease-associated genes as potential sweeps.

Milk production traits in sheep are influenced by complex genetic factors, and understanding these traits requires the identification of candidate genes under selection. This study employed two methods, FST and XP-EHH, to identify selection signatures and candidate genes associated with milk production traits in sheep. For this purpose, 9 different breeds from the Sheep HapMap dataset generated by the International Sheep Genomics Consortium (ISGC) based on analysis of the Ovine SNP50 BeadChip were used. The dairy breeds included Brown East Friesian (n = 39), Milk Lacaune (n = 103), Chios (n = 23), Churra (n = 120), and Comisana (n = 24), while the non-dairy breeds included Afshari (n = 37), Moghani (n = 34), Galway (n = 49), and Australian Suffolk (n = 109). Genomic regions in the top 0.1 percentile of FST values revealed 71 genes, while regions with the highest positive XP-EHH values identified 69 genes. Five overlapping genes-DHRS3, TNFRSF1B, AADACL4, ARHGEF11, and LRRC71-were detected by both methods, highlighting their relevance to milk production. Several candidate genes in regions identified from FST, such as PER2, SH3PXD2A, TMEM117, DDX6, PDCD11, CALHM2, and CALHM3, have been previously associated with milk production traits. Notably, CRABP2, PEAR1, PGM1, ALG6, COX15, and OAT were identified in regions with high XP-EHH values in the dairy group. Gene ontology analysis indicated that the identified genes are enriched in pathways related to chemokine receptor activity, gap junction channel activity, and gap junction-mediated intercellular transport, as well as cellular components like the connexin complex. Further studies on these genes may improve understanding of the genetic architecture of milk production traits in sheep.

In the study, data obtained from OvineSNP50K SNP chips using the Illumina® iScan platform for Eşme sheep were used. The integrated haplotype score (iHS) and runs of homozygosity (ROH) statistical approaches were used to identify selection signatures. Using the iHS analysis, it was discovered that there are 10 genomic regions and 51 genes on ovine chromosomes 1, 9, 11, and 12 that are under selection. Three genomic regions and 97 genes on ovine chromosomes 6 and 11 were found to be under selection using the ROH analysis. Candidate genes associated with economic and ecological traits were detected using both approaches. Among the genetic diversity parameters considered in this study, the minor allele frequency (MAF), the genetic distance between individuals (D), as well as observed (Ho) and expected heterozygosities (He) values were 0.300, 0.309, 0.388, and 0.390, respectively. The obtained Ho, He and D values indicate a moderate level of genetic diversity. The ratio of polymorphic SNPs (PN) was 0.947, and the average values of FROH and FHOM were 0.030 and 0.029, respectively. Considering the PN value obtained in the study, it is evident that the SNPs in the population exhibit a high level of polymorphism at 94.7%. While the FROH value obtained indicates high genetic diversity among the individuals in the present study, the FHOM value suggests that the population is predominantly composed of heterozygous individuals. As a result, evidence indicating genetic advancements have been made for target traits in breeding programs within the population. Additionally, candidate genes suitable for future molecular marker-supported breeding programs have been identified. In addition, a better understanding of the genetic structure and production potential of the population has been achieved. Findings have shown that Eşme sheep are a breed with high meat production potential and strong adaptation abilities.

Natural and artificial selection in domesticated animals can cause specific changes in genomic regions known as selection signatures. Our study used the integrated haplotype score (iHS) and Tajima's D tests within non-overlapping windows of 100 kb to identify selection signatures, in addition to genetic diversity and linkage disequilibrium estimates in 9498 sheep from breeds in Ireland (Belclare, Charollais, Suffolk, Texel, and Vendeen). The mean observed and expected heterozygosity for all the sheep breeds were 0.353 and 0.355, respectively. Suffolk had the least genetic variation and, along with Texel, had slower linkage disequilibrium decay. iHS and Tajima's D detected selection signatures for all breeds, with some regions overlapping, thus forming longer segments of selection signatures. Common selection signatures were identified across iHS and Tajima's D methods for all breeds, with Belclare and Texel having several common regions under positive selection. Several genes were detected within the selection signature regions, including ITGA4, TLR3, and TGFB2 related to the immune system against endoparasites; DLG1, ROBO2, MXI1, MTMR2, CEP57, and FAM78B related to reproductive traits; WDR70 related to milk traits; SCHM1 and MYH15 related to meat traits; and TAS2R4, TAS2R39, and TAS2R40 related to adaptive traits. In conclusion, our results demonstrated moderate genetic diversity in the sheep breeds and detected and characterized selection signatures harboring genes associated with reproductive traits, milk production, meat production, and adaptive traits such as endoparasite resistance.

Failing to correct for multiple testing in selection scans can lead to false discoveries of recent genetic adaptations. The scanning statistics in selection studies are often too complicated to theoretically derive a genome-wide significance level or empirically validate control of the family-wise error rate (FWER). By modeling the autocorrelation of identity-by-descent (IBD) rates, we propose a computationally efficient method to determine genome-wide significance levels in an IBD-based scan for recent positive selection. In whole genome simulations, we show that our method has approximate control of the FWER and can adapt to the spacing of tests along the genome. We also show that these scans can have more than fifty percent power to reject the null model in hard sweeps with a selection coefficient s > = 0.01 and a sweeping allele frequency between twenty-five and seventy-five percent. A few human genes and gene complexes have statistically significant excesses of IBD segments in thousands of samples of African, European, and South Asian ancestry groups from the Trans-Omics for Precision Medicine project and the United Kingdom Biobank. Among the significant loci, many signals of recent selection are shared across ancestry groups. One shared selection signal at a skeletal cell development gene is extremely strong in African ancestry samples.

Understanding interspecific introgressive hybridisation and the biological significance of introgressed variation remains an important goal in population genomics. European (Anguilla anguilla) and American eel (A. rostrata) represent a remarkable case of hybridisation. Both are panmictic and spawn in partial sympatry in the Sargasso Sea, occasionally producing viable, fertile hybrids, primarily found in Iceland. We studied introgressive hybridisation from American into European eel using whole-genome sequences of 78 individuals, including European, American and 21 putative hybrid eels. Previous studies using few genetic markers could not resolve whether hybridisation involved simple unidirectional backcrossing or a more complex hybrid swarm scenario. However, local ancestry inference along individual chromosomes revealed that Icelandic hybrids were primarily F1 or first-generation backcrosses towards European eel, with some showing more complex backcrossing. All European eels outside Iceland contained short chromosomal blocks from American eel, indicating a porous genome. We found no evidence for previously hypothesised geographical gradients of introgression in European eel outside Iceland. Several chromosomal regions showed high interspecific divergence, but haplotype blocks introgressed from American eel were identified both within and outside these regions. There was little correspondence between regions of high relative (FST) and absolute divergence (dXY), with the former reflecting selective sweeps within species or reduced recombination rather than barrier loci. A single genomic region showed evidence of repeated introgression from American into European eel under positive selection in both species. The study illustrates that species can maintain genetic integrity despite porous genomes and that standing variation in one species can potentially be available for future adaptive responses in the other species.

How mammalian herbivores evolve to feed on chemically defended plants remains poorly understood. In this study, we investigated the adaptation of two species of woodrats (Neotoma lepida and N. bryanti) to creosote bush (Larrea tridentata), a toxic shrub that expanded across the southwestern United States after the Last Glacial Maximum. We found that creosote-adapted woodrats have elevated gene dosage across multiple biotransformation enzyme families. These duplication events occurred independently across species and substantially increase expression of biotransformation genes, especially within the glucuronidation pathway. We propose that increased gene dosage resulting from duplication is an important mechanism by which animals initially adapt to novel environmental pressures.

With the rapid advancement of high-throughput sequencing technologies, whole genome sequencing (WGS) has emerged as a crucial tool for studying genetic variation and population structure. Utilizing population genomics tools to analyze resequencing data allows for the effective integration of selection signals with population history, precise estimation of effective population size, historical population trends, and structural insights, along with the identification of specific genetic loci and variations. This paper reviews current whole genome sequencing technologies, detailing primary research methods, relevant software, and their advantages and limitations within population genomics. The goal is to examine the application and progress of resequencing technologies in this field and to consider future developments, including deep learning models and machine learning algorithms, which promise to enhance analytical methodologies and drive further advancements in population genomics.

Autism spectrum disorder (ASD) affects up to 1 in 59 children, and is one of the most common neurodevelopmental disorders. Recent genomic studies have highlighted the role of rare variants in ASD. This study aimed to identify genes affected by rare variants shared by siblings with ASD and validate the function of a candidate gene FRRS1L. By integrating the whole genome sequencing data of 866 multiplex families from the Hartwell Foundation's Autism Research and Technology Initiative and Autism Speaks MSSNG project, we identified rare variants shared by two or more siblings with ASD. Using shared rare variants (SRVs), we selected candidate genes for ASD. Gene prioritization by evolutionary features and expression alterations on autism identified FRRS1L in two families, including one with impaired social behaviors. One variant in this family was 6 bp away from human-specific trinucleotide fixation. Additionally, CRISPR/Cas9 experiments demonstrated downregulation by a family variant and upregulation by a fixed site. Population genetics further demonstrated that upregulation of this gene has been favored during human evolution. Various mouse behavioral tests showed that Frrs1l knockout specifically impairs social novelty recognition without altering other behavioral phenotypes. Furthermore, we constructed humanized mice by introducing human sequences into a mouse genome. These knockin mice showed improved abilities to retain social memory over time. The results of our population genetic and evolutionary analyses, behavior experiments, and genome editing propose a molecular mechanism that may confer a selective advantage through social memory enhancement and may cause autism-related social impairment when disrupted in humans. These findings highlight the role of FRRS1L, the AMPA receptor subunit, in social behavior and evolution.

Selection pressures found in the prevailing production environments have shaped the genetic structure of indigenous chickens we see today. Indigenous chickens, raised in villages, provide essential genetic resources and income for poverty alleviation by providing affordable protein. However, they are threatened by predators, emerging diseases, and market demand for ideal breeds and fast production which causes loss of their valuable traits. The lack of knowledge about genetic diversity and genetic mechanisms underlying adaptive variants may compromise the goal of conserving indigenous chicken breeds. The main insights of the study are that indigenous chickens are highly diversified, and environmental factors play a key role in enabling chicken adaptation and distribution. Genomic and spatial technologies have made it possible to explore the genetic structure and fully comprehend the mechanism underlying the local adaptation of indigenous chickens. These technologies can aid in creating programs that enhance productivity and promote climate-resilient breeds. This review explores the impact of natural selection on indigenous chicken, genetic diversity, population size, and the advancement of technologies in understanding local adaptation drivers. In conclusion, this review highlights the importance of studying the habitats and how this will guide in conserving local breeds in their intended production environment.

Italian local turkey populations are an important source of genetic diversity that should be preserved through an in vivo approach. Whole genome sequencing (WGS) and genotyping datasets were used to assess genetic variability within and across populations, to perform a genome-wide comparative analysis among populations and to identify selection signatures in Italian turkey populations. We used new data from 73 WGS samples (12X) representing five turkey populations, together with previous data from 107 birds genotyped with the Affymetrix 600K single nucleotide polymorphism (SNP) turkey array from 11 populations. The PCA and Admixture show a relatively strong isolation effect between the populations. Moreover, the values of genomic inbreeding based on ROH (FROH) showed marked differences among populations and ranged from 0.096 to 0.643. Selective sweeps were identified using the integrated haplotype score (iHS) within the local group, the commercial line, and the Narragansett breed, resulting in the identification of 20, 19, and 27 regions with a total of 73, 48, and 90 candidate genes, respectively. Some of these genes such as FAM107B, MSTN, PDZRN4, HSF2 and GJA1 are associated with heat stress, growth, and carcass traits. We conclude that our results improve our understanding of the genomic architecture of the Italian turkey populations. The findings of iHS suggest that selection can play a significant role in shaping selection signatures in local turkey populations and could provide a basis for identifying gene mutations that may be beneficial in adaptation to climate change. Our results will be useful in developing and implementing conservation and selection plans for Italian turkey populations.

Invasive species offer uniquely replicated model systems to study rapid adaptation. The common myna (Acridotheres tristis) has been introduced to over a dozen countries and is classified as one of the most invasive birds in the world. Their multiple invasions provide an opportunity to identify repeated adaptation, as invasive populations originated from multiple source populations. We compared whole-genome resequencing data from 80 individuals from four native and seven invasive populations, representing two independent introduction pathways. Results from two different selection scan methods were combined and identified a strongly selected region on chromosome 8 that spans two copies of AMY2A, part of the alpha-amylase gene family, a putative ncRNA and an insertion-deletion structural variant (SV) that contains an ERVK transposable element (TE). Outlier SNPs and the SV are polymorphic in native populations, but fixed or close-to-fixed in the two invasive pathways, with the fixation of the same alleles in two independent lineages providing evidence for parallel selection on standing variation. Intriguingly, the second copy of AMY2A has a non-conservative missense mutation at a phylogenetically conserved site. This mutation, alongside variation in the SV, TE and ncRNA, provide possible routes for changes to protein function or expression. AMY2A has been associated with human commensalism in house sparrows, and genes in this family have been linked to adaptation to high-starch diets in humans and dogs. This study illustrates the value of replicated analyses within and across species to understand rapid adaptation at the molecular level.

The genetic improvement of beef cattle breeds is crucial for the advancement of the beef cattle industry. Whole-genome resequencing technology has been widely applied in genetic breeding as well as research on selection signatures in beef cattle. In this study, 20× whole-genome resequencing was performed on 282 Angus cattle from the Ningxia region, and a high-quality dataset encompassing extensive genomic variations across the entire genome was constructed. The iHS test identified 495 selection signal regions, which included pregnancy-associated glycoprotein (PAG) family genes and immune-related genes such as UL16-binding protein 21 (ULBP21), CD1b molecule (CD1B), and tumor necrosis factor ligand superfamily member 11 (TNFSF11). A quantitative trait locus (QTL) enrichment analysis revealed that several economic traits, including longissimus muscle area, marbling score, carcass weight, average daily gain, and milk yield, were significantly enriched in cattle with these selection signatures. Although the enrichment of QTLs for health traits was low, immune-related genes may indirectly contribute to improvements in production performance. These findings show the genetic basis of economic and adaptive traits in Ningxia Angus cattle, providing a theoretical foundation and guidance for further genetic improvement and breeding strategies.

Functional divergences of coding genes can be caused by divergences in their coding sequences and expression. However, whether and how expression divergences and coding sequence divergences coevolve is not clear. Gene expression divergences in differentiated cells and tissues recapitulate developmental models within a species, while gene expression divergences between analogous cells and tissues resemble traditional phylogenies in different species, suggesting that gene expression divergences are molecular traits that can be used for evolutionary studies. Using transcriptomes and evolutionary proxies to study gene expression divergences among differentiated cells and tissues in Arabidopsis, expression divergences of coding genes are shown to be strongly anti-correlated with phylostrata (gene ages), indicators of selective constraint Ka/Ks (nonsynonymous replacement rate/synonymous substitution rate) and indicators of positive selection (frequency of loci with Ka/Ks > 1), but only weakly or not correlated with indicators of neutral selection (Ks). Our results thus suggest that expression divergences largely coevolve with coding sequence divergences, suggesting that expression divergences of coding genes are selectively fixed by natural selection but not neutral selection, which provides a molecular framework for trait diversification, functional adaptation and speciation. Our findings therefore support that positive selection rather than negative or neutral selection is a major driver for the origin and evolution of Arabidopsis genes, supporting the Darwinian theory at molecular levels.

Identifying genomic regions under selection is the most challenging issue for improving important traits in animals. Few studies have focused on identifying genomic regions under selection in sheep. The aim of this study was to identify selective sweeps and to explore the relationship between these and quantitative trait loci (QTL) in both domestic and wild sheep species using single nucleotide polymorphism markers (SNPs).

Genomic data were obtained from the NextGen project, which included genotyping 20 domestic and 14 wild sheep using the Illumina Ovine SNP50K BeadChip. The XP-EHH, iHS, and RSB methods were employed to detect signatures of positive selection.

The results of the iHS method indicated 405 and 275 selective sweeps in domestic and wild sheep, respectively. Additionally, RSB and XP-EHH analyses revealed approximately 398 and 479 selective sweeps in domestic and wild sheep, respectively. Some of the genes associated with important QTL traits in domestic sheep include ADGRB3, CADM1, CAPN2, GALNT10, MTR, RELN, and USP25, while in wild sheep, the relevant genes include ACAN, ACO1, GADL1, MGST3, and PRDM16. Selective sweeps identified in domestic sheep were associated with body weight, muscle weight, milk protein percentage, and milk yield. In contrast, selective sweeps found in wild sheep were linked to average daily gain, bone weight, carcass fat percentage, and dressing percentage.

These results indicate that selection by humans and the environment have largely progressed in harmony, highlighting the importance of both economic and environmental traits for survival. Additionally, the identification of potential candidate genes associated with economic traits and genomic regions that have experienced selection can be utilized in sheep breeding programs. However, due to the incomplete information regarding the functional annotation of genes in sheep and the limited sample size, further research with a larger sample group is essential to gain a deeper understanding of the candidate genes linked to economic traits in both domestic and wild sheep. Advancing knowledge in this area can significantly enhance the effectiveness of breeding strategies. The quantitative trait loci identified in this study have the potential to be incorporated into breeding plans for both domestic and wild sheep.

Despite the well-known effects of sexual selection on phenotypes, links between this evolutionary process and reproductive isolation, genomic divergence, and speciation have been difficult to establish. We unravel the genetic basis of sexually selected plumage traits to investigate their effects on reproductive isolation in barn swallows. The genetic architecture of sexual traits is characterized by 12 loci on two autosomes and the Z chromosome. Sexual trait loci exhibit signatures of divergent selection in geographic isolation and barriers to gene flow in secondary contact. Linkage disequilibrium between these genes has been maintained by selection in hybrid zones beyond what would be expected under admixture alone. Our findings reveal that selection on coupled sexual trait loci promotes reproductive isolation, providing key empirical evidence for the role of sexual selection in speciation.

Genomic regions sometimes show patterns of genetic variation distinct from the genome-wide population structure. Such deviations have often been interpreted to represent effects of selection. However, systematic investigation of whether and how non-selective factors, such as recombination rates, can affect distinct patterns has been limited. Here, we associate distinct patterns of genetic variation with reduced recombination rates in a songbird, the Eurasian blackcap (Sylvia atricapilla), using a new reference genome assembly, whole-genome resequencing data and recombination maps. We find that distinct patterns of genetic variation reflect haplotype structure at genomic regions with different prevalence of reduced recombination rate across populations. At low-recombining regions shared in most populations, distinct patterns reflect conspicuous haplotypes segregating in multiple populations. At low-recombining regions found only in a few populations, distinct patterns represent variance among cryptic haplotypes within the low-recombining populations. With simulations, we confirm that these distinct patterns evolve neutrally by reduced recombination rate, on which the effects of selection can be overlaid. Our results highlight that distinct patterns of genetic variation can emerge through evolutionary reduction of local recombination rate. The recombination landscape as an evolvable trait therefore plays an important role determining the heterogeneous distribution of genetic variation along the genome.

Exploration of novel alleles from ex situ collection is still limited in modern plant breeding as these alleles exist in genetic backgrounds of landraces that are not adapted to modern production environments. The practice of backcross breeding results in preservation of the adapted background of elite parents but leaves little room for novel alleles from landraces to be incorporated. Selection of adaptation-associated linkage blocks instead of the entire adapted background may allow breeders to incorporate more of the landrace's genetic background and to observe and evaluate novel alleles. Important adaptation-associated linkage blocks would have been selected over multiple cycles of breeding and hence are likely to exhibit signatures of positive selection or selective sweeps. We conducted genome-wide scan for candidate selective sweeps (CSS) using Fst, Rsb, and xpEHH in state, regional, spring, winter, and market-class population pairs and reported 446 CSS in 19 population pairs over time and 1033 CSS in 44 population pairs across geography and class. Further validation of these CSS in specific breeding programs may lead to identification of sets of loci that can be selected to restore population-specific adaptation in pre-breeding germplasms.

The advent of affordable genome sequencing and the development of new computational tools have established a new era of genomic knowledge. Sequenced human genomes number in the tens of thousands, including thousands of ancient human genomes. The abundance of data has been met with new analysis tools that can be used to understand populations' demographic and evolutionary histories. Thus, a variety of computational methods now exist that can be leveraged to answer anthropological questions. This includes novel likelihood and Bayesian methods, machine learning techniques, and a vast array of population simulators. These computational tools provide powerful insights gained from genomic datasets, although they are generally inaccessible to those with less computational experience. Here, we outline the theoretical workings behind computational genomics methods, limitations and other considerations when applying these computational methods, and examples of how computational methods have already been applied to anthropological questions. We hope this review will empower other anthropologists to utilize these powerful tools in their own research.

Recent progress in evolutionary genomics on human (Homo sapiens) populations has revealed complex demographic events and genomic changes. These include population expansion with complicated migration, substantial population structure, and ancient introgression from other hominins, as well as human characteristics selections. Nevertheless, the genomic regions in which such evolutionary events took place have remained unclear.

Here, we focused on eight loci containing the haplotypes that were previously presented as atypical for the mutation pattern in sequence and/or geographic distribution pattern with the model of recent African origin, which constitute two major clusters: African only, and global. This was the consensus model before information regarding introgression from Neanderthal (Homo neanderthalensis) was available. We compared diversity in identical datasets of the modern human population genome, with the 1000 Genomes project among them.

This study identified representative genomic regions that show traces of various demographic events and genomic changes that modern humans have undergone by categorizing the relationships in sequence similarity and in worldwide geographic distribution among haplotypes.

The human gut microbiome is composed of a highly diverse consortia of species which are continually evolving within and across hosts. The ability to identify adaptations common to many human gut microbiomes would not only reveal shared selection pressures across hosts, but also key drivers of functional differentiation of the microbiome that may affect community structure and host traits. However, to date there has not been a systematic scan for adaptations that have spread across human gut microbiomes. Here, we develop a novel selection scan statistic named the integrated Linkage Disequilibrium Score (iLDS) that can detect the spread of adaptive haplotypes across host microbiomes via migration and horizontal gene transfer. Specifically, iLDS leverages signals of hitchhiking of deleterious variants with the beneficial variant. Application of the statistic to ~30 of the most prevalent commensal gut species from 24 populations around the world revealed more than 300 selective sweeps across species. We find an enrichment for selective sweeps at loci involved in carbohydrate metabolism-potentially indicative of adaptation to features of host diet-and we find that the targets of selection significantly differ between Westernized and non-Westernized populations. Underscoring the potential role of diet in driving selection, we find a selective sweep absent from non-Westernized populations but ubiquitous in Westernized populations at a locus known to be involved in the metabolism of maltodextrin, a synthetic starch that has recently become a widespread component of Western diets. In summary, we demonstrate that selective sweeps across host microbiomes are a common feature of the evolution of the human gut microbiome, and that targets of selection may be strongly impacted by host diet.

Understanding the genetic architecture of human traits is of key biological, medical and evolutionary importance[1]. Despite much progress, little is known about how genetic architecture varies across the trait continuum and, in particular, if it differs in the tails of complex traits, where disease often occurs. Here, applying a novel approach based on polygenic scores, we reveal striking departures from polygenic architecture across 148 quantitative trait tails, consistent with distinct concentrations of high-impact rare alleles in one or both tails of most of the traits. We demonstrate replication of these results across ancestries, cohorts, repeat measures, and using an orthogonal family-based approach[2]. Furthermore, trait tails with inferred enrichment of rare alleles are associated with more exome study hits, reduced fecundity, advanced paternal age, and lower predictive accuracy of polygenic scores. Finally, we find evidence of ongoing selection consistent with the observed departures in polygenicity and demonstrate, via simulation, that traits under stabilising selection are expected to have tails enriched for rare, large-effect alleles. Overall, our findings suggest that while common variants of small effect likely account for most of the heritability in complex traits[3], rare variants of large effect are often more important in the trait tails, particularly among individuals at highest risk of disease. Our study has implications for rare variant discovery, the utility of polygenic scores, the study of selection in humans, and for the relative importance of common and rare variants to complex traits and diseases.

High altitude adapted livestock species (cattle, yak, goat, sheep, and horse) has critical role in the human socioeconomic sphere and acts as good source of animal source products including milk, meat, and leather, among other things. These species sustain production and reproduction even in harsh environments on account of adaptation resulting from continued evolution of beneficial traits. Selection pressure leads to various adaptive strategies in livestock whose footprints are evident at the different genomic sites as the "Selection Signature". Scrutiny of these signatures provides us crucial insight into the evolutionary process and domestication of livestock adapted to diverse climatic conditions. These signatures have the potential to change the sphere of animal breeding and further usher the selection programmes in right direction. Technological revolution and recent strides made in genomic studies has opened the routes for the identification of selection signatures. Numerous statistical approaches and bioinformatics tools have been developed to detect the selection signature. Consequently, studies across years have identified candidate genes under selection region found associated with numerous traits which have a say in adaptation to high-altitude environment. This makes it pertinent to have a better understanding about the selection signature, the ways to identify and how to utilize them for betterment of livestock populations as well as farmers. This review takes a closer look into the general concept, various methodologies, and bioinformatics tools commonly employed in selection signature studies and summarize the results of recent selection signature studies related to high-altitude adaptation in various livestock species. This review will serve as an informative and useful insight for researchers and students in the field of animal breeding and evolutionary biology.

Large ancient DNA (aDNA) studies offer the chance to examine genomic changes over time, providing direct insights into human evolution. While recent studies have used time-stratified aDNA for selection scans, most focus on single-locus methods. We conducted a multi-locus genotype scan on 708 samples spanning 7000 years of European history. We show that the G12 statistic, originally designed for unphased diploid data, can effectively detect selection in aDNA processed to create 'pseudo-haplotypes'. In simulations and at known positive control loci (e.g., lactase persistence), G12 outperforms the allele frequency-based selection statistic, SweepFinder2, previously used on aDNA. Applying our approach, we identified 14 candidate regions of selection across four time periods, with half the signals detectable only in the earliest period. Our findings suggest that selective events in European prehistory, including from the onset of animal domestication, have been obscured by neutral processes like genetic drift and demographic shifts such as admixture.

Recent positive selection can result in an excess of long identity-by-descent (IBD) haplotype segments overlapping a locus. The statistical methods that we propose here address three major objectives in studying selective sweeps: scanning for regions of interest, identifying possible sweeping alleles, and estimating a selection coefficient s. First, we implement a selection scan to locate regions with excess IBD rates. Second, we estimate the allele frequency and location of an unknown sweeping allele by aggregating over variants that are more abundant in an inferred outgroup with excess IBD rate versus the rest of the sample. Third, we propose an estimator for the selection coefficient and quantify uncertainty using the parametric bootstrap. Comparing against state-of-the-art methods in extensive simulations, we show that our methods are more precise at estimating s when s≥0.015. We also show that our 95% confidence intervals contain s in nearly 95% of our simulations. We apply these methods to study positive selection in European ancestry samples from the Trans-Omics for Precision Medicine project. We analyze eight loci where IBD rates are more than four standard deviations above the genome-wide median, including LCT where the maximum IBD rate is 35 standard deviations above the genome-wide median. Overall, we present robust and accurate approaches to study recent adaptive evolution without knowing the identity of the causal allele or using time series data.

Mechanisms of abdominal obesity GWAS variants have remained largely unknown. To elucidate these mechanisms, we leveraged subcutaneous adipose tissue (SAT) single nucleus RNA-sequencing and genomics data. After discovering that heritability of abdominal obesity is enriched in adipocytes, we focused on a SAT unique adipocyte marker gene, the transcription factor TBX15, and its abdominal obesity-associated deleterious missense variant, rs10494217. The allele frequency of rs10494217 revealed a north-to-south decreasing gradient, with consistent significant FST values observed for 25 different populations when compared to Finns, a population with a history of genetic isolation. Given the role of Tbx15 in mouse thermogenesis, the frequency may have increased as an adaptation to cold in Finns. Our selection analysis provided significant evidence of selection for the abdominal obesity risk allele T of rs10494217 in Finns, with a north-to-south decreasing trend in other populations, and demonstrated that latitude significantly predicts the allele frequency. We also discovered that the risk allele status significantly affects SAT adipocyte expression of multiple adipocyte marker genes in trans in two cohorts. Two of these trans genes have been connected to thermogenesis, supporting the thermogenic effect of the TBX15 missense variant as a possible cause of its selection. Adipose expression of one trans gene, a lncRNA, AC002066.1, was strongly associated with adipocyte size, implicating it in metabolically unhealthy adipocyte hypertrophy. In summary, the abdominal obesity variant rs10494217 was selected in Finns, and individuals with the risk allele have trans effects on adipocyte expression of genes relating to thermogenesis and adipocyte hypertrophy.

Recombination breaks down genetic linkage by reshuffling existing variants onto new genetic backgrounds. These dynamics are traditionally quantified by examining the correlations between alleles, and how they decay as a function of the recombination rate. However, the magnitudes of these correlations are strongly influenced by other evolutionary forces like natural selection and genetic drift, making it difficult to tease out the effects of recombination. Here, we introduce a theoretical framework for analyzing an alternative family of statistics that measure the homoplasy produced by recombination. We derive analytical expressions that predict how these statistics depend on the rates of recombination and recurrent mutation, the strength of negative selection and genetic drift, and the present-day frequencies of the mutant alleles. We find that the degree of homoplasy can strongly depend on this frequency scale, which reflects the underlying timescales over which these mutations occurred. We show how these scaling properties can be used to isolate the effects of recombination and discuss their implications for the rates of horizontal gene transfer in bacteria.

The mobilization of transposable elements is a potent source of mutations. In plants, several stransposable elements respond to external cues, fuelling the hypothesis that natural transposition can create environmentally sensitive alleles for adaptation. Here we report on the detailed characterization of a retrotransposon insertion within the first intron of the Arabidopsis floral-repressor gene FLOWERING LOCUS C (FLC) and the discovery of its role for adaptation. The insertion mutation augments the environmental sensitivity of FLC by affecting the balance between coding and non-coding transcripts in response to stress, thus expediting flowering. This balance is modulated by DNA methylation and orchestrated by IBM2, a factor involved in the processing of intronic heterochromatic sequences. The stress-sensitive allele of FLC has spread across populations subjected to recurrent chemical weeding, and we show that retrotransposon-driven acceleration of the life cycle represents a rapid response to herbicide application. Our work provides a compelling example of a transposable element-driven environmentally sensitive allele that confers an adaptive response in nature.

The Italian pig farming industry is unique in its focus on raising heavy pigs primarily for the production of high-quality dry-cured hams. These products require pigs to be slaughtered at a live weight of around 170 kg at 9 months of age. The primary breeds used in this system are Italian Duroc, Italian Landrace, and Italian Large White which are crossed to produce lines that meet standard requirements. Over the past four decades, selection and breeding programmes for these breeds have been subjected to distinct selective pressures to highlight the characteristics of each breed. In this study, we investigated the genome of these breeds by analysing high-density single nucleotide polymorphism data from over 9 000 pigs to scan for signatures of selection using four different methods, two within breeds and two across breeds. This allowed to identify the genomic regions that differentiate these breeds as well as any relevant genes and biological terms. On a global scale, we found that the Italian Duroc breed exhibited a higher genetic differentiation from the Italian Landrace and Italian Large White breeds, with a pairwise FST value of 0.20 compared with the 0.13 between Italian Landrace and Italian Large White. This may reflect either their different origins or the different breeding goals, which are more similar for the Italian Landrace and Italian Large White breeds. Despite these genetic differences at a global level, few signatures of selection regions reached complete fixation, possibly due to challenges in detecting selection linked to quantitative polygenic traits. The differences among the three breeds are confirmed by the low level of overlap in the regions detected. Genetic enrichment analyses of the three breeds revealed pathways and genes related to various productive traits associated with growth and fat deposition. This may indicate a common selection direction aimed at enhancing specific production traits, though different biological mechanisms are likely targeted by the same directional selection in these three breeds. Therefore, these genes may play a critical role in determining the distinctive characteristics of Italian Duroc, Italian Landrace, and Italian Large White, and potentially influence the traits in crossbred pigs derived from them. Overall, the insights gained from this study will contribute to understanding how directional selection has shaped the genome of these heavy pig breeds and to better address selection strategies aimed at enhancing the meat processing industry linked with dry-cured ham production chains.

As population genetic data increase in size, new methods have been developed to store genetic information in efficient ways, such as tree sequences. These data structures are computationally and storage efficient but are not interchangeable with existing data structures used for many population genetic inference methodologies such as the use of convolutional neural networks applied to population genetic alignments. To better utilize these new data structures, we propose and implement a graph convolutional network to directly learn from tree sequence topology and node data, allowing for the use of neural network applications without an intermediate step of converting tree sequences to population genetic alignment format. We then compare our approach to standard convolutional neural network approaches on a set of previously defined benchmarking tasks including recombination rate estimation, positive selection detection, introgression detection, and demographic model parameter inference. We show that tree sequences can be directly learned from using a graph convolutional network approach and can be used to perform well on these common population genetic inference tasks with accuracies roughly matching or even exceeding that of a convolutional neural network-based method. As tree sequences become more widely used in population genetic research, we foresee developments and optimizations of this work to provide a foundation for population genetic inference moving forward.