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Selective sweep is an important phenomenon in the aspect of natural selection. It plays significant role in adaptability as well as survival of species, including crop cultivars. Various existing approaches for selective sweep analysis are mostly built on traditional rule base approach that lack the advanced approaches such as machine learning and deep learning and often result in poor prediction accuracy. In this study a new method or model for the prediction of selective sweep has been presented. This method has been initiated with simulation, preceded through feature extraction and selection and finally fed to different machine learning algorithms. Here eight different machine learning based methods have been implemented—(1) Support Vector Machine (SVM), (2) Regression Tree, (3) Random Forest, (4) Naive Bayes, (5) Multiple logistic regression, (6) K-Nearest Neighbor (KNN), (7) Gradient boosting and (8) Artificial Neural Network (ANN) and results of their comparative evaluations are presented. It has been observed that random forest model outperformed to its counterparts in terms of evaluation matrices with an area under the ROC (Receiver Operating Characteristic) curve (AUC) score of 0.8448 as well as 1st rank in TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution) analysis. Further, a robust model for selective sweep prediction based upon random forest has been developed. Model developed in the current study has outperformed to other existing approaches for prediction and analysis of selective sweep. This new approach for selective sweep analysis is excellent in its accuracy as well as reliability.

Identifying selective sweeps in populations that have complex demographic histories remains a difficult problem in population genetics. We previously introduced a supervised machine learning approach, S/HIC, for finding both hard and soft selective sweeps in genomes on the basis of patterns of genetic variation surrounding a window of the genome. While S/HIC was shown to be both powerful and precise, the utility of S/HIC was limited by the use of phased genomic data as input. In this report we describe a deep learning variant of our method, diploS/HIC, that uses unphased genotypes to accurately classify genomic windows. diploS/HIC is shown to be quite powerful even at moderate to small sample sizes.

The Hulunbuir short-tailed sheep (Ovis aries) is a breed native to China, in which the short-tail phenotype is the result of artificial and natural selection favoring a specific set of genetic mutations. Here, we analyzed the genetic differences between short-tail and normal-tail phenotypes at the genomic level. Selection signals were identified in genome-wide sequences. From 16 sheep, we identified 72,101,346 single nucleotide polymorphisms. Selection signals were detected based on the fixation index and heterozygosity. Seven genomic regions under putative selection were identified, and these regions contained nine genes. Among these genes, T was the strongest candidate as T is related to vertebral development. In T, a nonsynonymous mutation at c.G334T resulted in p.G112W substitution. We inferred that the c.G334T mutation in T leads to functional changes in Brachyury—encoded by this gene—resulting in the short-tail phenotype. Our findings provide a valuable insight into the development of the short-tail phenotype in sheep and other short-tailed animals.

Understanding the genetic diversity of domestic chicken breeds under the impact of socio–cultural and ecological dynamics is vital for the conservation of natural resources. Mae Hong Son chicken is a local breed of North Thai domestic chicken widely distributed in Mae Hong Son Province, Thailand; however, its genetic characterization, origin, and diversity remain poorly understood. Here, we studied the socio–cultural, environmental, and genetic aspects of the Mae Hong Son chicken breed and investigated its diversity and allelic gene pool. We genotyped 28 microsatellite markers and analyzed mitochondrial D-loop sequencing data to evaluate genetic diversity and assessed spatial habitat suitability using maximum entropy modeling. Sequence diversity analysis revealed a total of 188 genotyped alleles, with overall nucleotide diversity of 0.014 ± 0.007, indicating that the Mae Hong Son chicken population is genetically highly diverse, with 35 (M1–M35) haplotypes clustered into haplogroups A, B, E, and F, mostly in the North ecotype. Allelic gene pool patterns showed a unique DNA fingerprint of the Mae Hong Son chicken, as compared to other breeds and red junglefowl. A genetic introgression of some parts of the gene pool of red junglefowl and other indigenous breeds was identified in the Mae Hong Son chicken, supporting the hypothesis of the origin of the Mae Hong Son chicken. During domestication in the past 200–300 years after the crossing of indigenous chickens and red junglefowl, the Mae Hong Son chicken has adapted to the highland environment and played a significant socio–cultural role in the Northern Thai community. The unique genetic fingerprint of the Mae Hong Son chicken, retaining a high level of genetic variability that includes a dynamic demographic and domestication history, as well as a range of ecological factors, might reshape the adaptation of this breed under selective pressure.

Sialic acids are nine carbon sugars ubiquitously found on the surfaces of vertebrate cells and are involved in various immune response-related processes. In humans, at least 58 genes spanning diverse functions, from biosynthesis and activation to recycling and degradation, are involved in sialic acid biology. Because of their role in immunity, sialic acid biology genes have been hypothesized to exhibit elevated rates of evolutionary change. Consistent with this hypothesis, several genes involved in sialic acid biology have experienced higher rates of non-synonymous substitutions in the human lineage than their counterparts in other great apes, perhaps in response to ancient pathogens that infected hominins millions of years ago (paleopathogens). To test whether sialic acid biology genes have also experienced more recent positive selection during the evolution of the modern human lineage, reflecting adaptation to contemporary cosmopolitan or geographically-restricted pathogens, we examined whether their protein-coding regions showed evidence of recent hard and soft selective sweeps. This examination involved the calculation of four measures that quantify changes in allele frequency spectra, extent of population differentiation, and haplotype homozygosity caused by recent hard and soft selective sweeps for 55 sialic acid biology genes using publicly available whole genome sequencing data from 1,668 humans from three ethnic groups. To disentangle evidence for selection from confounding demographic effects, we compared the observed patterns in sialic acid biology genes to simulated sequences of the same length under a model of neutral evolution that takes into account human demographic history. We found that the patterns of genetic variation of most sialic acid biology genes did not significantly deviate from neutral expectations and were not significantly different among genes belonging to different functional categories. Those few sialic acid biology genes that significantly deviated from neutrality either experienced soft sweeps or population-specific hard sweeps. Interestingly, while most hard sweeps occurred on genes involved in sialic acid recognition, most soft sweeps involved genes associated with recycling, degradation and activation, transport, and transfer functions. We propose that the lack of signatures of recent positive selection for the majority of the sialic acid biology genes is consistent with the view that these genes regulate immune responses against ancient rather than contemporary cosmopolitan or geographically restricted pathogens.

Population bottlenecks, inbreeding, and artificial selection can all, in principle, influence levels of deleterious genetic variation. However, the relative importance of each of these effects on genome-wide patterns of deleterious variation remains controversial. Domestic and wild canids offer a powerful system to address the role of these factors in influencing deleterious variation because their history is dominated by known bottlenecks and intense artificial selection. Here, we assess genome-wide patterns of deleterious variation in 90 whole-genome sequences from breed dogs, village dogs, and gray wolves. We find that the ratio of amino acid changing heterozygosity to silent heterozygosity is higher in dogs than in wolves and, on average, dogs have 2–3% higher genetic load than gray wolves. Multiple lines of evidence indicate this pattern is driven by less efficient natural selection due to bottlenecks associated with domestication and breed formation, rather than recent inbreeding. Further, we find regions of the genome implicated in selective sweeps are enriched for amino acid changing variants and Mendelian disease genes. To our knowledge, these results provide the first quantitative estimates of the increased burden of deleterious variants directly associated with domestication and have important implications for selective breeding programs and the conservation of rare and endangered species. Specifically, they highlight the costs associated with selective breeding and question the practice favoring the breeding of individuals that best fit breed standards. Our results also suggest that maintaining a large population size, rather than just avoiding inbreeding, is a critical factor for preventing the accumulation of deleterious variants.

The mode and extent of rapid evolution and genomic change in response to human harvesting are key conservation issues. Although experiments and models have shown a high potential for both genetic and phenotypic change in response to fishing, empirical examples of genetic responses in wild populations are rare. Here, we compare whole-genome sequence data of Atlantic cod (Gadus morhua) that were collected before (early 20th century) and after (early 21st century) periods of intensive exploitation and rapid decline in the age of maturation from two geographically distinct populations in Newfoundland, Canada, and the northeast Arctic, Norway. Our temporal, genome-wide analyses of 346,290 loci show no substantial loss of genetic diversity and high effective population sizes. Moreover, we do not find distinct signals of strong selective sweeps anywhere in the genome, although we cannot rule out the possibility of highly polygenic evolution. Our observations suggest that phenotypic change in these populations is not constrained by irreversible loss of genomic variation and thus imply that former traits could be reestablished with demographic recovery.

Abstract Detecting signals of selection from genomic data is a central problem in population genetics. Coupling the rich information in the ancestral recombination graph (ARG) with a powerful and scalable deep-learning framework, we developed a novel method to detect and quantify positive selection: Selection Inference using the Ancestral recombination graph (SIA). Built on a Long Short-Term Memory (LSTM) architecture, a particular type of a Recurrent Neural Network (RNN), SIA can be trained to explicitly infer a full range of selection coefficients, as well as the allele frequency trajectory and time of selection onset. We benchmarked SIA extensively on simulations under a European human demographic model, and found that it performs as well or better as some of the best available methods, including state-of-the-art machine-learning and ARG-based methods. In addition, we used SIA to estimate selection coefficients at several loci associated with human phenotypes of interest. SIA detected novel signals of selection particular to the European (CEU) population at the MC1R and ABCC11 loci. In addition, it recapitulated signals of selection at the LCT locus and several pigmentation-related genes. Finally, we reanalyzed polymorphism data of a collection of recently radiated southern capuchino seedeater taxa in the genus Sporophila to quantify the strength of selection and improved the power of our previous methods to detect partial soft sweeps. Overall, SIA uses deep learning to leverage the ARG and thereby provides new insight into how selective sweeps shape genomic diversity.

Within the mega-pest lineage of heliothine moths are a number of polyphagous, highly mobile species for which the exchange of adaptive traits through hybridization would affect their properties as pests. The recent invasion of South America by one of the most significant agricultural pests, Helicoverpa armigera, raises concerns for the formation of novel combinations of adaptive genes following hybridization with the closely related Helicoverpa zea. To investigate the propensity for hybridization within the genus Helicoverpa, we carried out whole-genome resequencing of samples from six species, focusing in particular upon H. armigera population structure and its relationship with H. zea. We show that both H. armigera subspecies have greater genetic diversity and effective population sizes than do the other species. We find no signals for gene flow among the six species, other than between H. armigera and H. zea, with nine Brazilian individuals proving to be hybrids of those two species. Eight had largely H. armigera genomes with some introgressed DNA from H. zea scattered throughout. The ninth resembled an F1 hybrid but with stretches of homozygosity for each parental species that reflect previous hybridization. Regions homozygous for H. armigera-derived DNA in this individual included one containing a gustatory receptor and esterase genes previously associated with host range, while another encoded a cytochrome P450 that confers insecticide resistance. Our data point toward the emergence of novel hybrid ecotypes and highlight the importance of monitoring H. armigera genotypes as they spread through the Americas.

Ellison et al. provide the first evidence that the genomic region bearing the Or gene is involved in the presence of carotenoids in carrot. Using a global collection of diverse carrot accessions, they identify 12 genomic regions... Carrots are among the richest sources of provitamin A carotenes in the human diet, but genetic variation in the carotenoid pathway does not fully explain the high levels of carotenoids in carrot roots. Using a diverse collection of modern and historic domesticated varieties, and wild carrot accessions, an association analysis for orange pigmentation revealed a significant genomic region that contains the Or gene, advancing it as a candidate for carotenoid presence in carrot. Analysis of sequence variation at the Or locus revealed a nonsynonymous mutation cosegregating with carotenoid content. This mutation was absent in all wild carrot samples and nearly fixed in all orange domesticated samples. Or has been found to control carotenoid presence in other crops but has not previously been described in carrot. Our analysis also allowed us to more completely characterize the genetic structure of carrot, showing that the Western domesticated carrot largely forms one genetic group, despite dramatic phenotypic differences among market classes. Eastern domesticated and wild accessions form a second group, which reflects the recent cultivation history of carrots in Central Asia. Other wild accessions form distinct geographic groups, particularly on the Iberian peninsula and in Northern Africa. Using genome-wide Fst, nucleotide diversity, and the cross-population composite likelihood ratio, we analyzed the genome for regions putatively under selection during domestication and identified 12 regions that were significant for all three methods of detection, one of which includes the Or gene. The Or domestication allele appears to have been selected after the initial domestication of yellow carrots in the East, near the proposed center of domestication in Central Asia. The rapid fixation of the Or domestication allele in almost all orange and nonorange carrots in the West may explain why it has not been found with less genetically diverse mapping populations.