BACKGROUND: The breeding of layers emphasizes the continual selection of egg-related traits, such as egg production, egg quality and eggshell, which enhance their productivity and meet the demand of market. As the breeding process continued, the genomic homozygosity of layers gradually increased, resulting in the emergence of runs of homozygosity (ROH). Therefore, ROH analysis can be used in conjunction with other methods to detect selection signatures and identify candidate genes associated with various important traits in layer breeding.
RESULTS: In this study, we generated whole-genome sequencing data from 686 hens in a Rhode Island Red population that had undergone fifteen consecutive generations of intensive artificial selection. We performed a genome-wide ROH analysis and utilized multiple methods to detect signatures of selection. A total of 141,720 ROH segments were discovered in whole population, and most of them (97.35%) were less than 3 Mb in length. Twenty-three ROH islands were identified, and they overlapped with some regions bearing selection signatures, which were detected by the De-correlated composite of multiple signals methods (DCMS). Sixty genes were discovered and functional annotation analysis revealed the possible roles of them in growth, development, immunity and signaling in layers. Additionally, two-tailed analyses including DCMS and ROH for 44 phenotypes of layers were conducted to find out the genomic differences between subgroups of top and bottom 10% phenotype of individuals. Combining the results of GWAS, we observed that regions significantly associated with traits also exhibited selection signatures between the high and low subgroups. We identified a region significantly associated with egg weight near the 25 Mb region of GGA 1, which exhibited selection signatures and has higher genomic homozygosity in the low egg weight subpopulation. This suggests that the region may be play a role in the decline in egg weight.
CONCLUSIONS: In summary, through the combined analysis of ROH, selection signatures, and GWAS, we identified several genomic regions that associated with the production traits of layers, providing reference for the study of layer genome.

Inferring the demographic history of populations provides fundamental insights into species dynamics and is essential for developing a null model to accurately study selective processes. However, background selection and selective sweeps can produce genomic signatures at linked sites that mimic or mask signals associated with historical population size change. While the theoretical biases introduced by the linked effects of selection have been well established, it is unclear whether ARG-based approaches to demographic inference in typical empirical analyses are susceptible to mis-inference due to these effects. To address this, we developed highly realistic forward simulations of human and Drosophila melanogaster populations, including empirically estimated variability of gene density, mutation rates, recombination rates, purifying and positive selection, across different historical demographic scenarios, to broadly assess the impact of selection on demographic inference using a genealogy-based approach. Our results indicate that the linked effects of selection minimally impact demographic inference for human populations, though it could cause mis-inference in populations with similar genome architecture and population parameters experiencing more frequent recurrent sweeps. We found that accurate demographic inference of D. melanogaster populations by ARG-based methods is compromised by the presence of pervasive background selection alone, leading to spurious inferences of recent population expansion which may be further worsened by recurrent sweeps, depending on the proportion and strength of beneficial mutations. Caution and additional testing with species-specific simulations are needed when inferring population history with non-human populations using ARG-based approaches to avoid mis-inference due to the linked effects of selection.

The taxonomic classification of Picea meyeri and P. mongolica has long been controversial. To investigate the genetic relatedness, evolutionary history, and population history dynamics of these species, genotyping-by-sequencing (GBS) technology was utilized to acquire whole-genome single nucleotide polymorphism (SNP) markers, which were subsequently used to assess population structure, population dynamics, and adaptive differentiation. Phylogenetic and population structural analyses at the genomic level indicated that although the ancestor of P. mongolica was a hybrid of P. meyeri and P. koraiensis, P. mongolica is an independent Picea species. Additionally, P. mongolica is more closely related to P. meyeri than to P. koraiensis, which is consistent with its geographic distribution. There were up to eight instances of interspecific and intraspecific gene flow between P. meyeri and P. mongolica. The P. meyeri and P. mongolica effective population sizes generally decreased, and Maxent modeling revealed that from the Last Glacial Maximum (LGM) to the present, their habitat areas decreased initially and then increased. However, under future climate scenarios, the habitat areas of both species were projected to decrease, especially under high-emission scenarios, which would place P. mongolica at risk of extinction and in urgent need of protection. Local adaptation has promoted differentiation between P. meyeri and P. mongolica. Genotype‒environment association analysis revealed 96,543 SNPs associated with environmental factors, mainly related to plant adaptations to moisture and temperature. Selective sweeps revealed that the selected genes among P. meyeri, P. mongolica and P. koraiensis are primarily associated in vascular plants with flowering, fruit development, and stress resistance. This research enhances our understanding of Picea species classification and provides a basis for future genetic improvement and species conservation efforts.

Detecting introgression between closely related populations or species is a fundamental objective in evolutionary biology. Existing methods for detecting migration and inferring migration rates from population genetic data often assume a neutral model of evolution. Growing evidence of the pervasive impact of selection on large portions of the genome across diverse taxa suggests that this assumption is unrealistic in most empirical systems. Further, ignoring selection has previously been shown to negatively impact demographic inferences (e.g., of population size histories). However, the impacts of biologically realistic selection on inferences of migration remain poorly explored. Here, we simulate data under models of background selection, selective sweeps, balancing selection, and adaptive introgression. We show that ignoring selection sometimes leads to false inferences of migration in popularly used methods that rely on the site frequency spectrum (SFS). Specifically, balancing selection and some models of background selection result in the rejection of isolation-only models in favor of isolation-with-migration models and lead to elevated estimates of migration rates. BPP, a method that analyzes sequence data directly, showed false positives for all conditions at recent divergence times, but balancing selection also led to false positives at medium divergence times. Our results suggest that such methods may be unreliable in some empirical systems, such that new methods that are robust to selection need to be developed.

Population branch statistics, which estimate the branch lengths of focal populations with respect to two outgroups, have been used as an alternative to FST-based genome-wide scans for identifying loci associated with local selective sweeps. In addition to the original population branch statistic (PBS), there are subsequently proposed branch rescalings: normalized population branch statistic (PBSn1), which adjusts focal branch length with respect to outgroup branch lengths at the same locus, and population branch excess (PBE), which also incorporates median branch lengths at other loci. PBSn1 and PBE have been proposed to be less sensitive to allele frequency divergence generated by background selection or geographically ubiquitous positive selection rather than local selective sweeps. However, the accuracy and statistical power of branch statistics have not been systematically assessed. To do so, we simulate genomes in representative large and small populations with varying proportions of sites evolving under genetic drift or background selection (approximated using variable Ne), local selective sweeps, and geographically parallel selective sweeps. We then assess the probability that local selective sweep loci are correctly identified as outliers by FST and by each of the branch statistics. We find that branch statistics consistently outperform FST at identifying local sweeps. When background selection and/or parallel sweeps are introduced, PBSn1 and especially PBE correctly identify local sweeps among their top outliers at a higher frequency than PBS. These results validate the greater specificity of rescaled branch statistics such as PBE to detect population-specific positive selection, supporting their use in genomic studies focused on local adaptation.

Understanding the genetic basis of local adaption is crucial in the context of global climate change. Mangroves, as salt-tolerant trees and shrubs in the intertidal zone of tropical and subtropical coastlines, are particularly vulnerable to climate change. Kandelia obovata, the most cold-tolerant mangrove species, has undergone ecological speciation from its cold-intolerant counterpart, Kandelia candel, with geographic separation by the South China Sea. In this study, we conducted whole-genome re-sequencing of K. obovata populations along China\'s southeast coast, to elucidate the genetic basis responsible for mangrove local adaptation to climate. Our analysis revealed a strong population structure among the three K. obovata populations, with complex demographic histories involving population expansion, bottleneck, and gene flow. Genome-wide scans unveiled pronounced patterns of selective sweeps in highly differentiated regions among pairwise populations, with stronger signatures observed in the northern populations compared to the southern population. Additionally, significant genotype-environment associations for temperature-related variables were identified, while no associations were detected for precipitation. A set of 39 high-confidence candidate genes underlying local adaptation of K. obovata were identified, which are distinct from genes under selection detected by comparison between K. obovata and its cold-intolerant relative K. candel. These results significantly contribute to our understanding of the genetic underpinnings of local adaptation in K. obovata and provide valuable insights into the evolutionary processes shaping the genetic diversity of mangrove populations in response to climate change.

The rate at which beneficial alleles fix in a population depends on the probability of and time to fixation of such alleles. Both of these quantities can be significantly impacted by population subdivision and limited gene flow. Here, we investigate how limited dispersal influences the rate of fixation of beneficial de novo mutations, as well as fixation time from standing genetic variation. We investigate this for a population structured according to the island model of dispersal allowing us to use the diffusion approximation, which we complement with simulations. We find that fixation may take on average fewer generations under limited dispersal than under panmixia when selection is moderate. This is especially the case if adaptation occurs from de novo recessive mutations, and dispersal is not too limited (such that approximately FST<0.2). The reason is that mildly limited dispersal leads to only a moderate increase in effective population size (which slows down fixation), but is sufficient to cause a relative excess of homozygosity due to inbreeding, thereby exposing rare recessive alleles to selection (which accelerates fixation). We also explore the effect of metapopulation dynamics through local extinction followed by recolonization, finding that such dynamics always accelerate fixation from standing genetic variation, while de novo mutations show faster fixation interspersed with longer waiting times. Finally, we discuss the implications of our results for the detection of sweeps, suggesting that limited dispersal mitigates the expected differences between the genetic signatures of sweeps involving recessive and dominant alleles.

The roughskin sculpin (Trachidermus fasciatus) is an endangered fish species in China. In recent years, artificial breeding technology has made significant progress, and the population of roughskin sculpin has recovered in the natural environment through enhancement programs and the release of juveniles. However, the effects of released roughskin sculpin on the genetic structure and diversity of wild populations remain unclear. Studies on genetic diversity analysis based on different types and numbers of molecular markers have yielded inconsistent results. In this study, we obtained 2,610,157 high-quality SNPs and 494,698 InDels through whole-genome resequencing of two farmed populations and one wild population. Both farmed populations showed consistent levels of genomic polymorphism and a slight increase in linkage compared with wild populations. The population structure of the two farmed populations was distinct from that of the wild population, but the degree of genetic differentiation was low (overall average Fst = 0.015). Selective sweep analysis showed that 523,529 genes were selected in the two farmed populations, and KEGG enrichment analysis showed that the selected genes were related to amino acid metabolism, which might be caused by artificial feeding. The findings of this study provide valuable additions to the existing genomic resources to help conserve roughskin sculpin populations.

The detection of selective sweeps from population genomic data often relies on the premise that the beneficial mutations in question have fixed very near the sampling time. As it has been previously shown that the power to detect a selective sweep is strongly dependent on the time since fixation as well as the strength of selection, it is naturally the case that strong, recent sweeps leave the strongest signatures. However, the biological reality is that beneficial mutations enter populations at a rate, one that partially determines the mean wait time between sweep events and hence their age distribution. An important question thus remains about the power to detect recurrent selective sweeps when they are modeled by a realistic mutation rate and as part of a realistic distribution of fitness effects, as opposed to a single, recent, isolated event on a purely neutral background as is more commonly modeled. Here we use forward-in-time simulations to study the performance of commonly used sweep statistics, within the context of more realistic evolutionary baseline models incorporating purifying and background selection, population size change, and mutation and recombination rate heterogeneity. Results demonstrate the important interplay of these processes, necessitating caution when interpreting selection scans; specifically, false-positive rates are in excess of true-positive across much of the evaluated parameter space, and selective sweeps are often undetectable unless the strength of selection is exceptionally strong.

Understanding how evolutionary processes shape the genetic variations and influence the response of species to environmental alterations is critical for biodiversity conservation and molecular breeding. Gymnocypris przewalskii przewalskii is the only known cyprinid fish that dwells in the brackish water of Lake Qinghai on the Qinghai-Tibetan Plateau. To reveal the genetic basis of its adaptation to high salinity and alkalinity, whole-genome sequencing was performed in G. p. przewalskii and its freshwater relatives Gymnocypris eckloni and Gymnocypris przewalskii ganzihonensis. Compared with freshwater species, lower genetic diversity and higher linkage disequilibrium were observed in G. p. przewalskii. Selective sweep analysis identified 424 core-selective genes enriched in transport activities. Transfection analysis showed that genetic changes in the positively selected gene aquaporin 3 (AQP3) improved cell viability after salt treatment, suggesting its involvement in brackish water adaptation. Our analysis indicates that ion and water transporter genes experienced intensive selection, which might have contributed to the maintenance of high osmolality and ion content in G. p. przewalskii. The current study identified key molecules involved in the adaptation of fish to brackish water, providing valuable genomic resources for the molecular breeding of salt-tolerant fish.