A regression-based approach to selection mapping

Detection of Selection Signatures Underlying Production and Adaptive Traits Based on Whole-Genome Sequencing of Six Donkey Populations

Simple Summary

After a long period of artificial selection, the donkey now presents a variety of body types and production performance values. In this experiment, we performed selective signal scanning on the second-generation resequencing data of six different varieties. The regions and candidate genes related to artificial selection were identified to provide reference for future breeding.

Abstract

Donkeys (Equus asinus) are an important farm animal. After long-term natural and artificial selection, donkeys now exhibit a variety of body sizes and production performance values. In this study, six donkey breeds, representing different regions and phenotypes, were used for second-generation resequencing. The sequencing results revealed more than seven million single nucleotide variants (SNVs), with an average of more than four million SNVs per species. We combined two methods, Z-transformed heterozygosity (ZHp) and unbiased estimates of pairwise fixation index (di) values, to analyze the signatures of selection. We mapped 11 selected regions and identified genes associated with coat color, body size, motion capacity, and high-altitude adaptation. These candidate genes included staining (ASIP and KITLG), body type (ACSL4, BCOR, CDKL5, LCOR, NCAPG, and TBX3), exercise (GABPA), and adaptation to low-oxygen environments (GLDC and HBB). We also analyzed the SNVs of the breed-specific genes for their potential functions and found that there are three varieties in the conserved regions with breed-specific mutation sites. Our results provide data to support the establishment of the donkey SNV chip and reference information for the utilization of the genetic resources of Chinese domestic donkeys.

Identifying adaptive alleles in the human genome: from selection mapping to functional validation

The suite of phenotypic diversity across geographically distributed human populations is the outcome of genetic drift, gene flow, and natural selection throughout human evolution. Human genetic variation underlying local biological adaptations to selective pressures is incompletely characterized. With the emergence of population genetics modeling of large-scale genomic data derived from diverse populations, scientists are able to map signatures of natural selection in the genome in a process known as selection mapping. Inferred selection signals further can be used to identify candidate functional alleles that underlie putative adaptive phenotypes. Phenotypic association, fine mapping, and functional experiments facilitate the identification of candidate adaptive alleles. Functional investigation of candidate adaptive variation using novel techniques in molecular biology is slowly beginning to unravel how selection signals translate to changes in biology that underlie the phenotypic spectrum of our species. In addition to informing evolutionary hypotheses of adaptation, the discovery and functional annotation of adaptive alleles also may be of clinical significance. While selection mapping efforts in non-European populations are growing, there remains a stark under-representation of diverse human populations in current public genomic databases, of both clinical and non-clinical cohorts. This lack of inclusion limits the study of human biological variation. Identifying and functionally validating candidate adaptive alleles in more global populations is necessary for understanding basic human biology and human disease.

Genomic, Transcriptomic and Epigenomic Tools to Study the Domestication of Plants and Animals: A Field Guide for Beginners

In the last decade, genomics and the related fields of transcriptomics and epigenomics have revolutionized the study of the domestication process in plants and animals, leading to new discoveries and new unresolved questions. Given that some domesticated taxa have been more studied than others, the extent of genomic data can range from vast to nonexistent, depending on the domesticated taxon of interest. This review is meant as a rough guide for students and academics that want to start a domestication research project using modern genomic tools, as well as for researchers already conducting domestication studies that are interested in following a genomic approach and looking for alternate strategies (cheaper or more efficient) and future directions. We summarize the theoretical and technical background needed to carry out domestication genomics, starting from the acquisition of a reference genome and genome assembly, to the sampling design for population genomics, paleogenomics, transcriptomics, epigenomics and experimental validation of domestication-related genes. We also describe some examples of the aforementioned approaches and the relevant discoveries they made to understand the domestication of the studied taxa.

Selection signature analysis reveals genes underlying sheep milking performance

Sheep milk is the most important feed resource for newborn lambs and an important food resource for humans. Sheep milk production and ingredients are influenced by genetic and environmental factors. In this study, we implemented selection signature analysis using Illumina Ovine SNP50 BeadChip data of 78 meat Lacaune and 103 milk Lacaune sheep, which have similar genetic backgrounds, from the Sheep HapMap project to identify candidate genes related to ovine milk traits. Since different methods can detect different variation types and complement each other, we used a haplotype-based method (hapFLK) to implement selection signature analysis. The results revealed six selection signature regions showing signs of being selected ( P < 0.001 ): chromosomes 1, 2, 3, 6, 13 and 18. In addition, 38 quantitative trait loci (QTLs) related to sheep milk performance were identified in selection signature regions, which contain 334 candidate genes. Of those, SUCNR1 (succinate receptor 1) and PPARGC1A (PPARG coactivator 1 alpha) may be the most significant genes that affect sheep milking performance, which supply a significant indication for future studies to investigate candidate genes that play an important role in milk production and quality.

High-resolution analysis of selection sweeps identified between fine-wool Merino and coarse-wool Churra sheep breeds

BACKGROUND

With the aim of identifying selection signals in three Merino sheep lines that are highly specialized for fine wool production (Australian Industry Merino, Australian Merino and Australian Poll Merino) and considering that these lines have been subjected to selection not only for wool traits but also for growth and carcass traits and parasite resistance, we contrasted the OvineSNP50 BeadChip (50 K-chip) pooled genotypes of these Merino lines with the genotypes of a coarse-wool breed, phylogenetically related breed, Spanish Churra dairy sheep. Genome re-sequencing datasets of the two breeds were analyzed to further explore the genetic variation of the regions initially identified as putative selection signals.

RESULTS

Based on the 50 K-chip genotypes, we used the overlapping selection signals (SS) identified by four selection sweep mapping analyses (that detect genetic differentiation, reduced heterozygosity and patterns of haplotype diversity) to define 18 convergence candidate regions (CCR), five associated with positive selection in Australian Merino and the remainder indicating positive selection in Churra. Subsequent analysis of whole-genome sequences from 15 Churra and 13 Merino samples identified 142,400 genetic variants (139,745 bi-allelic SNPs and 2655 indels) within the 18 defined CCR. Annotation of 1291 variants that were significantly associated with breed identity between Churra and Merino samples identified 257 intragenic variants that caused 296 functional annotation variants, 275 of which were located across 31 coding genes. Among these, four synonymous and four missense variants (NPR2_His847Arg, NCAPG_Ser585Phe, LCORL_Asp1214Glu and LCORL_Ile1441Leu) were included.

CONCLUSIONS

Here, we report the mapping and genetic variation of 18 selection signatures that were identified between Australian Merino and Spanish Churra sheep breeds, which were validated by an additional contrast between Spanish Merino and Churra genotypes. Analysis of whole-genome sequencing datasets allowed us to identify divergent variants that may be viewed as candidates involved in the phenotypic differences for wool, growth and meat production/quality traits between the breeds analyzed. The four missense variants located in the NPR2, NCAPG and LCORL genes may be related to selection sweep regions previously identified and various QTL reported in sheep in relation to growth traits and carcass composition.

Use of high-density SNP data to identify patterns of diversity and signatures of selection in broiler chickens

The development of broiler chickens over the last 70 years has been accompanied by large phenotypic changes, so that the resulting genomic signatures of selection should be detectable by current statistical techniques with sufficiently dense genetic markers. Using two approaches, this study analysed high-density SNP data from a broiler chicken line to detect low-diversity genomic regions characteristic of past selection. Seven regions with zero diversity were identified across the genome. Most of these were very small and did not contain many genes. In addition, fifteen regions were identified with diversity increasing asymptotically from a low level. These regions were larger and thus generally included more genes. Several candidate genes for broiler traits were found within these 'regression regions', including IGF1, GPD2 and MTNR1AI. The results suggest that the identification of zero-diversity regions is too restrictive for characterizing regions under selection, but that regions showing patterns of diversity along the chromosome that are consistent with selective sweeps contain a number of genes that are functional candidates for involvement in broiler development. Many regions identified in this study overlap or are close to regions identified in layer chicken populations, possibly due to their shared precommercialization history or to shared selection pressures between broilers and layers.

Whole-genome sequencing of eight goat populations for the detection of selection signatures underlying production and adaptive traits

The goat (Capra hircus) is one of the first farm animals that have undergone domestication and extensive natural and artificial selection by adapting to various environments, which in turn has resulted in its high level of phenotypic diversity. Here, we generated medium-coverage (9–13×) sequences from eight domesticated goat breeds, representing morphologically or geographically specific populations, to identify genomic regions representing selection signatures. We discovered ~10 million single nucleotide polymorphisms (SNPs) for each breed. By combining two approaches, ZH_p and di values, we identified 22 genomic regions that may have contributed to the phenotypes in coat color patterns, body size, cashmere traits, as well as high altitude adaptation in goat populations. Candidate genes underlying strong selection signatures including coloration (ASIP, KITLG, HTT, GNA11, and OSTM1), body size (TBX15, DGCR8, CDC25A, and RDH16), cashmere traits (LHX2, FGF9, and WNT2), and hypoxia adaptation (CDK2, SOCS2, NOXA1, and ENPEP) were identified. We also identified candidate functional SNPs within selected genes that may be important for each trait. Our results demonstrated the potential of using sequence data in identifying genomic regions that are responsible for agriculturally significant phenotypes in goats, which in turn can be used in the selection of goat breeds for environmental adaptation and domestication.

Application of selection mapping to identify genomic regions associated with dairy production in sheep

In Europe, especially in Mediterranean areas, the sheep has been traditionally exploited as a dual purpose species, with income from both meat and milk. Modernization of husbandry methods and the establishment of breeding schemes focused on milk production have led to the development of "dairy breeds." This study investigated selective sweeps specifically related to dairy production in sheep by searching for regions commonly identified in different European dairy breeds. With this aim, genotypes from 44,545 SNP markers covering the sheep autosomes were analysed in both European dairy and non-dairy sheep breeds using two approaches: (i) identification of genomic regions showing extreme genetic differentiation between each dairy breed and a closely related non-dairy breed, and (ii) identification of regions with reduced variation (heterozygosity) in the dairy breeds using two methods. Regions detected in at least two breeds (breed pairs) by the two approaches (genetic differentiation and at least one of the heterozygosity-based analyses) were labeled as core candidate convergence regions and further investigated for candidate genes. Following this approach six regions were detected. For some of them, strong candidate genes have been proposed (e.g. ABCG2, SPP1), whereas some other genes designated as candidates based on their association with sheep and cattle dairy traits (e.g. LALBA, DGAT1A) were not associated with a detectable sweep signal. Few of the identified regions were coincident with QTL previously reported in sheep, although many of them corresponded to orthologous regions in cattle where QTL for dairy traits have been identified. Due to the limited number of QTL studies reported in sheep compared with cattle, the results illustrate the potential value of selection mapping to identify genomic regions associated with dairy traits in sheep.

Detecting natural selection in genomic data

The past fifty years have seen the development and application of numerous statistical methods to identify genomic regions that appear to be shaped by natural selection. These methods have been used to investigate the macro- and microevolution of a broad range of organisms, including humans. Here, we provide a comprehensive outline of these methods, explaining their conceptual motivations and statistical interpretations. We highlight areas of recent and future development in evolutionary genomics methods and discuss ongoing challenges for researchers employing such tests. In particular, we emphasize the importance of functional follow-up studies to characterize putative selected alleles and the use of selection scans as hypothesis-generating tools for investigating evolutionary histories.