Association of the single nucleotide polymorphism in CAPN3 gene with growth performance in Merino and Garut (MEGA) backcross sheep.

BACKGROUND: Sheep is one of the commodities of livestock which has been known widely in Indonesia for supporting the national food security. Improvement in genetic quality by selection based on genetic markers for growth is necessary to increase meat production. Quantitative trait loci (QTL) analysis in sheep suggests that Calpain 3 gene (CAPN3) gene might be one of the candidate loci affecting growth traits. CAPN3 is located on chromosome 7 sheep expressed in the skeletal muscles. The aim of this study was to investigate polymorphism CAPN3 intron 11 in Merino × Garut (MEGA) backcross using the PCR-RLFP method and to determine their association with growth traits. RESULTS: SNP intron 11 CAPN3 | BseSI of Merino × Garut (MEGA) backcross sheep was polymorphic and resulted in two alleles of C and T with a frequency of 0.76 and 0.24, respectively, and CC, CT, and TT genotypes with a frequency of 0.54, 0.43, and 0.02, respectively. These loci were found to be in Hardy-Weinberg equilibrium. The SNP CAPN3 | BseSI significantly affected (P < 0.05) the birth weight in Merino × Garut (MEGA) backcross sheep. CONCLUSION: This result suggests that the CAPN3 | BseSI can be used as a genetic marker for birth weight trait in sheep.

Detection of carrier Booroola (FecB) allele in BMPR1B gene of MEGA (Merino × Garut) sheep and its association with growth traits.

BACKGROUND: Bone morphogenetic protein receptor 1B (BMPR1B) gene is one of candidate genes for reproductive and growth traits in sheep. The present study was aimed to detect the Booroola (FecB) allele in BMPR1B gene and its association with growth traits in MEGA (Merino × Garut) sheep. A total of 82DNA samples collected from individual lamb (mixed-sex) blood were genotyped for allelic polymorphism using a PCR-RFLP method. RESULTS: The PCR analysis in BMPR1B gene resulted the amplicons with size of140 bp. The RFLP analysis with AvaII restriction enzymeresultedtwo allelic types of wildtype (A/Fec+) and mutant or Booroola (G/FecB) with frequency of 0.89 and 0.11, respectively. However, the genetic diversity in BMPR1B/AvaII gene of animal studies was categorized tolow category (PIC = 0.18)and under in a genetic equilibrium (χ2 = 1.25). CONCLUSIONS: Itshowed us that carrying FecB allele in the heterozygous sheep were not associated with growth traits in MEGA sheep.

Genetic analysis of digital image derived morphometric traits of black tiger shrimp (Penaeus monodon) by incorporating G × E investigations.

The black tiger shrimp, Penaeus monodon, is the second most economically important aquaculture shrimp species in the world, and in Australia it is one of the most commonly farmed shrimp species. Despite its economic significance, very few studies have reported the genetic evaluation of economically important morphological size and shape traits of shrimp grown in commercial grow-out environments. In this study we obtained genetic parameter estimates and evaluated genotype-by-environment interaction (GxE) for nine body morphological traits of shrimp derived from images. The data set contained image and body weight (BW) records of 5,308 shrimp, from 64 sires and 54 dams, reared in eight grow-out ponds for an average of 133 days. From the images, landmark based morphological distances were computed from which novel morphological traits associated with size and shape were derived for genetic evaluation. These traits included body weight (BW), body length (BL), body size (BS), head size (HS), Abdominal size (AS), abdominal percentage (AP), tail tip (TT), front by back ratio (FBR), condition factor (CF) and condition factor length (CFL). We also evaluated G×E interaction effects of these traits for shrimp reared in different ponds. The heritability estimates for growth related morphological and body weight traits were moderately high (BW: h 2 = 0.32 ± 0.05; BL: h 2 = 0.36 ± 0.06; BS: h 2 = 0.32 ± 0.05; HS: h 2 = 0.31 ± 0.05; AS: h 2 = 0.32 ± 0.05; and TT: h 2 = 0.28 ± 0.05) and low for abdominal percentage and body shape traits (AP: h 2 = 0.09 ± 0.02; FBR: h 2 = 0.08 ± 0.02; CF: h 2 = 0.06 ± 0.02; and CFL: h 2 = 0.003 ± 0.004). G × E interaction were negligible for all traits for shrimp reared in different ponds, suggesting re-ranking is not prevalent for this population. Genetic correlations among growth related morphological traits were high ranging from 0.36 to 0.99, suggesting these traits can be simultaneously improved through indirect genetic selection. However, negative genetic correlations were observed for FBR & CF shape traits with major growth traits (ranged -0.08 to -0.95), suggesting genetic selection for rapid growth will likely result in thick/fatty shrimp over generations. Our study showed image-based landmark data can be successfully employed for genetic evaluation of complex morphological traits of shrimp and is potentially amenable to machine-learning derived parameters in semi-automated high volume phenotyping systems needed under commercial conditions.

Genetic parameters of color phenotypes of black tiger shrimp (Penaeus monodon).

Black tiger shrimp (Penaeus monodon) is the second most important aquaculture species of shrimp in the world. In addition to growth traits, uncooked and cooked body color of shrimp are traits of significance for profitability and consumer acceptance. This study investigated for the first time, the phenotypic and genetic variances and relationships for body weight and body color traits, obtained from image analyses of 838 shrimp, representing the progeny from 55 sires and 52 dams. The color of uncooked shrimp was subjectively scored on a scale from 1 to 4, with "1" being the lightest/pale color and "4" being the darkest color. For cooked shrimp color, shrimp were graded firstly by subjective scoring using a commercial grading score card, where the score ranged from 1 to 12 representing light to deep coloration which was subsequently found to not be sufficiently reliable with poor repeatability of measurement (r = 0.68-0.78) Therefore, all images of cooked color were regraded on a three-point scale from brightest and lightest colored cooked shrimp, to darkest and most color-intense, with a high repeatability (r = 0.80-0.92). Objective color of both cooked and uncooked color was obtained by measurement of RGB intensities (values range from 0 to 255) for each pixel from each shrimp. Using the "convertColor" function in "R", the RGB values were converted to L*a*b* (CIE Lab) systems of color properties. This system of color space was established in 1976, by the International Commission of Illumination (CIE) where "L*" represents the measure of degree of lightness, values range from 0 to 100, where 0 = pure black and 100 = pure white. The value "a*" represents red to green coloration, where a positive value represents the color progression towards red and a negative value towards green. The value "b*" represents blue to yellow coloration, where a positive value refers to more yellowish and negative towards the blue coloration. In total, eight color-related traits were investigated. An ordinal mixed (threshold) model was adopted for manually (subjectively) scored color phenotypes, whereas all other traits were analyzed by linear mixed models using ASReml software to derive variance components and estimated breeding values (EBVs). Moderate to low heritability estimates (0.05-0.35) were obtained for body color traits. For subjectively scored cooked and uncooked color, EBV-based selection would result in substantial genetic improvement in these traits. The genetic correlations among cooked, uncooked and body weight traits were high and ranged from -0.88 to 0.81. These suggest for the first time that 1) cooked color can be improved indirectly by genetic selection based on color of uncooked/live shrimp, and 2) intensity of coloration is positively correlated with body weight traits and hence selection for body weight will also improve color traits in this population.

Genome assembly of the Australian black tiger shrimp (Penaeus monodon) reveals a novel fragmented IHHNV EVE sequence.

Shrimp are a valuable aquaculture species globally; however, disease remains a major hindrance to shrimp aquaculture sustainability and growth. Mechanisms mediated by endogenous viral elements have been proposed as a means by which shrimp that encounter a new virus start to accommodate rather than succumb to infection over time. However, evidence on the nature of such endogenous viral elements and how they mediate viral accommodation is limited. More extensive genomic data on Penaeid shrimp from different geographical locations should assist in exposing the diversity of endogenous viral elements. In this context, reported here is a PacBio Sequel-based draft genome assembly of an Australian black tiger shrimp (Penaeus monodon) inbred for 1 generation. The 1.89 Gbp draft genome is comprised of 31,922 scaffolds (N50: 496,398 bp) covering 85.9% of the projected genome size. The genome repeat content (61.8% with 30% representing simple sequence repeats) is almost the highest identified for any species. The functional annotation identified 35,517 gene models, of which 25,809 were protein-coding and 17,158 were annotated using interproscan. Scaffold scanning for specific endogenous viral elements identified an element comprised of a 9,045-bp stretch of repeated, inverted, and jumbled genome fragments of infectious hypodermal and hematopoietic necrosis virus bounded by a repeated 591/590 bp host sequence. As only near complete linear ∼4 kb infectious hypodermal and hematopoietic necrosis virus genomes have been found integrated in the genome of P. monodon previously, its discovery has implications regarding the validity of PCR tests designed to specifically detect such linear endogenous viral element types. The existence of joined inverted infectious hypodermal and hematopoietic necrosis virus genome fragments also provides a means by which hairpin double-stranded RNA could be expressed and processed by the shrimp RNA interference machinery.

Estimating heritability using family-pooled phenotypic and genotypic data: a simulation study applied to aquaculture.

Estimating heritability based on individual phenotypic and genotypic measurements can be expensive and labour-intensive in commercial aquaculture breeding. Here, the feasibility of estimating heritability using within-family means of phenotypes and allelic frequencies was investigated. Different numbers of full-sib families and family sizes across ten generations with phenotypic and genotypic information on 10 K SNPs were analysed in ten replicates. Three scenarios, representing differing numbers of pools per family (one, two and five) were considered. The results showed that using one pool per family did not reliably estimate the heritability of family means. Using simulation parameters appropriate for aquaculture, at least 200 families of 60 progeny per family divided equally in two pools per family was required to estimate the heritability of family means effectively. Although application of five pools generated more within- and between- family relationships, it reduced the number of individuals per pool and increased within-family residual variation, hence, decreased the heritability of family means. Moreover, increasing the size of pools resulted in increasing the heritability of family means towards one. In addition, heritability of family mean estimates were higher than family heritabilities obtained from Falconer's formula due to lower intraclass correlation estimate compared to the coefficient of relationship.

Development and validation of a RAD-Seq target-capture based genotyping assay for routine application in advanced black tiger shrimp (Penaeus monodon) breeding programs.

BACKGROUND: The development of genome-wide genotyping resources has provided terrestrial livestock and crop industries with the unique ability to accurately assess genomic relationships between individuals, uncover the genetic architecture of commercial traits, as well as identify superior individuals for selection based on their specific genetic profile. Utilising recent advancements in de-novo genome-wide genotyping technologies, it is now possible to provide aquaculture industries with these same important genotyping resources, even in the absence of existing genome assemblies. Here, we present the development of a genome-wide SNP assay for the Black Tiger shrimp (Penaeus monodon) through utilisation of a reduced-representation whole-genome genotyping approach (DArTseq). RESULTS: Based on a single reduced-representation library, 31,262 polymorphic SNPs were identified across 650 individuals obtained from Australian wild stocks and commercial aquaculture populations. After filtering to remove SNPs with low read depth, low MAF, low call rate, deviation from HWE, and non-Mendelian inheritance, 7542 high-quality SNPs were retained. From these, 4236 high-quality genome-wide loci were selected for baits-probe development and 4194 SNPs were included within a finalized target-capture genotype-by-sequence assay (DArTcap). This assay was designed for routine and cost effective commercial application in large scale breeding programs, and demonstrates higher confidence in genotype calls through increased call rate (from 80.2 ± 14.7 to 93.0% ± 3.5%), increased read depth (from 20.4 ± 15.6 to 80.0 ± 88.7), as well as a 3-fold reduction in cost over traditional genotype-by-sequencing approaches. CONCLUSION: Importantly, this assay equips the P. monodon industry with the ability to simultaneously assign parentage of communally reared animals, undertake genomic relationship analysis, manage mate pairings between cryptic family lines, as well as undertake advance studies of genome and trait architecture. Critically this assay can be cost effectively applied as P. monodon breeding programs transition to undertaking genomic selection.

Considerations for Maintaining Family Diversity in Commercially Mass-Spawned Penaeid Shrimp: A Case Study on Penaeus monodon.

Skewed family distributions are common in aquaculture species that are highly fecund, communally (mass) spawned, and/or communally reared. The magnitude of skews pose challenges for maintaining family-specific genetic diversity, as increased resources are required to detect individuals from underrepresented families, or reliably determine relative survival as a measure of family performance. There is limited understanding of family skews or changes in family proportion of communally reared shrimp under commercial rearing conditions and particularly how this may affect genotyping strategies to recover family performance data in breeding programs. In this study, three separate batches of shrimp, Penaeus monodon, were communally spawned and reared, and then sampled as larvae when ponds were stocked at 30 days of culture (DOC) and as juveniles from commercial ponds during harvest at 150 DOC. A total of 199 broodstock contributed to the 5,734 progeny that were genotyped with a custom multiplex single nucleotide polymorphism (SNP) panel, and family assignments were cross-referenced using two parentage assignment methods, CERVUS and COLONY. A total of 121 families were detected, with some families contributing up to 11% of progeny at 30 DOC and up to 18% of progeny at harvest. Significant changes were detected for 20% of families from 30 to 150 DOC, with up to a 9% change in relative contribution. Family skew data was applied in several models to determine the optimal sample size to detect families, along with the ability to detect changes in relative family contribution over time. Results showed that an order of magnitude increase in sampling was required to capture the lowest represented 25% of families, as well as significantly improve the accuracy to determine changes in family proportion from 30 to 150 DOC. Practical measures may be implemented at the hatchery to reduce family skews; a cost-effective measure may be to address the initial magnitude differences in viable progeny produced among families, by pooling equal quantities of hatched larvae from each family. This study demonstrates the relationships between skews in families under commercial conditions, the ability to accurately detect families, and the balance of sampling effort and genotyping cost in highly fecund species such as shrimp.

Genomic comparisons reveal biogeographic and anthropogenic impacts in the koala (Phascolarctos cinereus): a dietary-specialist species distributed across heterogeneous environments.

The Australian koala is an iconic marsupial with highly specific dietary requirements distributed across heterogeneous environments, over a large geographic range. The distribution and genetic structure of koala populations has been heavily influenced by human actions, specifically habitat modification, hunting and translocation of koalas. There is currently limited information on population diversity and gene flow at a species-wide scale, or with consideration to the potential impacts of local adaptation. Using species-wide sampling across heterogeneous environments, and high-density genome-wide markers (SNPs and PAVs), we show that most koala populations display levels of diversity comparable to other outbred species, except for those populations impacted by population reductions. Genetic clustering analysis and phylogenetic reconstruction reveals a lack of support for current taxonomic classification of three koala subspecies, with only a single evolutionary significant unit supported. Furthermore, ~70% of genetic variance is accounted for at the individual level. The Sydney Basin region is highlighted as a unique reservoir of genetic diversity, having higher diversity levels (i.e., Blue Mountains region; AvHecorr=0.20, PL% = 68.6). Broad-scale population differentiation is primarily driven by an isolation by distance genetic structure model (49% of genetic variance), with clinal local adaptation corresponding to habitat bioregions. Signatures of selection were detected between bioregions, with no single region returning evidence of strong selection. The results of this study show that although the koala is widely considered to be a dietary-specialist species, this apparent specialisation has not limited the koala's ability to maintain gene flow and adapt across divergent environments as long as the required food source is available.

De novo assembly, characterization, functional annotation and expression patterns of the black tiger shrimp (Penaeus monodon) transcriptome.

The black tiger shrimp (Penaeus monodon) remains the second most widely cultured shrimp species globally; however, issues with disease and domestication have seen production levels stagnate over the past two decades. To help identify innovative solutions needed to resolve bottlenecks hampering the culture of this species, it is important to generate genetic and genomic resources. Towards this aim, we have produced the most complete publicly available P. monodon transcriptome database to date based on nine adult tissues and eight early life-history stages (BUSCO - Complete: 98.2% [Duplicated: 51.3%], Fragmented: 0.8%, Missing: 1.0%). The assembly resulted in 236,388 contigs, which were then further segregated into 99,203 adult tissue specific and 58,678 early life-history stage specific clusters. While annotation rates were low (approximately 30%), as is typical for a non-model organisms, annotated transcript clusters were successfully mapped to several hundred functional KEGG pathways. Transcripts were clustered into groups within tissues and early life-history stages, providing initial evidence for their roles in specific tissue functions, or developmental transitions. We expect the transcriptome to provide an essential resource to investigate the molecular basis of commercially relevant-significant traits in P. monodon and other shrimp species.

The State of "Omics" Research for Farmed Penaeids: Advances in Research and Impediments to Industry Utilization.

Elucidating the underlying genetic drivers of production traits in agricultural and aquaculture species is critical to efforts to maximize farming efficiency. "Omics" based methods (i.e., transcriptomics, genomics, proteomics, and metabolomics) are increasingly being applied to gain unprecedented insight into the biology of many aquaculture species. While the culture of penaeid shrimp has increased markedly, the industry continues to be impeded in many regards by disease, reproductive dysfunction, and a poor understanding of production traits. Extensive effort has been, and continues to be, applied to develop critical genomic resources for many commercially important penaeids. However, the industry application of these genomic resources, and the translation of the knowledge derived from "omics" studies has not yet been completely realized. Integration between the multiple "omics" resources now available (i.e., genome assemblies, transcriptomes, linkage maps, optical maps, and proteomes) will prove critical to unlocking the full utility of these otherwise independently developed and isolated resources. Furthermore, emerging "omics" based techniques are now available to address longstanding issues with completing keystone genome assemblies (e.g., through long-read sequencing), and can provide cost-effective industrial scale genotyping tools (e.g., through low density SNP chips and genotype-by-sequencing) to undertake advanced selective breeding programs (i.e., genomic selection) and powerful genome-wide association studies. In particular, this review highlights the status, utility and suggested path forward for continued development, and improved use of "omics" resources in penaeid aquaculture.

Genomic Selection in Aquaculture: Application, Limitations and Opportunities With Special Reference to Marine Shrimp and Pearl Oysters.

Within aquaculture industries, selection based on genomic information (genomic selection) has the profound potential to change genetic improvement programs and production systems. Genomic selection exploits the use of realized genomic relationships among individuals and information from genome-wide markers in close linkage disequilibrium with genes of biological and economic importance. We discuss the technical advances, practical requirements, and commercial applications that have made genomic selection feasible in a range of aquaculture industries, with a particular focus on molluscs (pearl oysters, Pinctada maxima) and marine shrimp (Litopenaeus vannamei and Penaeus monodon). The use of low-cost genome sequencing has enabled cost-effective genotyping on a large scale and is of particular value for species without a reference genome or access to commercial genotyping arrays. We highlight the pitfalls and offer the solutions to the genotyping by sequencing approach and the building of appropriate genetic resources to undertake genomic selection from first-hand experience. We describe the potential to capture large-scale commercial phenotypes based on image analysis and artificial intelligence through machine learning, as inputs for calculation of genomic breeding values. The application of genomic selection over traditional aquatic breeding programs offers significant advantages through being able to accurately predict complex polygenic traits including disease resistance; increasing rates of genetic gain; minimizing inbreeding; and negating potential limiting effects of genotype by environment interactions. Further practical advantages of genomic selection through the use of large-scale communal mating and rearing systems are highlighted, as well as presenting rate-limiting steps that impact on attaining maximum benefits from adopting genomic selection. Genomic selection is now at the tipping point where commercial applications can be readily adopted and offer significant short- and long-term solutions to sustainable and profitable aquaculture industries.

A comparative integrated gene-based linkage and locus ordering by linkage disequilibrium map for the Pacific white shrimp, Litopenaeus vannamei.

The Pacific whiteleg shrimp, Litopenaeus vannamei, is the most farmed aquaculture species worldwide with global production exceeding 3 million tonnes annually. Litopenaeus vannamei has been the focus of many selective breeding programs aiming to improve growth and disease resistance. However, these have been based primarily on phenotypic measurements and omit potential gains by integrating genetic selection into existing breeding programs. Such integration of genetic information has been hindered by the limited available genomic resources, background genetic parameters and knowledge on the genetic architecture of commercial traits for L. vannamei. This study describes the development of a comprehensive set of genomic gene-based resources including the identification and validation of 234,452 putative single nucleotide polymorphisms in-silico, of which 8,967 high value SNPs were incorporated into a commercially available Illumina Infinium ShrimpLD-24 v1.0 genotyping array. A framework genetic linkage map was constructed and combined with locus ordering by disequilibrium methodology to generate an integrated genetic map containing 4,817 SNPs, which spanned a total of 4552.5 cM and covered an estimated 98.12% of the genome. These gene-based genomic resources will not only be valuable for identifying regions underlying important L. vannamei traits, but also as a foundational resource in comparative and genome assembly activities.

Identification of key contributors in complex population structures.

Evaluating the genetic contribution of individuals to population structure is essential to select informative individuals for genome sequencing, genotype imputation and to ascertain complex population structures. Existing methods for the selection of informative individuals for genomic imputation solely focus on the identification of key ancestors, which can lead to a loss of phasing accuracy of the reference population. Currently many methods are independently applied to investigate complex population structures. Based on the Eigenvalue Decomposition (EVD) of a genomic relationship matrix we describe a novel approach to evaluate the genetic contribution of individuals to population structure. We combined the identification of key contributors with model-based clustering and population network visualization into an integrated three-step approach, which allows identification of high-resolution population structures and substructures around such key contributors. The approach was applied and validated in four disparate datasets including a simulated population (5,100 individuals and 10,000 SNPs), a highly structured experimental sheep population (1,421 individuals and 44,693 SNPs) and two large complex pedigree populations namely horse (1,077 individuals and 38,124 SNPs) and cattle (2,457 individuals and 45,765 SNPs). In the simulated and experimental sheep dataset, our method, which is unsupervised, successfully identified all known key contributors. Applying our three-step approach to the horse and cattle populations, we observed high-resolution population substructures including the absence of obvious important key contributors. Furthermore, we show that compared to commonly applied strategies to select informative individuals for genotype imputation including the computation of marginal gene contributions (Pedig) and the optimization of genetic relatedness (Rel), the selection of key contributors provided the highest phasing accuracies within the selected reference populations. The presented approach opens new perspectives in the characterization and informed management of populations in general, and in areas such as conservation genetics and selective animal breeding in particular, where assessing the genetic contribution of influential and admixed individuals is crucial for research and management applications. As such, this method provides a valuable complement to common applied tools to visualize complex population structures and to select individuals for re-sequencing.

A Meta-Assembly of Selection Signatures in Cattle.

Since domestication, significant genetic improvement has been achieved for many traits of commercial importance in cattle, including adaptation, appearance and production. In response to such intense selection pressures, the bovine genome has undergone changes at the underlying regions of functional genetic variants, which are termed "selection signatures". This article reviews 64 recent (2009-2015) investigations testing genomic diversity for departure from neutrality in worldwide cattle populations. In particular, we constructed a meta-assembly of 16,158 selection signatures for individual breeds and their archetype groups (European, African, Zebu and composite) from 56 genome-wide scans representing 70,743 animals of 90 pure and crossbred cattle breeds. Meta-selection-scores (MSS) were computed by combining published results at every given locus, within a sliding window span. MSS were adjusted for common samples across studies and were weighted for significance thresholds across and within studies. Published selection signatures show extensive coverage across the bovine genome, however, the meta-assembly provides a consensus profile of 263 genomic regions of which 141 were unique (113 were breed-specific) and 122 were shared across cattle archetypes. The most prominent peaks of MSS represent regions under selection across multiple populations and harboured genes of known major effects (coat color, polledness and muscle hypertrophy) and genes known to influence polygenic traits (stature, adaptation, feed efficiency, immunity, behaviour, reproduction, beef and dairy production). As the first meta-assembly of selection signatures, it offers novel insights about the hotspots of selective sweeps in the bovine genome, and this method could equally be applied to other species.

netview p: a network visualization tool to unravel complex population structure using genome-wide SNPs.

Network-based approaches are emerging as valuable tools for the analysis of complex genetic structure in wild and captive populations. netview p combines data quality control with the construction of population networks through mutual k-nearest neighbours thresholds applied to genome-wide SNPs. The program is cross-platform compatible, open-source and efficiently operates on data ranging from hundreds to hundreds of thousands of SNPs. The pipeline was used for the analysis of pedigree data from simulated (n = 750, SNPs = 1279) and captive silver-lipped pearl oysters (n = 415, SNPs = 1107), wild populations of the European hake from the Atlantic and Mediterranean (n = 834, SNPs = 380) and grey wolves from North America (n = 239, SNPs = 78 255). The population networks effectively visualize large- and fine-scale genetic structure within and between populations, including family-level structure and relationships. netview p comprises a network-based addition to other population analysis tools and provides user-friendly access to a complex network analysis pipeline through implementation in python.

Targeted Analysis Reveals an Important Role of JAK-STAT-SOCS Genes for Milk Production Traits in Australian Dairy Cattle.

The Janus kinase and signal transducer and activator of transcription (JAK-STAT) pathway genes along with suppressors of cytokine signalling (SOCS) family genes play a crucial role in controlling cytokine signals in the mammary gland and thus mammary gland development. Mammary gene expression studies showed differential expression patterns for all the JAK-STAT pathway genes. Gene expression studies using qRT-PCR revealed differential expression of SOCS2, SOCS4, and SOCS5 genes across the lactation cycle in dairy cows. Using genotypes from 1,546 Australian Holstein-Friesian bulls, a statistical model for an association analysis based on SNPs within 500 kb of JAK-STAT pathway genes, and SOCS genes alone was constructed. The analysis suggested that these genes and pathways make a significant contribution to the Australian milk production traits. There were 24 SNPs close to SOCS1, SOCS3, SOCS5, SOCS7, and CISH genes that were significantly associated with Australian Profit Ranking (APR), Australian Selection Index (ASI), and protein yield (PY). This study supports the view that there may be some merit in choosing SNPs around functionally relevant genes for the selection and genetic improvement schemes for dairy production traits.

Composite Selection Signals for Complex Traits Exemplified Through Bovine Stature Using Multibreed Cohorts of European and African Bos taurus.

Understanding the evolution and molecular architecture of complex traits is important in domestic animals. Due to phenotypic selection, genomic regions develop unique patterns of genetic diversity called signatures of selection, which are challenging to detect, especially for complex polygenic traits. In this study, we applied the composite selection signals (CSS) method to investigate evidence of positive selection in a complex polygenic trait by examining stature in phenotypically diverse cattle comprising 47 European and 8 African Bos taurus breeds, utilizing a panel of 38,033 SNPs genotyped on 1106 animals. CSS were computed for phenotypic contrasts between multibreed cohorts of cattle by classifying the breeds according to their documented wither height to detect the candidate regions under selection. Using the CSS method, clusters of signatures of selection were detected at 26 regions (9 in European and 17 in African cohorts) on 13 bovine autosomes. Using comparative mapping information on human height, 30 candidate genes mapped at 12 selection regions (on 8 autosomes) could be linked to bovine stature diversity. Of these 12 candidate gene regions, three contained known genes (i.e., NCAPG-LCORL, FBP2-PTCH1, and PLAG1-CHCHD7) related to bovine stature, and nine were not previously described in cattle (five in European and four in African cohorts). Overall, this study demonstrates the utility of CSS coupled with strategies of combining multibreed datasets in the identification and discovery of genomic regions underlying complex traits. Characterization of multiple signatures of selection and their underlying candidate genes will elucidate the polygenic nature of stature across cattle breeds.

Composite selection signals can localize the trait specific genomic regions in multi-breed populations of cattle and sheep.

BACKGROUND: Discerning the traits evolving under neutral conditions from those traits evolving rapidly because of various selection pressures is a great challenge. We propose a new method, composite selection signals (CSS), which unifies the multiple pieces of selection evidence from the rank distribution of its diverse constituent tests. The extreme CSS scores capture highly differentiated loci and underlying common variants hauling excess haplotype homozygosity in the samples of a target population. RESULTS: The data on high-density genotypes were analyzed for evidence of an association with either polledness or double muscling in various cohorts of cattle and sheep. In cattle, extreme CSS scores were found in the candidate regions on autosome BTA-1 and BTA-2, flanking the POLL locus and MSTN gene, for polledness and double muscling, respectively. In sheep, the regions with extreme scores were localized on autosome OAR-2 harbouring the MSTN gene for double muscling and on OAR-10 harbouring the RXFP2 gene for polledness. In comparison to the constituent tests, there was a partial agreement between the signals at the four candidate loci; however, they consistently identified additional genomic regions harbouring no known genes. Persuasively, our list of all the additional significant CSS regions contains genes that have been successfully implicated to secondary phenotypic diversity among several subpopulations in our data. For example, the method identified a strong selection signature for stature in cattle capturing selective sweeps harbouring UQCC-GDF5 and PLAG1-CHCHD7 gene regions on BTA-13 and BTA-14, respectively. Both gene pairs have been previously associated with height in humans, while PLAG1-CHCHD7 has also been reported for stature in cattle. In the additional analysis, CSS identified significant regions harbouring multiple genes for various traits under selection in European cattle including polledness, adaptation, metabolism, growth rate, stature, immunity, reproduction traits and some other candidate genes for dairy and beef production. CONCLUSIONS: CSS successfully localized the candidate regions in validation datasets as well as identified previously known and novel regions for various traits experiencing selection pressure. Together, the results demonstrate the utility of CSS by its improved power, reduced false positives and high-resolution of selection signals as compared to individual constituent tests.