High-density Single Nucleotide Polymorphism Research Articles

Bayesian genome-wide analysis of cattle traits using variants with functional and evolutionary significance

Context Functional genomics studies have highlighted genomic regions with regulatory and evolutionary significance. Such information independent of association analysis may benefit fine-mapping and genomic selection of economically important traits. However, systematic evaluation of the use of functional information in mapping, and genomic selection of cattle traits, is lacking. Also, single-nucleotide polymorphisms (SNPs) from the high-density (HD) panel are known to tag informative variants, but the performance of genomic prediction using HD SNPs together with variants supported by different functional genomics is unknown. Aims We selected six sets of functionally important variants and modelled each set together with HD SNPs in Bayesian models to map and predict protein, fat and milk yield as well as mastitis, somatic cell count and temperament of dairy cattle. Methods Two models were used, namely (1) BayesR, which includes priors of four distribution of variant effects, and (2) BayesRC, which includes additional priors of different functional classes of variants. Bayesian models were trained in three breeds of 28 000 cows of Holstein, Jersey and Australian Red and predicted into 2600 independent bulls. Key results Adding functionally important variants significantly increased the enrichment of genetic variance explained for mapped variants, suggesting improved genome-wide mapping precision. Such improvement was significantly higher when the same set of variants was modelled by BayesRC than by BayesR. Combining functional variant sets with HD SNPs improves genomic prediction accuracy in the majority of the cases and such improvement was more common and stronger for non-Holstein breeds and traits such as mastitis, somatic cell count and temperament. In contrast, adding a large number of random sequence variants to HD SNPs reduces mapping precision and has a worse or similar prediction accuracy, compared with using HD SNPs alone to map or predict. While BayesRC tended to have better genomic prediction accuracy than did BayesR, the overall difference in prediction accuracy between the two models was insignificant. Conclusions Our findings demonstrated the usefulness of functional data in genomic mapping and prediction. Implications We have highlighted the need for effective tools exploiting complex functional datasets to improve genomic prediction.

Ultra-small SNP panels to uniquely identify individuals in thousands of samples

Context Genomic profiles are the only information source that can uniquely identify an individual but have not yet been strongly considered in the context of paddock to plate traceability due to the lack of value proposition. Aim The aim of this study was to define the minimum number of single nucleotide polymorphisms (SNP) required to distinguish a unique genotype profile for each individual sample within a large given population. At the same time, ad hoc approaches were explored to reduce SNP density, and therefore, the size of the dataset to improve computing efficiency and storage requirements while maintaining informativeness to distinguish individuals. Methods Data for this study included two datasets. One included 78 411 high-density SNP genotypes from commercial Angus cattle and the other 2107 from a research data (1000-bull genome data). In a stepwise approach, different-size SNP panels were explored, with the last step being a successive removal resulting in the smallest set of SNPs that still produced the maximum number of unique genotypes. Key results First study that has demonstrated for large datasets, that ultra-small SNP panels with 20–23 SNPs can generate unique genotypes for up to ~80 000 individuals, allowing for 100% matching accuracy. Conclusions Ultra-small SNP panels could provide an efficient method to approach the large-scale task of the traceability of beef products through the beef supply chain. Implications Genomic tools could enhance supply-chain traceability.

Genetic parameter estimates for male and female fertility traits using genomic data to improve fertility in Australian beef cattle

Context Studies have shown that favourable genetic correlations exist between female and male fertility traits. However, investigations regarding these correlations in Australian tropical beef cattle are limited to either pedigree or single-breed analysis. Aim The study aims to use genomic information to estimate genetic parameters of six female and seven male fertility traits measured during the first 2 years of life, in two tropical breeds. Methods Single-, bivariate and multi-trait models were used to analyse fertility data from Brahman (BB; 996 cows and 1022 bulls); and Tropical Composite (TC; 1091 cows and 998 bulls) cattle genotyped with high-density single-nucleotide polymorphism chip assay. Key results Heritability estimates in BB cows ranged from low (0.07 ± 0.04) for days to calving at the first calving opportunity (DC1, days) to high (0.57 ± 0.08) for age at first corpus luteum (AGECL, days). In BB bulls, estimates varied from low (0.09 ± 0.05) for sperm motility (score 1–5) to high (0.64 ± 0.06) for scrotal circumference (SC) measured at 24 months (SC24, cm). Similarly, heritability estimates in TC cows were low (0.04 ± 0.03) for DC1 and high (0.69 ± 0.02) for AGECL. In TC bulls, the heritability was low (0.09 ± 0.05) for sperm motility and high (0.69 ± 0.07) for SC24. Within-sex for both breeds, blood concentrations of insulin growth-factor 1 (IGF1) measured in cows at 18 months (IGF1c) were negatively correlated with female fertility phenotypes. In BB, across-sex, bulls’ blood concentration of IGF1 measured at 6 months (IGF1b) was a good indicator trait for the following four female traits: AGECL, the first postpartum anoestrus interval, age at first calving and DC1. In TC, IGF1b and percentage normal sperm were good predictors of female fertility phenotypes. Conclusions The heritability estimates and genomic correlations from the present study generally support and confirmed the earlier estimates from pedigree analyses. The findings suggest that selection for female fertility traits will benefit male fertility, and vice versa. Implications Heritability estimates and genomic correlations suggest that we can select for fertility traits measured early in life, with benefits within and across sex. Using traits available through veterinary assessment of bull fertility as selection indicators will enhance bull and cow fertility, which can lead to better breeding rates in tropical herds.

Improved Genetic Map Identified Major QTLs for Drought Tolerance- and Iron Deficiency Tolerance-Related Traits in Groundnut.

A deep understanding of the genetic control of drought tolerance and iron deficiency tolerance is essential to hasten the process of developing improved varieties with higher tolerance through genomics-assisted breeding. In this context, an improved genetic map with 1205 loci was developed spanning 2598.3 cM with an average 2.2 cM distance between loci in the recombinant inbred line (TAG 24 × ICGV 86031) population using high-density 58K single nucleotide polymorphism (SNP) “Axiom_Arachis” array. Quantitative trait locus (QTL) analysis was performed using extensive phenotyping data generated for 20 drought tolerance- and two iron deficiency tolerance-related traits from eight seasons (2004–2015) at two locations in India, one in Niger, and one in Senegal. The genome-wide QTL discovery analysis identified 19 major main-effect QTLs with 10.0–33.9% phenotypic variation explained (PVE) for drought tolerance- and iron deficiency tolerance- related traits. Major main-effect QTLs were detected for haulm weight (20.1% PVE), SCMR (soil plant analytical development (SPAD) chlorophyll meter reading, 22.4% PVE), and visual chlorosis rate (33.9% PVE). Several important candidate genes encoding glycosyl hydrolases; malate dehydrogenases; microtubule-associated proteins; and transcription factors such as MADS-box, basic helix-loop-helix (bHLH), NAM, ATAF, and CUC (NAC), and myeloblastosis (MYB) were identified underlying these QTL regions. The putative function of these genes indicated their possible involvement in plant growth, development of seed and pod, and photosynthesis under drought or iron deficiency conditions in groundnut. These genomic regions and candidate genes, after validation, may be useful to develop molecular markers for deploying genomics-assisted breeding for enhancing groundnut yield under drought stress and iron-deficient soil conditions.

Genome-wide pedigree analysis of elite rice Shuhui 527 reveals key regions for breeding

Hybrid rice significantly contributes to the food supply worldwide. Backbone parents play important roles in elite hybrid rice breeding systems. In this study, we performed pedigree-based analysis of the elite backbone parent rice variety, namely, Shuhui 527 (SH527, Oryza sativa), to exploit key genome regions during breeding. Twenty-four cultivars (including SH527, its six progenitors and 17 derived cultivars) were collected and analyzed with high-density single nucleotide polymorphism (SNP) array. Scanning all these cultivars with genome-wide SNP data indicated the unique contributions of progenitors to the SH527 genome and identified the key genomic regions of SH527 conserved within all its derivatives. These findings were further supported by known rice yield-related genes or unknown QTLs identified by genome-wide association study. This study reveals several key regions for SH527 and provides insights into hybrid rice breeding.

Genome-wide association studies for mottled eggs in chickens using a high-density single-nucleotide polymorphism array

Mottled eggs in layer chickens are gaining increasing attention because of the economic impact on the egg industry caused by the reduced sale value of commodity eggs. However, the genetic architecture underlying mottled eggs is not well understood. The genetic architecture underlying the mottled egg trait was investigated using genome-wide association studies (GWAS) by high-density arrays, using a total of 407 pink eggs and 799 blue eggs from an F2 resource population generated by crossing Dongxiang Blue-shelled and White Leghorn chickens. The mottled egg score in blue eggs was found to be higher than that in pink eggs. The single-nucleotide polymorphism heritability of mottled egg at laying day and storage for 7 days was 0.18 and 0.20, respectively. Bivariate GWAS provided 29 significant loci, mainly located on GGA2, GGA3, GGA8, GGA10, GGA15, GGA17, and GGA23, affecting mottled egg on laying day. Candidate genes RIMS2, SLC25A32, RIMBP2, VPS13B, and RGS3 were obtained for mottled eggshell by bivariate GWAS and gene annotation. Our findings provide new insights into the genetic architecture of mottled egg in hens, and demonstrate that a genomic selection method would be profitable for breeding out the mottled egg trait.

Genome-wide identification of major genes and genomic prediction using high-density and text-mined gene-based SNP panels in Hanwoo (Korean cattle).

It was hypothesized that single-nucleotide polymorphisms (SNPs) extracted from text-mined genes could be more tightly related to causal variant for each trait and that differentially weighting of this SNP panel in the GBLUP model could improve the performance of genomic prediction in cattle. Fitting two GRMs constructed by text-mined SNPs and SNPs except text-mined SNPs from 777k SNPs set (exp_777K) as different random effects showed better accuracy than fitting one GRM (Im_777K) for six traits (e.g. backfat thickness: + 0.002, eye muscle area: + 0.014, Warner-Bratzler Shear Force of semimembranosus and longissimus dorsi: + 0.024 and + 0.068, intramuscular fat content of semimembranosus and longissimus dorsi: + 0.008 and + 0.018). These results can suggest that attempts to incorporate text mining into genomic predictions seem valuable, and further study using text mining can be expected to present the significant results.

Construction of a high-density genetic map using genotyping by sequencing (GBS) for quantitative trait loci (QTL) analysis of pink petal trait in <italic>Brassica napus </italic>L.

<p indent=0mm>Petal color is an important ornamental trait in <italic>B</italic>.<italic> napus</italic>, and the breeding and improvement of petal color have become the main goal in breeding and genetic research. To date, the research about interval location of pink petal trait in <italic>B</italic>.<italic> napus</italic> is very less yet. In this study, the genetic basis of petal phenotype was examined in the 62 (yellow petal) and 77 (pink petal) parents as well as 114 individuals comprising the doubled haploid (DH) mapping population. This DH population was examined using genotyping by sequencing (GBS) with 6065 high-density polymorphism single nucleotide polymorphism (SNP) markers to construct a genetic linkage map comprised of 3253 polymorphic markers. The genetic map spanned 1766.06 cM, with an average distance of 0.54 cM between markers. The complete interval mapping method identified two quantitative trait loci (QTL) for petal color located on chromosomes A07 and C03, respectively. Synteny analysis showed that some homologous genes in the interval of <italic>B</italic>.<italic> napus</italic> were located in <italic>Brassica rapa</italic> and <italic>Brassica oleracea</italic>. Also, eight genes related to flower color were analyzed between inbred line 77 and inbred line 62, the splice junctions of <italic>BnaA07g15980D</italic> and <italic>BnaA07g17500D</italic> were belong to intron retention type in pink petal of 77 parent. This study lays a foundation for further research on fine mapping of pink petal trait and molecular marker-assisted selection in <italic>Brassica napus</italic> L.

Genome-wide characterisation of regions under intense selection based on runs of homozygosity in Charolais cattle

Submitted 2020-06-22 | Accepted 2020-07-20 | Available 2020-12-01 https://doi.org/10.15414/afz.2020.23.mi-fpap.347-352 In this study, 68 genotyped purebred cows and bulls of Charolais cattle were used to determine runs of homozygosity (ROH) for evaluation of selection effect on the genome structure. ROH analysis was performed for 41,153 SNPs, and homozygous segments considering a minimum of 15 consecutive SNPs. The aim was to identify if regions of the genome with a high frequency of SNPs in ROH include signatures of selection. The most significant outlier SNPs were found on BTA2, 5, 7 and 19 (11 regions), with a sum of ROH length of 15.85 Mb. These regions contained genes included in various biological processes associated with the functioning of the immune system, growth, reproduction and metabolism. Various quantitative traits loci (QTLs) which affected the characteristics of meat production and reproduction have been identified in these regions. Overall, obtained results suggest that the Charolais cattle genome includes selection signatures reflecting the improvement of meat production and reproduction in accordance to breeding objecives. Keywords: artificial selection, autozygosity, beef, bovine, candidate genes References Aspatwar, A. et al. (2014). Carbonic anhydrase related proteins: molecular biology and evolution. Subcell. Biochem. 75: 135–156. Biscarini, F. et al. (2019). detectRUNS: an R package to detect runs of homozygosity and heterozygosity in diploid genomes. The R Project. Retrieved June 8, 2019 from https://cran.r-project.org/web/packages/detectRUNS/vignettes/detectRUNS.vignette.html Dixit, S. P. et al. (2020). Genome-Wide Runs of Homozygosity Revealed Selection Signatures in Bos indicus. Front. Genet. https://doi.org/10.3389/fgene.2020.00092 Gaudet, P. et al. (2011). Phylogenetic-based Propagation of Functional Annotations Within the Gene Ontology Consortium. Brief Bioinform, 12(5): 446-62. https://doi.org/10.1093/bib/bbr042 Chang, C.C. (2015). Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience, 4(1). https://doi.org/10.1186/s13742-015-0047-8 Jahuey-Martinez, F. L. et al. (2019). Signatures of selection in Charolais beef cattle identified by genome‐wide analysis. Journal of Animal Breeding and Genetics, 136(5). https://doi.org/10.1111/jbg.12399 Mallikarjunappa, S. et al. (2018) Short communication: Uncovering quantitative trait loci associated with resistance to Mycobacterium avium ssp. paratuberculosis infection in Holstein cattle using a high-density single nucleotide polymorphism panel, Journal of Dairy Science, 101(8):7280-7286. https://doi.org/10.3168/jds.2018-14388 Marras, G. et al. (2015). Analysis of runs of homozygosity and their relationship with inbreeding in five cattle breeds farmed in Italy. Anim. Genet., 46, 110–121. https://doi.org/10.1111/age.12259 Mastrangelo, S. (2020). Genome‐wide detection of signatures of selection in three Valdostana cattle populations. Journal of Animal Breeding and Genetics. https://doi.org/10.1111/jbg.12476 Mastrangelo, S. et al. (2019). Novel and known signals of selection for fat deposition in domestic sheep breeds from Africa and Eurasia. In PLoS ONE 14(6): e0209632. https://doi.org/10.1371/journal.pone.0209632 McTavish E.J. (2013). New world cattle show ancestry from multiple independent domestication events. PNAS, 110(15): 1398–1406. https://doi.org/10.1073/pnas.1303367110 Meszaros, G. et al. (2015) Genomic analysis for managing small and endangered populations: A case study in Tyrol Grey cattle. Front. Genet, 13(6):173. https://doi.org/10.3389/fgene.2015.00173 Moravcikova, N. et al. (2019). Analysis of selection signatures in the beef cattle genome. Czech Journal of Animal Science, 64(12): 491–503. https://doi.org/10.17221/226/2019-CJAS Moravcikova, N. et al.(2018). Genomic Response to Natural Selection within Alpine Cattle Breeds. Czech J. Anim. Sci., 63(4):136-143. https://doi.org/10.17221/62/2017-CJAS Onzima, R. B. et al. (2018). Genome-Wide Characterisation of Selection Signatures and Runs of Homozygosity in Ugandan Goat Breeds. Front. Genet. https://doi.org/10.3389/fgene.2018.00318 Patel, A. B. et al. (2017). Identification of single nucleotide polymorphism from Indian Bubalus bubalis through targeted sequence capture. Current Science, 112(6): 1230. ISSN 0011-3891 Pomichal. (2009) The breeding program of Charolais cattle. The Slovak Beef Breeders Association, cooperative. Retrieved June 8, 2019 from http://www.zchmd.eu/charolais Purfield, D.C. et al. (2012). Runs of homozygosity and population history in cattle. BMC Genet., 13, 70. https://doi.org/10.1186/1471-2156-13-70 Purfield, D.C. et al. (2017). The Distribution of Runs of Homozygosity and Selection Signatures in Six Commercial Meat Sheep Breeds. In PLoS ONE 12(5): e0176780. https://doi.org/10.1371/journal.pone.0176780 Shi, L. et al. (2020). Estimation of inbreeding and identification of regions under heavy selection based on runs of homozygosity in a Large White pig population. Journal of Animal Science and Biotechnology, 11(46). https://doi.org/10.1186/s40104-020-00447-0 Szmatola, T. et al. (2019). A Comprehensive Analysis of Runs of Homozygosity of Eleven Cattle Breeds Representing Different Production Types. Animals, 9(12), https://doi.org/10.3390/ani9121024 Szmatola, T. et al. (2020). Detection of runs of homozygosity in conserved and commercial pig breeds in Poland. Journal of Animal Breeding and Genetics. https://doi.org/10.1111/jbg.12482 The Breeding Services of the Slovak Republic, s.e. (2019). The results of beef and suckling cows performance recording in Slovak Republic, Year 2019. The Breeding Services of the Slovak Republic, s.e. Retrieved June 8, 2020 from http://test.plis.sk/volne/rocenkamagazin/rocenka.aspx?id=kump2019 Trukhachev, V. et al. (2015). Myostatin gene (MSTN) polymorphism with a negative effect on meat productivity in Dzhalginsky Merino sheep breed. In J.BioSci. Biotechnol. 4(2):191-199

Predicted Feed Efficiency index applied to Italian Holstein Friesian cattle population

Submitted 2020-08-02 | Accepted 2020-09-21 | Available 2020-12-01 https://doi.org/10.15414/afz.2020.23.mi-fpap.326-330 Feed efficiency has a major influence on farm profitability and environmental stewardship in the dairy industry. The aim of this study was to describe a new selection index adopted by the Italian Holstein and Jersey Association (ANAFIJ, Cremona, Italy) to improve feed efficiency using data recorded by the official dairy recording system. Predicted dry matter intake (pDMI) was derived from milk yield, fat content, and estimated cow body weight. Fat-protein corrected milk (FPCM) was derived from milk yield corrected for fat, protein, and a fixed coefficient for lactose content (4.80%). Therefore, the predicted feed efficiency (pFE) was estimated as ratio between FPCM and pDMI. Average pFE was 1.27±0.18 (kg.d -1 ) with heritability of 0.32. Predicted Feed Efficiency index (pFEi), traditional and genomic, has been implemented in the Italian Holstein Friesian evaluation system. Results suggest that pFEi may be a new breeding objective for Italian Friesians. The official selection index (PFT), in use since 2002, is positively correlated with pFEi. However, the introduction of pFEi will improve the positive feed efficiency trend. This approach will permit the Italian Holstein Friesian breeders to improve feed efficiency, without increasing costs of recording system. However, to avoid the risk of selecting animals with an excessive negative energy balance after calving, it would be useful to include in the pFE a correction for body condition score and reproductive performances. Meanwhile, in order to increase the accuracy of the predicted phenotype, an Italian consortium is creating a consistent phenotypic critical mass of individual data for dry matter intake in cows, heifers and young bulls. Keywords: feed efficiency, cattle breeding, dry matter intake, breeding value estimation References Cassandro, M. (2020). Animal breeding and climate change, mitigation and adaptation. Journal of Animal Breeding and Genetics, 137(2), 121-122. https://doi.org/10.1111/jbg.12469 Cassandro, M. et al. (2010). Genetic parameters of predicted methane production in Holstein Friesian cows. Proceedings of the 9th World Congress on Genetics Applied to Livestock Production, Leipzig, Germany. Cassandro, M., Mele, M. and Stefanon, B. (2013). Genetic aspects of enteric methane emission in livestock ruminants. Italian Journal of Animal Science, 12(3), 450-458. De Haas, Y. et al. (2012). Improved accuracy of genomic prediction for dry matter intake of dairy cattle from combined European and Australian data sets. Journal of Dairy Science, 95(10), 6103-6112. De Vries, M. and Veerkamp, R. (2000). Energy balance of dairy cattle in relation to milk production variables and fertility. Journal of Dairy Science, 83(1), 62-69. Finocchiaro, R. et al. (2017). Body weight prediction in Italian Holstein cows. ICAR Technical Series, 22, 95-98. Hegarty, R. et al. (2007). Cattle selected for lower residual feed intake have reduced daily methane production. Journal of Animal Science, 85(6), 1479-1486. Hurley, A. M. et al. (2018). Characteristics of feed efficiency within and across lactation in dairy cows and the effect of genetic selection. Journal of Dairy Science, 101(2), 1267-1280. https://doi.org/https://doi.org/10.3168/jds.2017-12841 Meuwissen, T. H., Hayes, B. J. and Goddard, M. E. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics, 157(4), 1819-1829. National Research Council. (2001). Nutrient Requirements of Dairy Cattle: Seventh Revised Edition, 2001. The National Academies Press. http://www.nap.edu/catalog.php?record_id=9825 Pryce, J. et al. (2014). Genomic selection for feed efficiency in dairy cattle. Animal, 8(1), 1-10. Sjaunja, L. et al. (1990). A Nordic proposal for an energy corrected milk formula. Proceedings of the 2nd Session of Committee for Recording and Productivity of Milk Animal, Paris, p. 156. Veerkamp, R. et al. (2000). Genetic correlation between days until start of luteal activity and milk yield, energy balance, and live weights. Journal of Dairy Science, 83(3), 577-583. Verbyla, K. et al. (2010). Predicting energy balance for dairy cows using high-density single nucleotide polymorphism information. Journal of Dairy Science, 93(6), 2757-2764. Wall, E., Coffey, M. and Brotherstone, S. (2007). The relationship between body energy traits and production and fitness traits in first-lactation dairy cows. Journal of Dairy Science, 90(3), 1527-1537. Wallace, R. J. et al. (2019). A heritable subset of the core rumen microbiome dictates dairy cow productivity and emissions. Science Advances, 5(7), eaav8391.

Evaluation of maize root growth and genome-wide association studies of root traits in response to low nitrogen supply at seedling emergence

Nitrogen (N) deficiency is one of the main factors limiting maize (Zea mays L.) productivity. Genetic improvement of root traits could improve nitrogen use efficiency. An association panel of 461 maize inbred lines was assayed for root growth at seedling emergence under high-nitrate (HN, 5 mmol L−1) and low-nitrate (LN, 0.05 mmol L−1) conditions. Twenty-one root traits and three shoot traits were measured. Under LN conditions, the root-to-shoot ratio, root dry weight, total root length, axial root length, and lateral root length on the primary root were all increased. Under LN conditions, the heritability of the plant traits ranged from 0.43 to 0.82, a range much wider than that of 0.27 to 0.55 observed under HN conditions. The panel was genotyped with 542,796 high-density single-nucleotide polymorphism (SNP) markers. Totally 328 significant SNP markers were identified using either mixed linear model (MLM) or general linear model analysis, with 34 detected by both methods. In the 100-kb intervals flanking these SNP markers, four candidate genes were identified. Under LN conditions, the protoporphyrinogen IX oxidase 2 gene was associated with total root surface area and the DELLA protein-encoding gene was associated with the length of the visible lateral root zone of the primary root. Under HN conditions, a histone deacetylase gene was associated with plant height. Under both LN and HN conditions, the gene encoding MA3 domain-containing protein was associated with the first whorl crown root number. The phenotypic and genetic information from this study may be exploited for genetic improvement of root traits aimed at increasing NUE in maize.

Estimation of Variance Components and Genomic Prediction for Individual Birth Weight Using Three Different Genome-Wide SNP Platforms in Yorkshire Pigs.

Simple SummaryThe individual birth weight (IBW) of pigs is an important trait regarding its relevance to mortality at weaning, sow prolificacy, and growth performance. This study investigates the variance component estimation, informative window regions, and the efficiency of genomic predictions associated with IBW traits in Yorkshire pigs. The low heritability (0.13) is estimated on the basis of a full model including individual genetic, sow genetic, and common environmental effects. Two common window regions of the genome are identified under three different genotyping platforms found within the ARAP2 and TSN genes concerning the IBW trait. The genomic prediction ability is improved using deregressed estimated breeding values including parental information as a response variable despite Bayesian methods and genotyping platforms for the IBW trait in Korean Yorkshire pigs.This study estimates the individual birth weight (IBW) trait heritability and investigates the genomic prediction efficiency using three types of high-density single nucleotide polymorphism (SNP) genotyping panels in Korean Yorkshire pigs. We use 38,864 IBW phenotypic records to identify a suitable model for statistical genetics, where 698 genotypes match our phenotypic records. During our genomic analysis, the deregressed estimated breeding values (DEBVs) and their reliabilities are used as derived response variables from the estimated breeding values (EBVs). Bayesian methods identify the informative regions and perform the genomic prediction using the IBW trait, in which two common significant window regions (SSC8 27 Mb and SSC15 29 Mb) are identified using the three genotyping platforms. Higher prediction ability is observed using the DEBV-including parent average as a response variable, regardless of the SNP genotyping panels and the Bayesian methods, relative to the DEBV-excluding parent average. Hence, we suggest that fine-mapping studies targeting the identified informative regions in this study are necessary to find the causal mutations to improve the IBW trait’s prediction ability. Furthermore, studying the IBW trait using a genomic prediction model with a larger genomic dataset may improve the genomic prediction accuracy in Korean Yorkshire pigs.

Selection signatures in two oldest Russian native cattle breeds revealed using high-density single nucleotide polymorphism analysis.

Native cattle breeds can carry specific signatures of selection reflecting their adaptation to the local environmental conditions and response to the breeding strategy used. In this study, we comprehensively analysed high-density single nucleotide polymorphism (SNP) genotypes to characterise the population structure and detect the selection signatures in Russian native Yaroslavl and Kholmogor dairy cattle breeds, which have been little influenced by introgression with transboundary breeds. Fifty-six samples of pedigree-recorded purebred animals, originating from different breeding farms and representing different sire lines, of the two studied breeds were genotyped using a genome-wide bovine genotyping array (Bovine HD BeadChip). Three statistical analyses-calculation of fixation index (FST) for each SNP for the comparison of the pairs of breeds, hapFLK analysis, and estimation of the runs of homozygosity (ROH) islands shared in more than 50% of animals-were combined for detecting the selection signatures in the genome of the studied cattle breeds. We confirmed nine and six known regions under putative selection in the genomes of Yaroslavl and Kholmogor cattle, respectively; the flanking positions of most of these regions were elucidated. Only two of the selected regions (localised on BTA 14 at 24.4-25.1 Mbp and on BTA 16 at 42.5-43.5 Mb) overlapped in Yaroslavl, Kholmogor and Holstein breeds. In addition, we detected three novel selection sweeps in the genome of Yaroslavl (BTA 4 at 4.74-5.36 Mbp, BTA 15 at 17.80-18.77 Mbp, and BTA 17 at 45.59-45.61 Mbp) and Kholmogor breeds (BTA 12 at 82.40-81.69 Mbp, BTA 15 at 16.04-16.62 Mbp, and BTA 18 at 0.19-1.46 Mbp) by using at least two of the above-mentioned methods. We expanded the list of candidate genes associated with the selected genomic regions and performed their functional annotation. We discussed the possible involvement of the identified candidate genes in artificial selection in connection with the origin and development of the breeds. Our findings on the Yaroslavl and Kholmogor breeds obtained using high-density SNP genotyping and three different statistical methods allowed the detection of novel putative genomic regions and candidate genes that might be under selection. These results might be useful for the sustainable development and conservation of these two oldest Russian native cattle breeds.

Genome sequence of Hydrangea macrophylla and its application in analysis of the double flower phenotype.

Owing to its high ornamental value, the double flower phenotype of hydrangea (Hydrangea macrophylla) is one of its most important traits. In this study, genome sequence information was obtained to explore effective DNA markers and the causative genes for double flower production in hydrangea. Single-molecule real-time sequencing data followed by a Hi-C analysis were employed. Two haplotype-phased sequences were obtained from the heterozygous genome of hydrangea. One assembly consisted of 3,779 scaffolds (2.256 Gb in length and N50 of 1.5 Mb), the other also contained 3,779 scaffolds (2.227 Gb in length, and N50 of 1.4 Mb). A total of 36,930 genes were predicted in the sequences, of which 32,205 and 32,222 were found in each haplotype. A pair of 18 pseudomolecules was constructed along with a high-density single-nucleotide polymorphism (SNP) genetic linkage map. Using the genome sequence data, and two F2 populations, the SNPs linked to double flower loci (djo and dsu) were discovered. DNA markers linked to djo and dsu were developed, and these could distinguish the recessive double flower allele for each locus, respectively. The LEAFY gene is a very likely candidate as the causative gene for dsu, since frameshift was specifically observed in the double flower accession with dsu.

Genome-Wide Association Studies Reveal Susceptibility Loci for Digital Dermatitis in Holstein Cattle.

Simple SummaryFoot warts (FW), a leading cause of foot problems in dairy cattle, is a welfare concern and causes financial losses due to treatment and reduced milk production. Foot warts, or the technically correct term of digital dermatitis, result from a bacterial infection followed by delayed healing due to both genetic and environmental factors. Dairy farmers are already combatting FW through environmental control, but they do not have genetic selection tools because the genetics influencing FW susceptibility are largely unknown. We sought to identify the genetics associated with FW which can be incorporated into genetic selection tools. Farmers can then use these genetic selection tools to breed cows that are less susceptible to FW. We identified promising genes that play a role in the immune response and wound healing—immune functions that, if impaired, could increase a cow’s susceptibility to FW. Though these genes were promising, their associated genetic markers had very little influence on FW susceptibility when compared to environmental management. Thus, the findings imply that the best approach for reducing FW prevalence is likely through combining a genetics approach with environmental management.Digital dermatitis (DD) causes lameness in dairy cattle. To detect the quantitative trait loci (QTL) associated with DD, genome-wide association studies (GWAS) were performed using high-density single nucleotide polymorphism (SNP) genotypes and binary case/control, quantitative (average number of FW per hoof trimming record) and recurrent (cases with ≥2 DD episodes vs. controls) phenotypes from cows across four dairies (controls n = 129 vs. FW n = 85). Linear mixed model (LMM) and random forest (RF) approaches identified the top SNPs, which were used as predictors in Bayesian regression models to assess the SNP predictive value. The LMM and RF analyses identified QTL regions containing candidate genes on Bos taurus autosome (BTA) 2 for the binary and recurrent phenotypes and BTA7 and 20 for the quantitative phenotype that related to epidermal integrity, immune function, and wound healing. Although larger sample sizes are necessary to reaffirm these small effect loci amidst a strong environmental effect, the sample cohort used in this study was sufficient for estimating SNP effects with a high predictive value.

Development and Evaluation of a High-Throughput Single-Nucleotide Polymorphism Array for Large Yellow Croaker (Larimichthys crocea).

High-density single-nucleotide polymorphism (SNP) genotyping array is an essential tool for genetic analyses of animals and plants. Large yellow croaker (Larimichthys crocea) is one of the most commercially important marine fish species in China. Although plenty of SNPs have been identified in large yellow croaker, no high-throughput genotyping array is available. In this study, a high-throughput SNP array named NingXin-I with 600K SNPs was developed and evaluated. A set of 82 large yellow croakers were collected from different locations of China and re-sequenced. A total of 9.34M SNPs were identified by mapping sequence reads to the large yellow croaker reference genome. About 1.98M candidate SNPs were selected for further analyses by using criteria such as SNP quality score and conversion performance in the final array. Finally, 579.5K SNPs evenly distributed across the large yellow croaker genome with an average spacing of 1.19 kb were proceeded to array production. The performance of NingXin-I array was evaluated in 96 large yellow croaker individuals from five populations, and 83.38% SNPs on the array were polymorphic sites. A further test of the NingXin-I array in five closely related species in Sciaenidae identified 26.68–56.23% polymorphic SNP rate across species. A phylogenetic tree inferred by using the genotype data generated by NingXin-I confirmed the phylogenetic distance of the species in Sciaenidae. The performance of NingXin-I in large yellow croaker and the other species in Sciaenidae suggested high accuracy and broad application. The NingXin-I array should be valuable for quantitative genetic studies, such as genome-wide association studies (GWASs), high-density linkage map construction, haplotype analysis, and genome-based selection.

High-density single nucleotide polymorphism chip-based conservation genetic analysis of indigenous pig breeds from Shandong Province, China.

ObjectiveShandong indigenous pig breeds are important Chinese pig resources. Their progressive population decline in recent decades has attracted attention towards their conservation. Conservation genetics of these indigenous breeds are essential for developing a conservation and utilization scheme.MethodsA high-density single nucleotide polymorphism (HD-SNP) chip-based comparative analysis of genetic characteristics was performed for seven Shandong indigenous pig breeds in the context of five Western commercial breeds.ResultsThe results showed that Shandong indigenous pig breeds varied greatly in genetic diversity, effective population size, inbreeding level, and genetic distance with the Western commercial breeds. Specifically, Laiwu and Dapulian displayed low genetic diversity, and had a genetically distant relationship with the Western commercial breeds (average F statistics [FST] value of 0.3226 and 0.2666, respectively). Contrastingly, the other five breeds (Yantai, Licha, Yimeng, Wulain, and Heigai) displayed high genetic diversity within breed and had some extent of mixture pattern with the Western commercial breeds, especially Duroc and Landrace (FST values from 0.1043 to 0.2536). Furthermore, intensive gene flow was discovered among the seven Shandong indigenous breeds, particularly Wulian, Licha, and Heigai, as indicated by the large cluster formed in the principal component analysis scatterplot and small population differentiation (average of 0.1253) among them.ConclusionOur study advances the understanding of genetic characteristics of Shandong indigenous breeds and provides essential information for developing an appropriate conservation and utilization scheme for these breeds.

Accuracies of Genomic Prediction for Growth Traits at Weaning and Yearling Ages in Yak.

Simple SummaryGenomic selection is a new technology in animal breeding after the selection according to the best linear unbiased prediction (BLUP) value and marker assisted selection. Genomic selection has gradually been used in practical applications over recent years following the advent of high-density single nucleotide polymorphism (SNP) chips for livestock and poultry. Yak are a critical species on the Qinghai–Tibet Plateau, which is of great significance to herders. The early selection of yak could save feeding costs and shorten the generation interval. In the present study, we estimated the accuracy of genomic prediction compared with different classical models for yak early growth traits. The results of cross-validation indicated that the average predictive accuracy ranged from 0.147 to 0.391. The average correlation coefficient between prediction and true phenotype was 0.4.Genomic selection is a promising breeding strategy that has been used in considerable numbers of breeding projects due to its highly accurate results. Yak are rare mammals that are remarkable because of their ability to survive in the extreme and harsh conditions predominantly at the so-called “roof of the world”—the Qinghai–Tibetan Plateau. In the current study, we conducted an exploration of the feasibility of genomic evaluation and compared the predictive accuracy of early growth traits with five different approaches. In total, four growth traits were measured in 354 yaks, including body weight, withers height, body length, and chest girth in two early stages of development (weaning and yearling). Genotyping was implemented using the Illumina BovineHD BeadChip. The predictive accuracy was calculated through five-fold cross-validation in five classical statistical methods including genomic best linear unbiased prediction (GBLUP) and four Bayesian methods. Body weights at 30 months in the same yak population were also measured to evaluate the prediction at 6 months. The results indicated that the predictive accuracy for the early growth traits of yak ranged from 0.147 to 0.391. Similar performance was found for the GBLUP and Bayesian methods for most growth traits. Among the Bayesian methods, Bayes B outperformed Bayes A in the majority of traits. The average correlation coefficient between the prediction at 6 months using different methods and observations at 30 months was 0.4. These results indicate that genomic prediction is feasible for early growth traits in yak. Considering that genomic selection is necessary in yak breeding projects, the present study provides promising reference for future applications.

Phenotypic variability and population structure analysis of Tanzanian free-range local chickens

BackgroundFree-range local chickens (FRLC) farming is an important activity in Tanzania, however, they have not been well-characterized. This study aimed to phenotypically characterize three Tanzanian FRLCs and to determine their population structure. A total of 389 mature breeder chickens (324 females and 65 males) from three popular Tanzanian FRLC ecotypes (Kuchi, Morogoro-medium and Ching’wekwe) were used for the phenotypic characterization. Progenies of these chickens were utilized to assess population structure. The ecotypes were collected from four geographical zones across Tanzania: Lake, Central, Northern and Coastal zones. Body weights and linear measurements were obtained from the mature breeders, including body, neck, shanks, wingspan, chest girth, and shank girth. Descriptive statistics were utilized to characterize the chickens. Correlations between the linear measurements and differences among the means of measured linear traits between ecotypes and between sexes were assessed. A total of 1399 progeny chicks were genotyped using a chicken 600 K high density single nucleotide polymorphism (SNP) panel for determination of population structure.ResultsThe means for most traits were significantly higher in Kuchi relative to Ching’wekwe and Morogoro-medium. However, shank length and shank girth were similar between Kuchi and Morogoro-medium females. All traits were correlated with the exception of shank girth in Morogoro-medium. Admixture analyses revealed that Morogoro-medium and Ching’wekwe clustered together as one population, separate from Kuchi.ConclusionsPhenotypic traits could be used to characterize FRLCs, however, there were variations in traits among individuals within ecotypes; therefore, complementary genomic methods should be considered to improve the characterization for selective breeding.

Construction of a high-density SNP-based genetic map and identification of fruit-related QTLs and candidate genes in peach [Prunus persica (L.) Batsch

BackgroundHigh-density genetic mapping is a valuable tool for mapping loci that control specific traits for perennial fruit trees. Peach is an economically important fruit tree and a model Rosaceae species for genomic and genetic research. In peach, even though many molecular markers, genetic maps and QTL mappings have been reported, further research on the improvement of marker numbers, map densities, QTL accuracy and candidate gene identification is still warranted.ResultsA high-density single nucleotide polymorphism (SNP)-based peach linkage map was constructed using specific locus amplified fragment sequencing (SLAF-seq). This genetic map consisted of 7998 SLAF markers, spanning 1098.79 cM with an average distance of 0.17 cM between adjacent markers. A total of 40 QTLs and 885 annotated candidate genes were detected for 10 fruit-related traits, including fruit weight (FW), fruit diameter (FD), percentage of red skin colour (PSC), eating quality (EQ), fruit flavour (FV), red in flesh (RF), red around pit (RP), adherence to pit (AP), fruit development period (FDP) and fruit fibre content (FFC). Eighteen QTLs for soluble solid content (SSC) were identified along LGs 1, 4, 5, and 6 in 2015 and 2016, and 540 genes were annotated in QTL intervals. Thirty-two QTLs for fruit acidity content (FA) were detected on LG1, and 2, 4, 5, 6, and 1232 candidate genes were identified. The expression profiles of 2 candidate genes for SSC and 4 for FA were analysed in parents and their offspring.ConclusionsWe constructed a high-density genetic map in peach based on SLAF-seq, which may contribute to the identification of important agronomic trait loci. Ninety QTLs for 12 fruit-related traits were identified, most of which overlapped with previous reports, and some new QTLs were obtained. A large number of candidate genes for fruit-related traits were screened and identified. These results may improve our understanding of the genetic control of fruit quality traits and provide useful information in marker-assisted selection for fruit quality in peach breeding programmes.