Impact of index hopping and bias towards the reference allele on accuracy of genotype calls from low-coverage sequencing

Abstract Background Inherent sources of error and bias that affect the quality of sequence data include index hopping and bias towards the reference allele. The impact of these artefacts is likely greater for low-coverage data than for high-coverage data because low-coverage data has scant information and many standard tools for processing sequence data were designed for high-coverage data. With the proliferation of cost-effective low-coverage sequencing, there is a need to understand the impact of these errors and bias on resulting genotype calls from low-coverage sequencing. Results We used a dataset of 26 pigs sequenced both at 2× with multiplexing and at 30× without multiplexing to show that index hopping and bias towards the reference allele due to alignment had little impact on genotype calls. However, pruning of alternative haplotypes supported by a number of reads below a predefined threshold, which is a default and desired step of some variant callers for removing potential sequencing errors in high-coverage data, introduced an unexpected bias towards the reference allele when applied to low-coverage sequence data. This bias reduced best-guess genotype concordance of low-coverage sequence data by 19.0 absolute percentage points. Conclusions We propose a simple pipeline to correct the preferential bias towards the reference allele that can occur during variant discovery and we recommend that users of low-coverage sequence data be wary of unexpected biases that may be produced by bioinformatic tools that were designed for high-coverage sequence data

Genetic architecture and major genes for backfat thickness in pig lines of diverse genetic backgrounds

Background Backfat thickness is an important carcass composition trait for pork production and is commonly included in swine breeding programmes. In this paper, we report the results of a large genome-wide association study for backfat thickness using data from eight lines of diverse genetic backgrounds. Methods Data comprised 275,590 pigs from eight lines with diverse genetic backgrounds (breeds included Large White, Landrace, Pietrain, Hampshire, Duroc, and synthetic lines) genotyped and imputed for 71,324 single-nucleotide polymorphisms (SNPs). For each line, we estimated SNP associations using a univariate linear mixed model that accounted for genomic relationships. SNPs with significant associations were identified using a threshold of p < 10(-6) and used to define genomic regions of interest. The proportion of genetic variance explained by a genomic region was estimated using a ridge regression model. Results We found significant associations with backfat thickness for 264 SNPs across 27 genomic regions. Six genomic regions were detected in three or more lines. The average estimate of the SNP-based heritability was 0.48, with estimates by line ranging from 0.30 to 0.58. The genomic regions jointly explained from 3.2 to 19.5% of the additive genetic variance of backfat thickness within a line. Individual genomic regions explained up to 8.0% of the additive genetic variance of backfat thickness within a line. Some of these 27 genomic regions also explained up to 1.6% of the additive genetic variance in lines for which the genomic region was not statistically significant. We identified 64 candidate genes with annotated functions that can be related to fat metabolism, including well-studied genes such as MC4R, IGF2, and LEPR, and more novel candidate genes such as DHCR7, FGF23, MEDAG, DGKI, and PTN. Conclusions Our results confirm the polygenic architecture of backfat thickness and the role of genes involved in energy homeostasis, adipogenesis, fatty acid metabolism, and insulin signalling pathways for fat deposition in pigs. The results also suggest that several less well-understood metabolic pathways contribute to backfat development, such as those of phosphate, calcium, and vitamin D homeostasis

Sequence variation, evolutionary constraint, and selection at the CD163 gene in pigs

Abstract Background In this work, we investigated sequence variation, evolutionary constraint, and selection at the CD163 gene in pigs. A functional CD163 protein is required for infection by porcine reproductive and respiratory syndrome virus, which is a serious pathogen with major impacts on pig production. Results We used targeted pooled sequencing of the exons of CD163 to detect sequence variants in 35,000 pigs of diverse genetic backgrounds and to search for potential stop-gain and frameshift indel variants. Then, we used whole-genome sequence data from three pig lines to calculate: a variant intolerance score that measures the tolerance of genes to protein coding variation; an estimate of selection on protein-coding variation over evolutionary time; and haplotype diversity statistics to detect recent selective sweeps during breeding. Conclusions Using a deep survey of sequence variation in the CD163 gene in domestic pigs, we found no potential knockout variants. The CD163 gene was moderately intolerant to variation and showed evidence of positive selection in the pig lineage, but no evidence of recent selective sweeps during breeding

A method for the allocation of sequencing resources in genotyped livestock populations

International audienceAbstractBackgroundThis paper describes a method, called AlphaSeqOpt, for the allocation of sequencing resources in livestock populations with existing phased genomic data to maximise the ability to phase and impute sequenced haplotypes into the whole population.MethodsWe present two algorithms. The first selects focal individuals that collectively represent the maximum possible portion of the haplotype diversity in the population. The second allocates a fixed sequencing budget among the families of focal individuals to enable phasing of their haplotypes at the sequence level. We tested the performance of the two algorithms in simulated pedigrees. For each pedigree, we evaluated the proportion of population haplotypes that are carried by the focal individuals and compared our results to a variant of the widely-used key ancestors approach and to two haplotype-based approaches. We calculated the expected phasing accuracy of the haplotypes of a focal individual at the sequence level given the proportion of the fixed sequencing budget allocated to its family.ResultsAlphaSeqOpt maximises the ability to capture and phase the most frequent haplotypes in a population in three ways. First, it selects focal individuals that collectively represent a larger portion of the population haplotype diversity than existing methods. Second, it selects focal individuals from across the pedigree whose haplotypes can be easily phased using family-based phasing and imputation algorithms, thus maximises the ability to impute sequence into the rest of the population. Third, it allocates more of the fixed sequencing budget to focal individuals whose haplotypes are more frequent in the population than to focal individuals whose haplotypes are less frequent. Unlike existing methods, we additionally present an algorithm to allocate part of the sequencing budget to the families (i.e. immediate ancestors) of focal individuals to ensure that their haplotypes can be phased at the sequence level, which is essential for enabling and maximising subsequent sequence imputation.ConclusionsWe present a new method for the allocation of a fixed sequencing budget to focal individuals and their families such that the final sequenced haplotypes, when phased at the sequence level, represent the maximum possible portion of the haplotype diversity in the population that can be sequenced and phased at that budget

Genome-Wide Association Study Singles Out SCD and LEPR as the Two Main Loci Influencing Intramuscular Fat Content and Fatty Acid Composition in Duroc Pigs

[EN] Intramuscular fat (IMF) content and fatty acid composition affect the organoleptic quality and nutritional value of pork. A genome-wide association study was performed on 138 Duroc pigs genotyped with a 60k SNP chip to detect biologically relevant genomic variants influencing fat content and composition. Despite the limited sample size, the genome-wide association study was powerful enough to detect the association between fatty acid composition and a known haplotypic variant in SCD (SSC14) and to reveal an association of IMF and fatty acid composition in the LEPR region (SSC6). The association of LEPR was later validated with an independent set of 853 pigs using a candidate quantitative trait nucleotide. The SCD gene is responsible for the biosynthesis of oleic acid (C18:1) from stearic acid. This locus affected the stearic to oleic desaturation index (C18:1/C18:0), C18: 1, and saturated (SFA) and monounsaturated (MUFA) fatty acids content. These effects were consistently detected in gluteus medius, longissimus dorsi, and subcutaneous fat. The association of LEPR with fatty acid composition was detected only in muscle and was, at least in part, a consequence of its effect on IMF content, with increased IMF resulting in more SFA, less polyunsaturated fatty acids (PUFA), and greater SFA/PUFA ratio. Marker substitution effects estimated with a subset of 65 animals were used to predict the genomic estimated breeding values of 70 animals born 7 years later. Although predictions with the whole SNP chip information were in relatively high correlation with observed SFA, MUFA, and C18: 1/C18: 0 (0.48-0.60), IMF content and composition were in general better predicted by using only SNPs at the SCD and LEPR loci, in which case the correlation between predicted and observed values was in the range of 0.36 to 0.54 for all traits. Results indicate that markers in the SCD and LEPR genes can be useful to select for optimum fatty acid profiles of pork.This research was funded by the Spanish Ministry of Economy and Competitiveness (MINECO; grants AGL2012-33529 and AGL2015-65846-R).Ros-Freixedes, R.; Gol, S.; Pena, R.; Tor, M.; Ibañez Escriche, N.; Dekkers, J.; Estany, J. (2016). Genome-Wide Association Study Singles Out SCD and LEPR as the Two Main Loci Influencing Intramuscular Fat Content and Fatty Acid Composition in Duroc Pigs. PLoS ONE. 11(3). https://doi.org/10.1371/journal.pone.0152496S113Cameron, N. ., Enser, M., Nute, G. ., Whittington, F. ., Penman, J. ., Fisken, A. ., … Wood, J. . (2000). Genotype with nutrition interaction on fatty acid composition of intramuscular fat and the relationship with flavour of pig meat. Meat Science, 55(2), 187-195. doi:10.1016/s0309-1740(99)00142-4Christophersen, O. A., & Haug, A. (2011). Animal products, diseases and drugs: a plea for better integration between agricultural sciences, human nutrition and human pharmacology. Lipids in Health and Disease, 10(1), 16. doi:10.1186/1476-511x-10-16Ntawubizi, M., Colman, E., Janssens, S., Raes, K., Buys, N., & De Smet, S. (2010). Genetic parameters for intramuscular fatty acid composition and metabolism in pigs1. Journal of Animal Science, 88(4), 1286-1294. doi:10.2527/jas.2009-2355Ros-Freixedes, R., Reixach, J., Tor, M., & Estany, J. (2012). Expected genetic response for oleic acid content in pork1. Journal of Animal Science, 90(12), 4230-4238. doi:10.2527/jas.2011-5063Clop, A., Ovilo, C., Perez-Enciso, M., Cercos, A., Tomas, A., Fernandez, A., … Noguera, J. L. (2003). Detection of QTL affecting fatty acid composition in the pig. Mammalian Genome, 14(9), 650-656. doi:10.1007/s00335-002-2210-7Kim, Y., Kong, M., Nam, Y. J., & Lee, C. (2006). A Quantitative Trait Locus for Oleic Fatty Acid Content on Sus scrofa Chromosome 7. Journal of Heredity, 97(5), 535-537. doi:10.1093/jhered/esl026Sanchez, M.-P., Iannuccelli, N., Basso, B., Bidanel, J.-P., Billon, Y., Gandemer, G., … Le Roy, P. (2007). Identification of QTL with effects on intramuscular fat content and fatty acid composition in a Duroc × Large White cross. BMC Genetics, 8(1), 55. doi:10.1186/1471-2156-8-55Guo, T., Ren, J., Yang, K., Ma, J., Zhang, Z., & Huang, L. (2009). Quantitative trait loci for fatty acid composition in longissimus dorsi and abdominal fat: results from a White Duroc × Erhualian intercross F2population. Animal Genetics, 40(2), 185-191. doi:10.1111/j.1365-2052.2008.01819.xC.M. Dekkers, J. (2012). Application of Genomics Tools to Animal Breeding. Current Genomics, 13(3), 207-212. doi:10.2174/138920212800543057Uemoto, Y., Nakano, H., Kikuchi, T., Sato, S., Ishida, M., Shibata, T., … Suzuki, K. (2011). Fine mapping of porcine SSC14 QTL and SCD gene effects on fatty acid composition and melting point of fat in a Duroc purebred population. Animal Genetics, 43(2), 225-228. doi:10.1111/j.1365-2052.2011.02236.xUemoto, Y., Soma, Y., Sato, S., Ishida, M., Shibata, T., Kadowaki, H., … Suzuki, K. (2011). Genome-wide mapping for fatty acid composition and melting point of fat in a purebred Duroc pig population. Animal Genetics, 43(1), 27-34. doi:10.1111/j.1365-2052.2011.02218.xEstany, J., Ros-Freixedes, R., Tor, M., & Pena, R. N. (2014). A Functional Variant in the Stearoyl-CoA Desaturase Gene Promoter Enhances Fatty Acid Desaturation in Pork. PLoS ONE, 9(1), e86177. doi:10.1371/journal.pone.0086177Ramayo-Caldas, Y., Mercadé, A., Castelló, A., Yang, B., Rodríguez, C., Alves, E., … Folch, J. M. (2012). Genome-wide association study for intramuscular fatty acid composition in an Iberian × Landrace cross1. Journal of Animal Science, 90(9), 2883-2893. doi:10.2527/jas.2011-4900Muñoz, M., Rodríguez, M. C., Alves, E., Folch, J. M., Ibañez-Escriche, N., Silió, L., & Fernández, A. I. (2013). Genome-wide analysis of porcine backfat and intramuscular fat fatty acid composition using high-density genotyping and expression data. BMC Genomics, 14(1), 845. doi:10.1186/1471-2164-14-845Yang, B., Zhang, W., Zhang, Z., Fan, Y., Xie, X., Ai, H., … Ren, J. (2013). Genome-Wide Association Analyses for Fatty Acid Composition in Porcine Muscle and Abdominal Fat Tissues. PLoS ONE, 8(6), e65554. doi:10.1371/journal.pone.0065554Zhang, W., Zhang, J., Cui, L., Ma, J., Chen, C., Ai, H., … Yang, B. (2016). Genetic architecture of fatty acid composition in the longissimus dorsi muscle revealed by genome-wide association studies on diverse pig populations. Genetics Selection Evolution, 48(1). doi:10.1186/s12711-016-0184-2Kim, E.-S., Ros-Freixedes, R., Pena, R. N., Baas, T. J., Estany, J., & Rothschild, M. F. (2015). Identification of signatures of selection for intramuscular fat and backfat thickness in two Duroc populations1. Journal of Animal Science, 93(7), 3292-3302. doi:10.2527/jas.2015-8879Bosch, L., Tor, M., Reixach, J., & Estany, J. (2009). Estimating intramuscular fat content and fatty acid composition in live and post-mortem samples in pigs. Meat Science, 82(4), 432-437. doi:10.1016/j.meatsci.2009.02.013AOAC. 1997. Supplement to AOAC Official Method 996.06: Fat (total, saturated, and monounsaturated) in foods hydrolytic extraction gas chromatographic method. Page 18 in Official Methods of Analysis (16th ed). Association of Official Analytical Chemists, Arlington, VA.ÓVILO, C., FERNÁNDEZ, A., NOGUERA, J. L., BARRAGÁN, C., LETÓN, R., RODRÍGUEZ, C., … TORO, M. (2005). Fine mapping of porcine chromosome 6 QTL and LEPR effects on body composition in multiple generations of an Iberian by Landrace intercross. Genetical Research, 85(1), 57-67. doi:10.1017/s0016672305007330Amills, M., Villalba, D., Tor, M., Mercad, A., Gallardo, D., Cabrera, B., … Estany, J. (2008). Plasma leptin levels in pigs with different leptin and leptin receptor genotypes. Journal of Animal Breeding and Genetics, 125(4), 228-233. doi:10.1111/j.1439-0388.2007.00715.xPurcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., … Sham, P. C. (2007). PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses. The American Journal of Human Genetics, 81(3), 559-575. doi:10.1086/519795Bouwman, A. C., Janss, L. L., & Heuven, H. C. (2011). A Bayesian approach to detect QTL affecting a simulated binary and quantitative trait. BMC Proceedings, 5(S3). doi:10.1186/1753-6561-5-s3-s4Legarra, A., Croiseau, P., Sanchez, M., Teyssèdre, S., Sallé, G., Allais, S., … Elsen, J.-M. (2015). A comparison of methods for whole-genome QTL mapping using dense markers in four livestock species. Genetics Selection Evolution, 47(1), 6. doi:10.1186/s12711-015-0087-7Kass, R. E., & Raftery, A. E. (1995). Bayes Factors. Journal of the American Statistical Association, 90(430), 773-795. doi:10.1080/01621459.1995.10476572Barrett, J. C., Fry, B., Maller, J., & Daly, M. J. (2004). Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics, 21(2), 263-265. doi:10.1093/bioinformatics/bth457Wolc, A., Arango, J., Settar, P., Fulton, J. E., O’Sullivan, N. P., Preisinger, R., … Dekkers, J. C. M. (2012). Genome-wide association analysis and genetic architecture of egg weight and egg uniformity in layer chickens. Animal Genetics, 43, 87-96. doi:10.1111/j.1365-2052.2012.02381.xChen, E. Y., Tan, C. M., Kou, Y., Duan, Q., Wang, Z., Meirelles, G., … Ma’ayan, A. (2013). Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics, 14(1), 128. doi:10.1186/1471-2105-14-128Rabbit programme. 2012. Available from: http://www.dcam.upv.es/dcia/ablasco/Programas/THE%20PROGRAM%20Rabbit.pdfHu, Z.-L., Park, C. A., & Reecy, J. M. (2015). Developmental progress and current status of the Animal QTLdb. Nucleic Acids Research, 44(D1), D827-D833. doi:10.1093/nar/gkv1233Óvilo, C., Fernández, A., Fernández, A. I., Folch, J. M., Varona, L., Benítez, R., … Silió, L. (2010). Hypothalamic expression of porcine leptin receptor (LEPR), neuropeptide Y (NPY), and cocaine- and amphetamine-regulated transcript (CART) genes is influenced by LEPR genotype. Mammalian Genome, 21(11-12), 583-591. doi:10.1007/s00335-010-9307-1Muñoz, G., Alcázar, E., Fernández, A., Barragán, C., Carrasco, A., de Pedro, E., … Rodríguez, M. C. (2011). Effects of porcine MC4R and LEPR polymorphisms, gender and Duroc sire line on economic traits in Duroc×Iberian crossbred pigs. Meat Science, 88(1), 169-173. doi:10.1016/j.meatsci.2010.12.018Galve, A., Burgos, C., Silió, L., Varona, L., Rodríguez, C., Ovilo, C., & López-Buesa, P. (2012). The effects of leptin receptor (LEPR) and melanocortin-4 receptor (MC4R) polymorphisms on fat content, fat distribution and fat composition in a Duroc×Landrace/Large White cross. Livestock Science, 145(1-3), 145-152. doi:10.1016/j.livsci.2012.01.010UEMOTO, Y., KIKUCHI, T., NAKANO, H., SATO, S., SHIBATA, T., KADOWAKI, H., … SUZUKI, K. (2011). Effects of porcine leptin receptor gene polymorphisms on backfat thickness, fat area ratios by image analysis, and serum leptin concentrations in a Duroc purebred population. Animal Science Journal, 83(5), 375-385. doi:10.1111/j.1740-0929.2011.00963.xHirose, K., Ito, T., Fukawa, K., Arakawa, A., Mikawa, S., Hayashi, Y., & Tanaka, K. (2013). Evaluation of effects of multiple candidate genes (LEP,LEPR,MC4R,PIK3C3, andVRTN) on production traits in Duroc pigs. Animal Science Journal, 85(3), 198-206. doi:10.1111/asj.12134López-Buesa, P., Burgos, C., Galve, A., & Varona, L. (2013). Joint analysis of additive, dominant and first-order epistatic effects of four genes (IGF2,MC4R,PRKAG3andLEPR) with known effects on fat content and fat distribution in pigs. Animal Genetics, 45(1), 133-137. doi:10.1111/age.12091Mackowski, M., Szymoniak, K., Szydlowski, M., Kamyczek, M., Eckert, R., Rozycki, M., & Switonski, M. (2005). Missense mutations in exon 4 of the porcine LEPR gene encoding extracellular domain and their association with fatness traits. Animal Genetics, 36(2), 135-137. doi:10.1111/j.1365-2052.2005.01247.xLi, X., Kim, S.-W., Choi, J.-S., Lee, Y.-M., Lee, C.-K., Choi, B.-H., … Kim, K.-S. (2010). Investigation of porcine FABP3 and LEPR gene polymorphisms and mRNA expression for variation in intramuscular fat content. Molecular Biology Reports, 37(8), 3931-3939. doi:10.1007/s11033-010-0050-1Tyra, M., & Ropka-Molik, K. (2011). Effect of the FABP3 and LEPR gene polymorphisms and expression levels on intramuscular fat (IMF) content and fat cover degree in pigs. Livestock Science, 142(1-3), 114-120. doi:10.1016/j.livsci.2011.07.003Muraoka, O., Xu, B., Tsurumaki, T., Akira, S., Yamaguchi, T., & Higuchi, H. (2003). Leptin-induced transactivation of NPY gene promoter mediated by JAK1, JAK2 and STAT3 in the neural cell lines. Neurochemistry International, 42(7), 591-601. doi:10.1016/s0197-0186(02)00160-2Wood, J. D., Enser, M., Fisher, A. V., Nute, G. R., Sheard, P. R., Richardson, R. I., … Whittington, F. M. (2008). Fat deposition, fatty acid composition and meat quality: A review. Meat Science, 78(4), 343-358. doi:10.1016/j.meatsci.2007.07.019Clément, K., Vaisse, C., Lahlou, N., Cabrol, S., Pelloux, V., Cassuto, D., … Guy-Grand, B. (1998). A mutation in the human leptin receptor gene causes obesity and pituitary dysfunction. Nature, 392(6674), 398-401. doi:10.1038/32911Dubern, B., & Clement, K. (2012). Leptin and leptin receptor-related monogenic obesity. Biochimie, 94(10), 2111-2115. doi:10.1016/j.biochi.2012.05.010Lim, K.-S., Kim, J.-M., Lee, E.-A., Choe, J.-H., & Hong, K.-C. (2014). A Candidate Single Nucleotide Polymorphism in the 3′ Untranslated Region of Stearoyl-CoA Desaturase Gene for Fatness Quality and the Gene Expression in Berkshire Pigs. Asian-Australasian Journal of Animal Sciences, 28(2), 151-157. doi:10.5713/ajas.14.0529Saatchi, M., Garrick, D. J., Tait, R. G., Mayes, M. S., Drewnoski, M., Schoonmaker, J., … Reecy, J. M. (2013). Genome-wide association and prediction of direct genomic breeding values for composition of fatty acids in Angus beef cattlea. BMC Genomics, 14(1). doi:10.1186/1471-2164-14-730Chen, L., Ekine-Dzivenu, C., Vinsky, M., Basarab, J., Aalhus, J., Dugan, M. E. R., … Li, C. (2015). Genome-wide association and genomic prediction of breeding values for fatty acid composition in subcutaneous adipose and longissimus lumborum muscle of beef cattle. BMC Genetics, 16(1). doi:10.1186/s12863-015-0290-

Combined effects of time spent in physical activity, sedentary behaviors and sleep on obesity and cardio-metabolic health markers: a novel compositional data analysis approach

<div><p>The associations between time spent in sleep, sedentary behaviors (SB) and physical activity with health are usually studied without taking into account that time is finite during the day, so time spent in each of these behaviors are codependent. Therefore, little is known about the combined effect of time spent in sleep, SB and physical activity, that together constitute a composite whole, on obesity and cardio-metabolic health markers. Cross-sectional analysis of NHANES 2005–6 cycle on N = 1937 adults, was undertaken using a compositional analysis paradigm, which accounts for this intrinsic codependence. Time spent in SB, light intensity (LIPA) and moderate to vigorous activity (MVPA) was determined from accelerometry and combined with self-reported sleep time to obtain the 24 hour time budget composition. The distribution of time spent in sleep, SB, LIPA and MVPA is significantly associated with BMI, waist circumference, triglycerides, plasma glucose, plasma insulin (all p<0.001), and systolic (p<0.001) and diastolic blood pressure (p<0.003), but not HDL or LDL. Within the composition, the strongest positive effect is found for the proportion of time spent in MVPA. Strikingly, the effects of MVPA replacing another behavior and of MVPA being displaced by another behavior are asymmetric. For example, re-allocating 10 minutes of SB to MVPA was associated with a lower waist circumference by 0.001% but if 10 minutes of MVPA is displaced by SB this was associated with a 0.84% higher waist circumference. The proportion of time spent in LIPA and SB were detrimentally associated with obesity and cardiovascular disease markers, but the association with SB was stronger. For diabetes risk markers, replacing SB with LIPA was associated with more favorable outcomes. Time spent in MVPA is an important target for intervention and preventing transfer of time from LIPA to SB might lessen the negative effects of physical inactivity.</p></div

Assessment of the performance of hidden Markov models for imputation in animal breeding

Abstract Background In this paper, we review the performance of various hidden Markov model-based imputation methods in animal breeding populations. Traditionally, pedigree and heuristic-based imputation methods have been used for imputation in large animal populations due to their computational efficiency, scalability, and accuracy. Recent advances in the area of human genetics have increased the ability of probabilistic hidden Markov model methods to perform accurate phasing and imputation in large populations. These advances may enable these methods to be useful for routine use in large animal populations, particularly in populations where pedigree information is not readily available. Methods To test the performance of hidden Markov model-based imputation, we evaluated the accuracy and computational cost of several methods in a series of simulated populations and a real animal population without using a pedigree. First, we tested single-step (diploid) imputation, which performs both phasing and imputation. Second, we tested pre-phasing followed by haploid imputation. Overall, we used four available diploid imputation methods (fastPHASE, Beagle v4.0, IMPUTE2, and MaCH), three phasing methods, (SHAPEIT2, HAPI-UR, and Eagle2), and three haploid imputation methods (IMPUTE2, Beagle v4.1, and Minimac3). Results We found that performing pre-phasing and haploid imputation was faster and more accurate than diploid imputation. In particular, among all the methods tested, pre-phasing with Eagle2 or HAPI-UR and imputing with Minimac3 or IMPUTE2 gave the highest accuracies with both simulated and real data. Conclusions The results of this study suggest that hidden Markov model-based imputation algorithms are an accurate and computationally feasible approach for performing imputation without a pedigree when pre-phasing and haploid imputation are used. Of the algorithms tested, the combination of Eagle2 and Minimac3 gave the highest accuracy across the simulated and real datasets

Genome-wide association study confirm major QTL for backfat fatty acid composition on SSC14 in Duroc pigs

Background: Fatty acid composition contributes importantly to meat quality and is essential to the nutritional value of the meat. Identification of genetic factors underlying levels of fatty acids can be used to breed for pigs with healthier meat. The aim of this study was to conduct genome-wide association studies (GWAS) to identify QTL regions affecting fatty acid composition in backfat from the pig breeds Duroc and Landrace. Results: Using data from the Axiom porcine 660 K array, we performed GWAS on 454 Duroc and 659 Landrace boars for fatty acid phenotypes measured by near-infrared spectroscopy (NIRS) technology (C16:0, C16:1n-7, C18:0, C18:1n-9, C18:2n-6, C18:3n-3, total saturated fatty acids, monounsaturated fatty acids and polyunsaturated fatty acids). Two QTL regions on SSC4 and SSC14 were identified in Duroc for the de novo synthesized fatty acids traits, whereas one QTL on SSC8 was detected in Landrace for C16:1n-7. The QTL region on SSC14 has been reported in previous studies and a putative causative mutation has been suggested in the promoter region of the SCD gene. Whole genome re-sequencing data was used for genotype imputation and to fine map the SSC14 QTL region in Norwegian Duroc. This effort confirms the location of the QTL on this chromosome as well as suggesting other putative candidate genes in the region. The most significant single nucleotide polymorphisms (SNPs) located on SSC14 explain between 55 and 76% of the genetic variance and between 27 and 54% of the phenotypic variance for the de novo synthesized fatty acid traits in Norwegian Duroc. For the QTL region on SSC8 in Landrace, the most significant SNP explained 19% of the genetic variance and 5% of the phenotypic variance for C16:1n-7. Conclusions: This study confirms a major QTL affecting fatty acid composition on SSC14 in Duroc, which can be used in genetic selection to increase the level of fatty acid desaturation. The SSC14 QTL was not segregating in the Landrace population, but another QTL on SSC8 affecting C16:1n-7 was identified and might be used to increase the level of desaturation in meat products from this breed