Microstructure of Winged Beans

Microstructures of seven plant introductions of winged beans (Psophocarpus tetragonolobus) produced in Okinawa, Japan were investigated. In cotyledonary cells of winged beans, protein bodies plus numerous lipid bodies were distributed in a cytoplasmic network. Starch granules were often found in some introductions but rarely in others. All seven introductions had very thick cell walls. The high protein, fat and hemicellulose contents of winged beans are consistent with the numerous protein bodies, lipid bodies and thick cell walls in the mature cotyledonary cells. The cell walls contained a number of depressions or cavities 1 to 2 lJ m deep which frequently occurred opposite complementary pits in adjacent cells (presumably pit-pairs). Plasmodesmata traverse the cell walls in the pit-pairs. In order to determine changes during development, cultivar UPS-32 cultivated at Fukuoka-city was used. In coty ledonary cells at 30 days after flowering, cell walls which had pitpairs with plasmodesmata, developing amyloplasts with starch granules, vacuoles with dense flocculent materials, tubular rough endoplasmic reticulum, mitochondria etc., were observed but no protein bodies or lipid bodies were apparent. Protein bodies and lipid bodies were, however, found at 45 days after flowering. Cotyledonary cells at 45 days contained many starch granules but mature seeds contained few, if any

Guardians Ad Litem as Surrogate Parents: Implication for Role Definition and Confidentiality

SALMON (Scalable Ab-initio Light–Mattersimulator for Optics and Nanoscience, http://salmon-tddft.jp) is a software package for the simulation of electron dynamics and optical properties of molecules, nanostructures, and crystalline solids based on first-principles time-dependent density functional theory. The core part of the software is the real-time, real-space calculation of the electron dynamics induced in molecules and solids by an external electric field solving the time-dependent Kohn–Sham equation. Using a weak instantaneous perturbing field, linear response properties such as polarizabilities and photoabsorptions in isolated systems and dielectric functions in periodic systems are determined. Using an optical laser pulse, the ultrafast electronic response that may be highly nonlinear in the field strength is investigated in time domain. The propagation of the laser pulse in bulk solids and thin films can also be included in the simulation via coupling the electron dynamics in many microscopic unit cells using Maxwell’s equations describing the time evolution of the electromagnetic fields. The code is efficiently parallelized so that it may describe the electron dynamics in large systems including up to a few thousand atoms. The present paper provides an overview of the capabilities of the software package showing several sample calculations. Program summary Program Title: SALMON: Scalable Ab-initio Light–Matter simulator for Optics and Nanoscience Program Files doi:http://dx.doi.org/10.17632/8pm5znxtsb.1 Licensing provisions: Apache-2.0 Programming language: Fortran 2003 Nature of problem: Electron dynamics in molecules, nanostructures, and crystalline solids induced by an external electric field is calculated based on first-principles time-dependent density functional theory. Using a weak impulsive field, linear optical properties such as polarizabilities, photoabsorptions, and dielectric functions are extracted. Using an optical laser pulse, the ultrafast electronic response that may be highly nonlinear with respect to the exciting field strength is described as well. The propagation of the laser pulse in bulk solids and thin films is considered by coupling the electron dynamics in many microscopic unit cells using Maxwell’s equations describing the time evolution of the electromagnetic field. Solution method: Electron dynamics is calculated by solving the time-dependent Kohn–Sham equation in real time and real space. For this, the electronic orbitals are discretized on a uniform Cartesian grid in three dimensions. Norm-conserving pseudopotentials are used to account for the interactions between the valence electrons and the ionic cores. Grid spacings in real space and time, typically 0.02 nm and 1 as respectively, determine the spatial and temporal resolutions of the simulation results. In most calculations, the ground state is first calculated by solving the static Kohn–Sham equation, in order to prepare the initial conditions. The orbitals are evolved in time with an explicit integration algorithm such as a truncated Taylor expansion of the evolution operator, together with a predictor–corrector step when necessary. For the propagation of the laser pulse in a bulk solid, Maxwell’s equations are solved using a finite-difference scheme. By this, the electric field of the laser pulse and the electron dynamics in many microscopic unit cells of the crystalline solid are coupled in a multiscale framework

X-Ray Magnetic Circular Dichroism at the K edge of Mn3GaC

We theoretically investigate the origin of the x-ray magnetic circular dichroism (XMCD) spectra at the K edges of Mn and Ga in the ferromagnetic phase of Mn3GaC on the basis of an ab initio calculation. Taking account of the spin-orbit interaction in the LDA scheme, we obtain the XMCD spectra in excellent agreement with the recent experiment. We have analyzed the origin of each structure, and thus elucidated the mechanism of inducing the orbital polarization in the p symmetric states. We also discuss a simple sum rule connecting the XMCD spectra with the orbital moment in the p symmetric states.Comment: 5 pages, 5 figures, accepted for publication in Physical Review

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-

Phase-field approach to heterogeneous nucleation

We consider the problem of heterogeneous nucleation and growth. The system is described by a phase field model in which the temperature is included through thermal noise. We show that this phase field approach is suitable to describe homogeneous as well as heterogeneous nucleation starting from several general hypotheses. Thus we can investigate the influence of grain boundaries, localized impurities, or any general kind of imperfections in a systematic way. We also put forward the applicability of our model to study other physical situations such as island formation, amorphous crystallization, or recrystallization.Comment: 8 pages including 7 figures. Accepted for publication in Physical Review

Prediction of the remnant liver hypertrophy ratio after preoperative portal vein embolization.

Background: Portal vein embolization (PVE) is considered to improve the safety of major hepatectomy. Various conditions might affect remnant liver hypertrophy after PVE. The aim of the present study was to clarify the factors that affect remnant liver hypertrophy and to establish a prediction formula for the hypertrophy ratio. Methods: Fifty-nine patients who underwent preoperative PVE for cholangiocarcinoma (39 patients), metastatic carcinoma (10 patients), hepatocellular carcinoma (8 patients), and other diseases (2 patients) were enrolled in this study. For the prediction of the hypertrophy ratio, a formula with stepwise multiple regression analysis was set up. The following parameters were used: age, gender, future liver remnant ratio to total liver (FLR%), plasma disappearance rate of indocyanine green (ICGK), platelet count, prothrombin activity, serum albumin, serum total bilirubin at the time of PVE and the maximum value before PVE (Max Bil), as well as a history of cholangitis, diabetes mellitus, and chemotherapy. Results: The mean hypertrophy ratio was 28.8%. The 5 parameters detected as predictive factors were age (p = 0.015), FLR% (p < 0.001), ICGK (p = 0.112), Max Bil (p < 0.001), and history of chemotherapy (p = 0.007). The following prediction formula was established: 101.6 - 0.78 × age - 0.88 × FLR% + 128 × ICGK - 1.48 × Max Bil (mg/dl) - 21.2 × chemotherapy. The value obtained using this formula significantly correlated with the actual value (r = 0.72, p < 0.001). A 10-fold cross validation also showed significant correlation (r = 0.62, p < 0.001), and a hypertrophy ratio <20% was predictable with a sensitivity of 100% and a specificity of 90.9%. Moreover, technetium-99m-diethylenetriaminepentaacetic acid-galactosyl human serum albumin scintigraphy showed a significantly smaller increase in the uptake ratio of the remnant liver in patients with prediction values <20% than in those with values ≥20% (6.8 vs. 20.8%, p = 0.030). Conclusions: The prediction formula can prognosticate the hypertrophy ratio after PVE, which may provide a new therapeutic strategy for major hepatectomy

Antagonistic genetic correlations for milking traits within the genome of dairy cattle

Genome-wide association studies can be applied to identify useful SNPs associated with complex traits. Furthermore, regional genomic mapping can be used to estimate regional variance and clarify the genomic relationships within and outside regions but has not previously been applied to milk traits in cattle. We applied both single SNP analysis and regional genomic mapping to investigate SNPs or regions associated with milk yield traits in dairy cattle. The de-regressed breeding values of three traits, total yield (kg) of milk (MLK), fat (FAT), and protein (PRT) in 305 days, from 2,590 Holstein sires in Japan were analyzed. All sires were genotyped with 40,646 single-nucleotide polymorphism (SNP) markers. A genome-wide significant region (P < 0.01) common to all three traits was identified by regional genomic mapping on chromosome (BTA) 14. In contrast, single SNP analysis identified significant SNPs only for MLK and FAT (P < 0.01), but not PRT in the same region. Regional genomic mapping revealed an additional significant region (P < 0.01) for FAT on BTA5 that was not identified by single SNP analysis. The additive whole-genomic effects estimated in the regional genomic mapping analysis for the three traits were positively correlated with one another (0.830-0.924). However, the regional genomic effects obtained by using a window size of 20 SNPs for FAT on BTA14 were negatively correlated (P < 0.01) with the regional genomic effect for MLK (-0.940) and PRT (-0.878). The BTA14 regional effect for FAT also showed significant negative correlations (P < 0.01) with the whole genomic effects for MLK (-0.153), FAT (-0.172), and PRT (-0.181). These negative genomic correlations between loci are consistent with the negative linkage disequilibrium expected for traits under directional selection. Such antagonistic correlations may hamper the fixation of the FAT increasing alleles on BTA14. In summary, regional genomic mapping found more regions associated with milk production traits than did single SNP analysis. In addition, the existence of non-zero covariances between regional and whole genomic effects may influence the detection of regional effects, and antagonistic correlations could hamper the fixation of major genes under intensive selection