Using gene networks to identify genes and pathways involved in milk production traits in Polish Holstein dairy cattle
T. Suchocki, K. Wojdak-Maksymiec, J. Szydahttps://doi.org/10.17221/43/2015-CJASCitation:Suchocki T., Wojdak-Maksymiec K., Szyda J. (2016): Using gene networks to identify genes and pathways involved in milk production traits in Polish Holstein dairy cattle. Czech J. Anim. Sci., 61: 526-538.
When analyzing phenotypes undergoing a complex mode of inheritance, it is of great interest to switch the scope from single genes to gene pathways, which form better defined functional units. We used gene networks to search for physiological processes and underlying genes responsible for complex traits recorded in dairy cattle. Major problems addressed included loss of information from multiple single nucleotide polymorphisms (SNPs) located within or close to the same gene, ignoring information on linkage disequilibrium and validation of the obtained gene network. 2601 bulls genotyped by the Illumina BovineSNP50 BeadChip were used. SNP effects were estimated using a mixed model, then underlying gene effects were estimated and tested for significance, subsequently a gene network was constructed and the functional information represented by the network was retrieved. The networks were validated by repeating the above-mentioned analyses after permutation of bulls’ pseudophenotypes. Effects of 4345 genes were estimated, what makes 16.4% of all genes mapped to the UMD3.1 reference genome. Assuming the maximum 10% type I error rate, for milk yield 50 different gene ontology (GO) terms and three pathways defined by the Kyoto Encyclopedia of Genes and Genomes (KEGG) were significantly overrepresented in the real data as compared to the permuted data sets, while for fat yield nine of the GO terms were significantly overrepresented in the real data network, although none of the KEGG pathways reached the significance level. In turn, for protein yield 28 of the GO terms and six KEGG pathways were significantly overrepresented in the real data. Based on the physiological information we identified sets of loci involved in the determination of milk yield (224 genes), fat yield (72 genes), and protein yield (546 genes). Among the genes some have large effects and have already been reported in previous studies, whereas some others represent novel discoveries and thus most probably genes with medium or small effects on trait variation.Keywords:
cattle; gene networks; GO; GWAS; KEGG; mixed model; SNP; validationReferences:
Behl Jyotsna Dhingra, Verma N. K., Tyagi Neha, Mishra Priyanka, Behl Rahul, Joshi B. K. (2012): The Major Histocompatibility Complex in Bovines: A Review. ISRN Veterinary Science, 2012, 1-12 https://doi.org/10.5402/2012/872710Berry S.D., McFadden T.B., Pearson R.E., Akers R.M. (2001): A local increase in the mammary IGF-1: IGFBP-3 ratio mediates the mammogenic effects of estrogen and growth hormone. Domestic Animal Endocrinology, 21, 39-53 https://doi.org/10.1016/S0739-7240(01)00101-1Bolormaa S., Pryce J.E., Hayes B.J., Goddard M.E. (2010): Multivariate analysis of a genome-wide association study in dairy cattle. Journal of Dairy Science, 93, 3818-3833 https://doi.org/10.3168/jds.2009-2980Braun Rosemary, Buetow Kenneth, Schork Nicholas J. (2011): Pathways of Distinction Analysis: A New Technique for Multi–SNP Analysis of GWAS Data. PLoS Genetics, 7, e1002101- https://doi.org/10.1371/journal.pgen.1002101CLAGUE Michael J. (): Molecular aspects of the endocytic pathway. Biochemical Journal, 336, 271-282 https://doi.org/10.1042/bj3360271Cochran Sarah D, Cole John B, Null Daniel J, Hansen Peter J (2013): Discovery of single nucleotide polymorphisms in candidate genes associated with fertility and production traits in Holstein cattle. BMC Genetics, 14, 49- https://doi.org/10.1186/1471-2156-14-49Gianola D., de los Campos G., Hill W. G., Manfredi E., Fernando R. (): Additive Genetic Variability and the Bayesian Alphabet. Genetics, 183, 347-363 https://doi.org/10.1534/genetics.109.103952Henderson C.R. (1984): Applications of Linear Models in Animal Breeding. University of Guelph, Guelph, Canada.Horst R.L., Goff J.P., Reinhardt T.A. (1997): Calcium and vitamin D metabolism during lactation. Journal of Mammary Gland Biology and Neoplasia, 2, 253–263. https://doi.org/10.1023/A:1026384421273Huang Yen-Tsung, Lin Xihong (2013): Gene set analysis using variance component tests. BMC Bioinformatics, 14, 210- https://doi.org/10.1186/1471-2105-14-210Jairath L., Dekkers J.C.M., Schaeffer L.R., Liu Z., Burnside E.B., Kolstad B. (1998): Genetic Evaluation for Herd Life in Canada. Journal of Dairy Science, 81, 550-562 https://doi.org/10.3168/jds.S0022-0302(98)75607-3Kaselonis G (1999): Expression of Hexokinase 1 and Hexokinase 2 in Mammary Tissue of Nonlactating and Lactating Rats: Evaluation by RT–PCR. Molecular Genetics and Metabolism, 68, 371-374 https://doi.org/10.1006/mgme.1999.2923Kochetov German A., Sevostyanova Irina A. (2010): Functional nonequivalence of transketolase active centers. IUBMB Life, 62, 797-802 https://doi.org/10.1002/iub.395Legarra A., Misztal I. (2008): Technical Note: Computing Strategies in Genome-Wide Selection. Journal of Dairy Science, 91, 360-366 https://doi.org/10.3168/jds.2007-0403Linzell J.L., Peaker M. (1973): Changes in mammary gland permeability at the onset of lactation in the goat: an effect on tight junctions? The Journal of Physiology, 230, 13–14.LUSH JAY L., HOLBERT J. C., WILLHAM O. S. (1936): GENETIC HISTORY OF THE HOLSTEIN-FRIESIAN CATTLE IN THE UNITED STATES. Journal of Heredity, 27, 61-72 https://doi.org/10.1093/oxfordjournals.jhered.a104174Mao X., Cai T., Olyarchuk J. G., Wei L. (): Automated genome annotation and pathway identification using the KEGG Orthology (KO) as a controlled vocabulary. Bioinformatics, 21, 3787-3793 https://doi.org/10.1093/bioinformatics/bti430Martin Alexander, Ochagavia Maria E, Rabasa Laya C, Miranda Jamilet, Fernandez-de-Cossio Jorge, Bringas Ricardo (2010): BisoGenet: a new tool for gene network building, visualization and analysis. BMC Bioinformatics, 11, 91- https://doi.org/10.1186/1471-2105-11-91Meredith Brian K, Kearney Francis J, Finlay Emma K, Bradley Daniel G, Fahey Alan G, Berry Donagh P, Lynn David J (2012): Genome-wide associations for milk production and somatic cell score in Holstein-Friesian cattle in Ireland. BMC Genetics, 13, 21- https://doi.org/10.1186/1471-2156-13-21Michelizzi Vanessa N., Wu Xiaolin, Dodson Michael V., Michal Jennifer J., Zambrano-Varon Jorge, McLean Derek J., Jiang Zhihua (2011): A Global View of 54,001 Single Nucleotide Polymorphisms (SNPs) on the Illumina BovineSNP50 BeadChip and Their Transferability to Water Buffalo. International Journal of Biological Sciences, 7, 18-27 https://doi.org/10.7150/ijbs.7.18Mukherjee S., Ghosh R.N., Maxfield F.R. (1997): Endocytosis. Physiological Reviews, 77, 759–803.Naylor M. J. (2005): Ablation of 1 integrin in mammary epithelium reveals a key role for integrin in glandular morphogenesis and differentiation. The Journal of Cell Biology, 171, 717-728 https://doi.org/10.1083/jcb.200503144Nemir M., Bhattacharyya D., Li X., Singh K., Mukherjee A. B., Mukherjee B. B. (): Targeted Inhibition of Osteopontin Expression in the Mammary Gland Causes Abnormal Morphogenesis and Lactation Deficiency. Journal of Biological Chemistry, 275, 969-976 https://doi.org/10.1074/jbc.275.2.969Neuman E, Ladha M H, Lin N, Upton T M, Miller S J, DiRenzo J, Pestell R G, Hinds P W, Dowdy S F, Brown M, Ewen M E (): Cyclin D1 stimulation of estrogen receptor transcriptional activity independent of cdk4.. Molecular and Cellular Biology, 17, 5338-5347 https://doi.org/10.1128/MCB.17.9.5338Neville Margaret C. (2005): Calcium Secretion into Milk. Journal of Mammary Gland Biology and Neoplasia, 10, 119-128 https://doi.org/10.1007/s10911-005-5395-zNguyen D.A., Neville M.C. (1998): Tight junction regulation in the mammary gland. Journal of Mammary Gland Biology and Neoplasia, 3, 233–246. https://doi.org/10.1023/A:1018707309361Ogorevc J., Kunej T., Razpet A., Dovc P. (2009): Database of cattle candidate genes and genetic markers for milk production and mastitis. Animal Genetics, 40, 832-851 https://doi.org/10.1111/j.1365-2052.2009.01921.xPurcell Shaun, Neale Benjamin, Todd-Brown Kathe, Thomas Lori, Ferreira Manuel A.R., Bender David, Maller Julian, Sklar Pamela, de Bakker Paul I.W., Daly Mark J., Sham Pak C. (2007): PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses. The American Journal of Human Genetics, 81, 559-575 https://doi.org/10.1086/519795Qanbari S., Pimentel E. C. G., Tetens J., Thaller G., Lichtner P., Sharifi A. R., Simianer H. (2010): A genome-wide scan for signatures of recent selection in Holstein cattle. Animal Genetics, , - https://doi.org/10.1111/j.1365-2052.2009.02016.xRichert Monica M., Wood Teresa L. (1999): The Insulin-Like Growth Factors (IGF) and IGF Type I Receptor during Postnatal Growth of the Murine Mammary Gland: Sites of Messenger Ribonucleic Acid Expression and Potential Functions 1. Endocrinology, 140, 454-461 https://doi.org/10.1210/endo.140.1.6413Sax Christina M., Salamon Csaba, Kays W. Todd, Guo Jing, Yu Fushin X., Cuthbertson R. Andrew, Piatigorsky Joram (1996): Transketolase Is a Major Protein in the Mouse Cornea. Journal of Biological Chemistry, 271, 33568-33574 https://doi.org/10.1074/jbc.271.52.33568Shannon P. (2003): Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks. Genome Research, 13, 2498-2504 https://doi.org/10.1101/gr.1239303Shennan D.B., Peaker M. (2000): Transport of milk constituents by the mammary gland. Physiological Reviews, 80, 925–951.Sicinski Piotr, Donaher Joana Liu, Parker Susan B., Li Tiansen, Fazeli Amin, Gardner Humphrey, Haslam Sandra Z., Bronson Roderick T., Elledge Stephen J., Weinberg Robert A. (1995): Cyclin D1 provides a link between development and oncogenesis in the retina and breast. Cell, 82, 621-630 https://doi.org/10.1016/0092-8674(95)90034-9Stelwagen K., McLaren R.D., Turner S.A., McFadden H.A., Prosser C.G. (1998a): No evidence for basolateral secretion of milk protein in the mammary gland of lactating goats. Journal of Dairy Science, 81, 434–437.Stelwagen K., van Espen D.C., Verkerk G.A., McFadden H.A., Farr V.C. (1998b): Elevated plasma cortisol reduces permeability of mammary tight junctions in the lactating bovine mammary epithelium. Journal of Endocrinology, 159, 173–178.Strabel T., Jamrozik J. (2006): Genetic analysis of milk production traits of Polish Black and White cattle using large-scale random regression test-day models. Journal of Dairy Science, 89, 3152–3163.VanRaden P.M. (2008): Efficient Methods to Compute Genomic Predictions. Journal of Dairy Science, 91, 4414-4423 https://doi.org/10.3168/jds.2007-0980Verbanck Marie, Lê Sébastien, Pagès Jérôme (2013): A new unsupervised gene clustering algorithm based on the integration of biological knowledge into expression data. BMC Bioinformatics, 14, 42- https://doi.org/10.1186/1471-2105-14-42Wilde CJ, Addey CV, Li P, Fernig DG (1997): Programmed cell death in bovine mammary tissue during lactation and involution. Experimental Physiology, 82, 943-953 https://doi.org/10.1113/expphysiol.1997.sp004075Wilde C.J., Knight C.H., Flint D.J. (1999): Control of milk secretion and apoptosis during mammary gland involution. Journal of Mammary Gland Biology and Neoplasia, 4, 129–136. https://doi.org/10.1023/A:1018717006152Xiao Yufei, Hsiao Tzu-Hung, Suresh Uthra, Chen Hung-I Harry, Wu Xiaowu, Wolf Steven E., Chen Yidong (2014): A novel significance score for gene selection and ranking. Bioinformatics, 30, 801-807 https://doi.org/10.1093/bioinformatics/btr671Zhe S., Naqvi S. A. Z., Yang Y., Qi Y. (2013): Joint network and node selection for pathway-based genomic data analysis. Bioinformatics, 29, 1987-1996 https://doi.org/10.1093/bioinformatics/btt335Zhou Xi, Chen Pengcheng, Wei Qiang, Shen Xueling, Chen Xin (2013): Human interactome resource and gene set linkage analysis for the functional interpretation of biologically meaningful gene sets. Bioinformatics, 29, 2024-2031 https://doi.org/10.1093/bioinformatics/btt353