شناسایی ژن‌های موثر بر صفات رشد در جوجه های گوشتی با استفاده از روش‌های رگرسیون خطی و یادگیری ماشین

Arabnejad, M., Montgomery, C. G., Gaffney, P. M., McKinney, B. A. (2020). Nearest-neighbor projected distance regression for epistasis detection in GWAS with population structure correction. Frontier Genetics, 11:784.

Bahadoran, S., Dehghani Samani, A., & Hassanpour, H. (2018). Effect of heat stress on the gene expression of ion transporters/channels in the uterus of laying hens during eggshell formation. Stress, 21(1), 51-58.

Baştanlar, Y., & Özuysal, M. (2014). Introduction to machine learning. Methods in Molecular Biology, 1107: 105-128.

Boulesteix, A. L., Janitza, S., ruppa, J. K., & König I. R. (2012). Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics. Technical Report. Department of Statistics, University of Munich.

Breiman, L. (2001). Random forests. Machine. Learning, 45, 5-32.

Brew, K., Dinakarpandian, D., & Nagase, H. (2000). Tissue inhibitors of metalloproteinases: evolution, structure and function. Biochimica et Biophysica Acta (BBA)-Protein Structure and Molecular Enzymology, 1477(1-2), 267-283.

Cha, J., Choo, H., Srikanth, K., Lee, S. H., Son, J. W., Park, M. R., Kim, N., Jang, G. W., & Park, J. E. (2021). Genome-wide association study identifies 12 loci associated with body weight at age 8 weeks in Korean native chickens. Genes, 12(8), 1170.

Cremonesi, P., Capoferri, R., Pisoni, G., Del Corvo, M., Strozzi, F., Rupp, R., Caillat, H., Modesto, P., Moroni, P., Williams, J. L., & Castiglioni, B. (2012). Response of the goat mammary gland to infection with Staphylococcus aureus revealed by gene expression profiling in milk somatic and white blood cells. BMC Genomics, 13(1), 1-17.

Dadousis, C., Somavilla, A., Ilska, J. J., Johnsson, M., Batista, L., Mellanby, R. J., Headon, D., Gottardo, P., Whalen, A., Wilson, D., & Dunn, I. C. (2021). A genome-wide association analysis for body weight at 35 days measured on 137,343 broiler chickens. Genetics Selection Evolution, 53, 1-14.

Dzomba, E. F., Chimonyo, M., Snyman, M. A., & Muchadeyi, F. C. (2020). The genomic architecture of South African mutton, pelt, dual‐purpose and nondescript sheep breeds relative to global sheep populations. Animal Genetics, 51 (6), 910-923.

Emrani, H., Vaez Torshizi, R., Masoudi, A. A., & Ehsani, A. (2017). Identification of new loci for body weight traits in F2 chicken population using genome-wide association study. Livestock Science, 206, 125–131.

Enoma, D. O., Bishung, J., Abiodun, T., Ogunlana, O., & Osamor, V. C. (2022). Machine learning approaches to genome-wide association studies. Journal of King Saud University-Science, 34(4), 101847.

Fehm, L., Kern, W., & Peters, A. (2004). Body weight regulation through the central nervous system. The development of a pathogenetically based adiposity therapy. Medizinische Klinik, 99 (11), 674-679.

Friedman, J. (2002). Stochastic gradient boosting. Computational Statistics & Data Analysis, 38,
367–378

Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of Statistics, 29 (5), 1189-1232.

Goddard M. E., & Hayes, B. J. (2009) Mapping genes for complex traits in domestic
animals and their use in breeding programmes. Nature Reviews Genetics, 10, 381–
391.

Goldstein, B. A., Hubbard, A. E., Cutler A., & Barcellos L. F. (2010). An application of random forests to a genome-wide association dataset: Methodological considerations and new findings. BMC Genetics, 11, 49.

Harada, A., Teng, J., Takei, Y., Oguchi, K., & Hirokawa, N. (2002). MAP2 is required for dendrite elongation, PKA anchoring in dendrites, and proper PKA signal transduction. The Journal of Cell Biology, 158 (3), 541-549.

Hayes B. (2013). Overview of Statistical Methods for Genome-Wide Association Studies (GWAS). Methods in Molecular Biology, 1019, 149-69.

Hong E. P. & Park J. W. (2012). Sample size and statistical power calculation in genetic association studies. Genomics Inform, 10(2), 117-22.

Hrabia, A., Miska, K. B., Schreier, L. L., Proszkowiec-Weglarz, M. (2022) Altered gene expression of selected matrix metalloproteinase system proteins in the broiler chicken gastrointestinal tract during post-hatch development and coccidia infection. Poultry Science, 101(8):101915.

Hu, T., Darabos, C., & Urbanowicz, R. (2020). Machine learning in genome-wide association studies. Frontiers in Genetics, 11, 593958.

Jin, C. F., Chen, Y. J., Yang, Z. Q., Shi, K., & Chen, C. K. (2015). A genome-wide association study of growth trait-related single nucleotide polymorphisms in Chinese Yancheng chickens. Genetics and Molecular Research, 14 (4), 15783-15792.

Kanakachari, M., Ashwini, R., Chatterjee, R. N., & Bhattacharya, T. K. (2021). Transcriptome analysis reveals potential mechanisms and pathways underlying embryonic development with respect to muscle growth and egg production in slow and fast growing chickens. BMC Genomics, 13 (1), 58.

Li, B., Zhang, N., Wang, Y. G., George, A. W., Reverter, A., & Li, Y. (2018). Genomic prediction of breeding values using a subset of SNPs identified by three machine learning methods. Frontiers in Genetics, 9, 237.

Liaw, A., & Wiener, M. (2015). randomForest: Breiman and Cutler’s random forests for classification and regression. R package version, 4, 14.

Liu, L., Cui, H., Xing, S., Zhao, G., & Wen, J. (2019). Effect of divergent selection for intramuscular fat content on muscle lipid metabolism in chickens. Animals, 10(1), 4.

Lyu, S., Yang, P., Liu, Y., Song, T., Zhang, Z., Shi, Q., Chen, F., Liu, X., Li, Z., Ru, B., & Cai, C. (2021). Genetic effects of MOGAT1 gene SNP in growth traits of Chinese cattle. Gene, 769, 145201.

Mebratie, W., Madsen, P., Hawken, R. Rome, H., Marois, D., Henshall, J., Bovenhuis, H., & Jensen J. (2019). Genetic parameters for body weight and different definitions of residual feed intake in broiler chickens. Genetics Selection Evolution, 51, 53.

Nicodemus, K. K., Malley, J. D., Strobl, C., & Ziegler, A. (2010). The behaviour of random forest permutation-based variable importance measures under predictor correlation. BMC Bioinformatics. 11,110.

Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A., Bender, D., Maller, J., Sklar, P., De Bakker, P. I., Daly, M. J., &  Sham, P. C. (2007). PLINK: a tool set for whole-genome association and population-based linkage analyses. American Journal of Human Genetics, 81 (3), 559-575.

Ren, T., Li, Z., Zhou, Y., Liu, X., Han, R., Wang, Y., Yan, F., Sun, G., Li, H., & Kang, X. (2018). Sequencing and characterization of lncRNAs in the breast muscle of Gushi and Arbor Acres chickens. Genome, 61(5), 337-347.

Resnyk, C. W., Carré, W., Wang, X., Porter, T. E., Simon, J., Le Bihan-Duval, E., Duclos, M. J., Aggrey, S. E., &  Cogburn, L.A. (2017). Transcriptional analysis of abdominal fat in chickens divergently selected on bodyweight at two ages reveals novel mechanisms controlling adiposity: validating visceral adipose tissue as a dynamic endocrine and metabolic organ. BMC Genomics, 18, 1-31.

Ridgeway, G. (2013). Package’GBM’: Generalized Boosted Regression Models. R Package version, 2.

Srikanth, K., Lee, S. H., Chung, K. Y., Park, J. E., Jang, G. W., Park, M. R., Kim, N. Y., Kim, T. H., Chai, H. H.,

Sun, J., Zhang, C., Lan, X., Lei, C., & Chen, H. (2012). Exploring polymorphisms and associations of the bovine MOGAT3 gene with growth traits. Genome, 55(1), 56-62.

Sun, S., Dong, B., & Zou, Q. (2021). Revisiting genome-wide association studies from statistical modelling to machine learning. Briefings in Bioinformatics, 22(4), 263.

Teuliere, J., Cordes, S., Singhvi, A., Talavera, K., & Garriga, G. (2014). Asymmetric neuroblast divisions producing apoptotic cells require the cytohesin GRP-1 in Caenorhabditis Elegans. Genetics, 198(1), 229-247.

Tian, W., Wang, D., Wang, Z., Jiang, K., Li, Z., Tian, Y., Kang, X., Liu, X., & Li, H. (2021). Evolution, expression profile, and regulatory characteristics of ACSL gene family in chicken (Gallus Gallus). Gene, 764, 145094.

Wang, J., Yuan, X., Ye, S., Huang, S., He, Y., Zhang, H., Li, J., Zhang, X., & Zhang, Z. (2019). Genome wide association study on feed conversion ratio using imputed sequence data in chickens. Asian-Australasian Journal of Animal Sciences, 32 (4), 494-500.

Willer, C. J., Schmidt, E. M., Sengupta, S., Peloso, G. M., Gustafsson, S., Kanoni, S., Ganna, A., Chen, J., Buchkovich, M.L., Mora, S., & Beckmann, J.S. (2013). Discovery and refinement of loci associated with lipid levels. Nature Genetics, 45 (11), 1274-1283.

Wray, N. R., Yang, J., Hayes, B. J., Price, A. L., Goddard, M. E., & Visscher, P. M. (2013). Pitfalls of predicting complex traits from SNPs. Nature Reviews Genetics, 14 (7), 507–515.

Yates, A., Akanni, W., Amode, M. R., Barrell, D., Billis, K., Carvalho-Silva, D., Cummins, C., Clapham, P., Fitzgerald, S., Gil, L., & Girón, C. G. (2016). Ensembl 2016. Nucleic Acids Research, 44, 710-716.

Yue, Q., Chen, Y., Chen, H., & Zhou, R. (2022). Transcriptome profile reveals novel candidate genes associated with bone strength in end-of-lay hens. Animal Biotechnology, 1-9.

Zhang, G. X., Fan, Q. C., Zhang, T., Wang, J. Y., Wang, W. H., Xue, Q., & Wang, Y. J. (2015). Genome-wide association study of growth traits in the Jinghai Yellow chicken. Genetics and Molecular Research, 14(4), 15331-15338.