RESSALVA Atendendo solicitação do(a) autor(a), o texto completo desta tese será disponibilizado somente a partir de 12/03/2026. SÃO PAULO STATE UNIVERSITY SCHOOL OF AGRICULTURAL AND VETERINARIAN SCIENCES SELECTION SIGNATURES IN DAIRY CATTLE Larissa Graciano Braga Veterinary medicine MsC. in Genetics and Animal Breeding 2025 SÃO PAULO STATE UNIVERSITY SCHOOL OF AGRICULTURAL AND VETERINARIAN SCIENCES SELECTION SIGNATURES IN DAIRY CATTLE Larissa Graciano Braga Advisor: Prof. Dr. Danísio Prado Munari Co-advisors: Dra. Tatiane Cristina Seleguim Chud Dr. Marcos Vinicius Gualberto Barbosa da Silva Thesis presented to the São Paulo State University - School of Agricultural and Veterinary Sciences, Campus of Jaboticabal, in partial fulfillment of requirements for the degree of Ph.D. in Animal Science 2025 B813s Braga, Larissa Graciano Selection signatures in dairy cattle / Larissa Graciano Braga. -- Jaboticabal, 2025 116 p. Tese (doutorado) - Universidade Estadual Paulista (UNESP), Faculdade de Ciências Agrárias e Veterinárias, Jaboticabal Orientador: Danísio Prado Munari Coorientador: Marcos Vinicius Gualberto Barbosa Silva 1. Animais melhoramento genético. 2. Genética animal. 3. Genômica. I. Título. Sistema de geração automática de fichas catalográficas da Unesp. Dados fornecidos pelo autor(a). AUTHOR CURRICULUM INFORMATION Larissa Graciano Braga was born in Porto Velho, Rondônia, on May 29, 1996. She is the daughter of Bárbara Braga Graciano and Sebastião Graciano de Souza. She earned her degree in Veterinary Medicine from the Federal University of Goiás - School of Veterinary and Animal Science (EVZ/ UFG), in 2019. From 2016 to 2017, she served as a volunteer research assistant (PIVIC) in the Undergraduate Research Program (PIP/UFG), under the supervision of Prof. Dr. Paulo Henrique Jorge da Cunha. In 2016, she was Director of Projects at CONPAVet Jr., a junior enterprise based at EVZ/UFG. In 2017, she was awarded a scholarship through the MARCA/MERCOSUR academic mobility program and completed an exchange semester at the Facultad de Ciencias Veterinarias of the Universidad Nacional del Litoral in Esperanza, Santa Fé Province, Argentina. From August 2019 to July 2021, she pursued her Master’s degree in Animal Breeding and Genetics at the School of Agricultural and Veterinary Sciences of São Paulo State University “Júlio de Mesquita Filho” (FCAV/UNESP), Jaboticabal Campus, under the supervision of Prof. Dr. Danísio Prado Munari and co-supervision of Dr. Tatiane Cristina Seleguim Chud and Dr. Marcos Vinicius Gualberto Barbosa da Silva. In August 2021, she began her Ph.D. in Animal Science at the same institution, under the same supervisor and co- supervisors. In June 2023, she started a 12-month doctoral research internship at the University of Guelph, in Guelph, Ontario, Canada, under the supervision of Prof. Dr. Flávio Schramm Schenkel. In February 2025, she began working as a Sessional Professor affiliated with the Department of Exact Sciences at FCAV, teaching courses related to Statistics and Informatics. “The credit belongs to the man who is actually in the arena, whose face is marred by dust and sweat and blood; who strives valiantly; who errs, who comes short again and again, because there is no effort without error and shortcoming; but who does actually strive to do the deeds; who knows the great enthusiasms, the great devotions; who spends himself in a worthy cause; who at the best knows in the end the triumph of high achievement, and who at the worst, if he fails, at least fails while daring greatly, so that his place shall never be with those cold and timid souls who neither know victory nor defeat.” Man in the Arena, 1910, Theodore Roosevelt ACKNOWLEDGEMENTS Gostaria de agradecer a Deus por ter me permitido realizar grandes sonhos, agraciando-me com saúde e por ter me guiado durante toda a caminhada. À mãe Maria, que sempre passou na frente dos meus medos, dificuldades e desafios. Agradeço aos meus pais, Sebastião e Bárbara, por serem minha fortaleza e por me amarem incondicionalmente. Sempre me deram força e acolhimento nos momentos mais difíceis e sempre acreditaram no meu potencial. Vocês fizeram o que podiam para que eu tivesse uma excelente educação e me ensinaram o significado de responsabilidade e honestidade. A distância tem sido muito pesada, mas, mesmo assim, vocês sempre me apoiaram. À Lucas Gazolla Rodrigues Soares, por vibrar a cada conquista minha, acreditar em mim (por muitas vezes mais do que eu mesma) e me incentivar. O seu apoio e suporte tem sido um acalento, especialmente nos dias mais tempestuosos. À Alzira e Sidney Rodrigues Soares, por todo cuidado, apoio, torcida e carinho que vocês têm comigo. À Faculdade de Ciências Agrárias e Veterinárias da Universidade Estadual Paulista (FCAV/UNESP) e à University of Guelph, por ser minha segunda casa e pela excelência em pesquisa e ensino. Aos professores e funcionários por darem o melhor de si para minha formação acadêmico-profissional. Agradeço ao Programa de Pós-Graduação em Ciência Animal e à CAPES (001 e 88887.802720/2023-00) pelo financiamento da minha bolsa de pesquisa e de sanduíche. Especialmente agradeço: Ao meu orientador, Prof. Dr. Danísio Prado Munari, aos meus coorientadores Dr. Marcos Vinicius Gualberto da Silva e Dr.ᵃ Tatiane Cristina Seleguim Chud, e ao meu supervisor durante estágio sanduíche, Prof. Dr. Flávio Schenkel. Eu sempre achei a frase “se pude ver mais longe é porque estava em ombros de gigantes” um clichê, mas não há melhor maneira de descrever o que foram esses anos de doutorado. Vocês sempre foram pacientes e solícitos comigo, agradeço por todas as oportunidades, críticas, discussões científicas, ensinamentos acadêmicos e de vida, e pela confiança depositada em mim. Vocês foram fundamentais na minha formação e continuam a me inspirar a ser uma pesquisadora melhor. Aos membros da banca do exame geral de qualificação, Prof. Dr. Marcos Buzankas e Prof.ᵃ Dr.ᵃ Amanda Maiorano, e aos membros da banca de defesa da dissertação, Dra. Lenira El Faro, Prof.ᵃ Dr.ᵃ Priscila Bernardes, Prof.ᵃ Dr.ᵃ Amanda Maiorano e Prof. Dr. Salvador Ramos, e pelas críticas, reflexões e sugestões a este trabalho. Agradeço à Embrapa (SEG 02.13.05.011.00.00), CNPq (310199/2015-8 e 431629/2016-1) e FAPEMIG (CVZ PPM 00606/16 e APQ-02750-23) pelo financiamento das amostras utilizadas neste trabalho. Agradeço também a Associação de Criadores de Gir Leiteiro (ABCGIL), Embrapa Gado de Leite e 1000 Bulls Project Consortium pelos dados cedidos e ao Laboratório Multiusuário de Bioinformática da Embrapa Informática Agropecuária e Centre for Genetic Improvement of Livestock (CGIL) pela infraestrutura computacional e recurso de TI. Aos companheiros de pós-graduação e do Estatística Aplicada a Genética e Melhoramento Animal (EAGMA) pelo apoio durante toda a trajetória. Com vocês compartilhei muitas dúvidas, momentos de estudo, alegria e confraternização que fizeram meus dias mais leves e felizes. Em especial agradeço a Amanda Baldi, Ana Carolina de Jesus, Ana Carolina Paz, Bruna Salatta, Camilla Arantes, Edimar Gabiati, Gabriel Gubiani, Ivan Filho, Júlia Rodrigues, Júlia Valente, Larissa Temp, Rafael Watanabe, Tádia Stivanin, Roney Teixeira, Thomaz Sena e Viviane Ligori. À Adriana e Shirley, funcionárias do departamento de Engenharia e Ciências Exatas, por toda atenção e disposição em me auxiliar. I thank the CGIL group, especially Alice Vanzin, Bruno Galindo, Chiara Gini, Colin Lynch, Emanueli Da Silva, Gabriella Dodd, Isis Hermisdorff, Ivan Campos, Krishani Sinhalage, Kristin Lee, Lucas Lopes, Ricarda Jahnel, Samla Cunha, Sirlene Lazzaro, and Stephanie Lam, for the amazing coffees, interactions in lab meetings, and for making the period abroad more fun. I also thank Dr. Filippo Miglior and Dr. Christine Baes for the scientific discussions and support. To my friends in Canada. Melissa Nagy (in memoriam), who became one of my greatest friends. Thanks for making my life cozier and reminding me that some things “look like a tomorrow problem” and that we don’t need to take life so seriously. To Barbara Mann and Digant Purandare, with whom I shared great adventures and learned more about life and kindness. To Mélanie Gagné, Magalie, Mélianne, Médérick, and Martin Gregoire for opening their home, farm, and hearts to me. At Triple G Farm, I learned so much about dairy farming and the beauty of Canadian endangered breeds. Agradeço aos demais que, de alguma forma, contribuíram com a minha formação e com a realização deste trabalho, mas que não foram citados nominalmente. Também agradeço pelas oportunidades, direitos não universalizados e privilégios que me foram concedidos ao longo de toda minha vida acadêmica. Aos meus objetivos, erros e acertos que me ensinaram muito. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001. i TABLE OF CONTENTS CHAPTER 01 – GENERAL CONSIDERATIONS ……………………………………… 1 1 INTRODUCTION ……………………………………………………………………….. 1 2 LITERATURE REVIEW ……………………………………………………………….. 2 2.1 Breeds …………………………………………………………………………….. 2 2.2 Selection signatures …………………………………………………………….. 4 2.3 Approaches to detect selection signatures …………………………………… 7 2.4 Copy number variations (CNV) under selection in dairy cattle ………….… 13 2.5 Biodiversity variables …………………………………………………….…….. 14 3 REFERENCES ………………………………………………………………………... 16 CHAPTER 02 – SELECTION SIGNATURES IN GIR AND HOLSTEIN CATTLE ... 23 1 INTRODUCTION ……………………………………………………………………… 24 2 MATERIALS AND METHODS ……………………………………………………… 25 2.1 Samples and Sequencing ……………………………………………………... 25 2.2 Genomic Diversity …………………………………………………………….... 27 2.3 Selection Signatures …………………………………………………………… 28 2.4 Functional Analysis …………………………………………………………….. 30 3 RESULTS ……………………………………………………………………………… 31 3.1 Alignment and Variant Calling ………………………………………………… 31 3.2 Genomic Diversity ……………………………………………………………… 32 3.3 Selection Signatures and Genes Under Selection …………………………. 37 3.4 Overlapping Selection Signatures ………………………………………….… 45 4 DISCUSSION …………………………………………………………………………. 47 4.1 Population Differentiation and Genetic Diversity ……………………………. 47 4.2 Minor Allele Frequency (MAF) ………………………………………………... 49 4.3 Heterozygosity and Inbreeding Coefficient ………………………………….. 49 4.4 Selection Signatures and Genes Under Selection …………………………. 50 5 CONCLUSIONS …………………………………………………………………….… 56 6 REFERENCES ………………………………………………………………………... 57 CHAPTER 03 – SELECTION SIGNATURES IN DAIRY CATTLE: A SYSTEMATIC REVIEW ………………………………………………………………………………….... 67 1 INTRODUCTION ……………………………………………………………………… 68 ii 2 MATERIAL AND METHODS ………………………………………………………... 69 2.1 Literature search and inclusion criteria ………………………………………. 69 2.2 Exclusion criteria ……………………………………………………………….. 70 2.3 Functional analysis …………………………………………………………….. 70 3 RESULTS AND DISCUSSION ……………………………………………………… 70 3.1 Studies and genes under selection …………………………………………... 70 3.2 Fertility …………………………………………………………………………... 76 3.3 Health ………………………………………………….………………...……… 77 3.4 Milk production and components ……………………………………………... 79 3.5 Environmental adaptation …………………………………………….……….. 82 3.6 Color patterns …………………………………………………………………... 85 3.7 Possible pleiotropic genes under selection ………………………..………… 87 4 PROSPECTS ………………………………………………………………………….. 88 4.1 Molecular technology ………………………………………………………….. 88 4.2 Integrative approaches ………………………………………………………… 88 4.3 Future hot topics ……………………………………………………………….. 89 6 CONCLUSIONS ………………………………………………………………………. 89 7 REFERENCES ………………………………………………………………………... 90 iii ASSINATURAS DE SELEÇÃO EM BOVINOS DE LEITE RESUMO – Tanto a seleção natural como a seleção artificial deixam marcas no genoma, conhecidas como assinaturas de seleção, que podem revelar regiões relacionadas a características adaptativas e economicamente importantes para a produção de leite. Além disso, a seleção pode alterar a diversidade genética de uma população. Elevada intensidade de seleção e uso recorrente de um número reduzido de reprodutores podem diminuir a diversidade genética, com possíveis impactos negativos em programas de melhoramento a longo prazo. Como exemplo de população leiteira, a raça Holandesa tem sido fortemente selecionada para alta produtividade no Canadá e nos Estados Unidos, enquanto a raça Gir desempenha papel relevante na produção de leite em regiões tropicais, como Índia e América Latina. No Brasil, o gado Gir foi introduzido entre os séculos XIX e XX e, desde 1985, é selecionado para produção de leite. O objetivo desta tese foi caracterizar assinaturas de seleção em populações de bovinos leiteiros. O capítulo 2 teve como objetivo avaliar a diversidade genética e identificar assinaturas de seleção em bovinos Holandeses da América do Norte (HOL), Gir da Índia (GIR_IN) e Gir Leiteiro (GIR_BR). A diversidade genética foi medida por meio da nucleotide diversity, densidade de variantes de nucleotídeo único, frequência do alelo menor, heterozigosidade observada e esperada, e coeficiente de endogamia. As assinaturas de seleção foram detectadas utilizando Tajima’s D, iHS (integrated haplotype score), FST (fixation index) e XP-EHH (cross-population extended haplotype homozygosity) nos cromossomos autossomos, além de Tajima’s D e iHS no cromossomo X. A população HOL apresentou menor diversidade, enquanto GIR_IN e GIR_BR apresentaram maior variabilidade genética. Genes previamente associados a características de importância econômica, incluindo crescimento, reprodução, mastite, produção de leite, tolerância ao calor, saúde e adaptação, foram identificados em regiões sob seleção. No capítulo 3, uma revisão sistemática sobre assinaturas de seleção em bovinos leiteiros é apresentada. O total de 21 estudos em 19 raças (taurinas, zebuínas e compostas) foram selecionados, com assinaturas de seleção que sobrepuseram 5.099 genes. Genes sob seleção relacionados a características como fertilidade, saúde, produção e composição do leite, adaptação ambiental, padrões de pelagem e genes com possível pleiotropia, foram discutidos. Genes foram relatados recorrentemente nos estudos de assinaturas de seleção, como PLAG1 e HMGA2. Embora DGAT1 seja um gene importante para características do leite, não apresentou evidências de seleção na literatura revisada. A revisão também discutiu perspectivas futuras envolvendo ferramentas moleculares e abordagens integrativas. Como uma conclusão geral, a seleção em bovinos leiteiros é moldada não somente por características econômicas, mas também por pressões ecológicas e adaptativas. Em ambos os capítulos 2 e 3, os genes sob seleção foram relacionados a diversas características de importância econômica. Por fim, o estudo de assinaturas de seleção fornece informações valiosas para o melhoramento genético e a sustentabilidade da pecuária leiteira. Palavras-chave: assinatura de seleção, bovinos de leite, diversidade genética, genes iv SELECTION SIGNATURES IN DAIRY CATTLE ABSTRACT – Natural and artificial selection leave footprints on the genome, known as selection signatures, which can reveal regions related to adaptive and economically important traits for the dairy sector. Additionally, selection may also alter the genetic diversity of a population. Strong selection pressure and the recurrent use of a limited number of sires may decrease genetic diversity, with possible negative effects on long-term breeding programs. As an example of dairy cattle, the Holstein breed has been strongly selected for high productivity in Canada and the United States. In contrast, the Gir breed has an important role in milk production in tropical regions such as India and Latin America. In Brazil, Gir cattle were introduced during the 19th and 20th centuries and, since 1985, have been selected for milk production. This doctoral dissertation aimed to characterize selection signatures in dairy cattle populations. The second chapter aimed to assess genetic diversity and identify selection signatures in Holstein cattle from North America (HOL), Gir cattle from India (GIR_IN), and Dairy Gir cattle from Brazil (GIR_BR). Genetic diversity was measured using nucleotide diversity, single-nucleotide variant density, minor allele frequency, observed and expected heterozygosity, and the inbreeding coefficient. Selection signatures were detected using Tajima’s D, integrated haplotype score (iHS), fixation index (FST), and cross-population extended haplotype homozygosity (XP-EHH) on autosomes, as well as Tajima’s D and iHS on the X chromosome. The HOL population showed lower diversity, while GIR_IN and GIR_BR presented greater variability. Genes previously associated with traits of economic importance were identified, including traits related to growth, reproduction, mastitis, milk production, heat tolerance, health, and adaptation. In addition, in the third chapter, we systematically reviewed studies published from 2016 to 2023 on selection signatures in dairy cattle. A total of 21 studies in 19 breeds (taurine, indicine, and composite) identified selection signatures overlapping with 5,099 genes. We discussed genes under selection related to economically important traits, such as fertility, health, milk production and composition, environmental adaptation, color patterns, and potential pleiotropic genes. Several genes were reported in numerous selection signature studies, such as PLAG1 and HMGA2. Although DGAT1 is an important gene for milk production and composition, it did not show evidence of selection in recent literature. The systematic review also discussed future perspectives involving molecular tools and integrative approaches. As a general conclusion, dairy cattle selection is shaped not only by economic traits but also by ecological and adaptive pressures. In both chapters, the genes under selection were linked to several traits, including growth, reproduction, milk production and components, heat tolerance, health, and adaptation. The study of selection signatures provides valuable information for genetic improvement and sustainable breeding. Keywords: dairy cattle, genes, genetic diversity, selection signatures 1 CHAPTER 01 – GENERAL CONSIDERATIONS 1 INTRODUCTION The identification of regions under selection and their consequences on the modification of species is one of the interests of genetic and animal breeding area. The combination of geographic separation and natural and artificial selection has shaped the genomic architecture of cattle, leading to genetic differentiation among bovine breeds. Therefore, this differentiation can shape patterns of genetic variation of selected regions, leading to selection signatures that provide information on genome-changing mechanisms and can be used to identify loci subject to selection (Campos et al. 2017). The study of selection signatures and genetic diversity contributes to understanding processes related to genome evolution (Nielsen et al. 2005). Furthermore, it may help the identification of causal variants that control the economically important phenotypes in cattle, such as milk production, reproduction, body conformation, diseases and parasites resistance, and behavior. In Brazil, the Dairy Gir cattle in the main dairy zebu breed, and Holstein is the primary dairy taurine breed. However, the genetic mechanism underlying selection signatures in these populations remains incompletely elucidated. Taurine cattle have higher performance but also larger environmental demands. Zebu cattle are better adapted to breeding conditions in the tropics. Taurine breeds have been selected for economic traits for a longer period and with greater intensity than zebu breeds, especially when referring to zebu populations from developed and sub-developed countries. The strong selection pressure may be the reason for the higher linkage disequilibrium (LD) level observed in Bos taurus than in Bos indicus animals (Thévenon et al. 2007; O’Brien et al. 2014). The detection of selection signatures is based on the frequency of alleles that are under selection or in adjacent haplotypes. When the frequency is higher, it may generate segments of consecutive homozygous genotypes (Ghoreishifar et al. 2020b, a). With the widespread use of reproductive technologies and increasing selection pressure, monitoring genetic diversity and selection signatures in dairy genetic resources is fundamental. This importance is recognized in Sustainable Development 2 Goal (SDG) 2.5, which aims to maintain genetic diversity. The aim of this thesis is to characterize selection signatures in dairy cattle populations. In Chapter 2 of this thesis, a study on selection signature and genetic diversity in Holstein and Dairy Gir cattle is presented. In Chapter 3, a systematic review of recent studies on selection signatures in dairy cattle is reported. 89 6 CONCLUSIONS This review provided a comprehensive overview of the current scenario of selection signatures in dairy cattle studies, including the selection of the most relevant traits for milk production worldwide and specific adaptive and disease resilience traits of dairy breeds. We highlighted important genes found to be under selection in taurine, indicine, and composite dairy cattle breeds. However, there is a lack of functional validation of candidate genes; these studies will enhance understanding in multiple fields of animal breeding and genetics. An alternative that can provide more information is the integration of GWAS and selection signature studies, as this may lead to gains in marker-enhanced selection and functional understanding of complex traits. Dairy cattle populations have not been selected only for traits that are present in the selection indices; rather, their genomes are also shaped by environmental pressures and the selection of desirable exterior traits, such as human preference regarding coat color. As selection indices evolve to incorporate sustainable traits such as feed efficiency and methane emission, the identification of selection signatures linked with these traits is encouraged in future generations. 90 7 REFERENCES Abbasi-Moshaii, B, Moradi, MH, Yin, T, Rahimi-Mianji, G, Nejati-Javaremi, A & König, S. 2023. Genome-wide scan for selective sweeps identifies novel loci associated with resistance to mastitis in German Holstein cattle. Journal of Animal Breeding and Genetics. 140(1):92–105. doi.org/10.1111/JBG.12737. Aguiar, TS, Torrecilha, RBP, Milanesi, M, Utsunomiya, ATH, Trigo, BB, Tijjani, A, Musa, HH, Lopes, FL, et al. 2018. Association of Copy Number Variation at Intron 3 of HMGA2 With Navel Length in Bos indicus. Frontiers in Genetics. 9:627. doi.org/10.3389/FGENE.2018.00627. Ajmone-Marsan, P, Garcia, JF & Lenstra, JA. 2010. On the origin of cattle: How aurochs became cattle and colonized the world. Evolutionary Anthropology. 19(4):148–157. doi.org/10.1002/evan.20267. Alvau, A, Battistone, MA, Gervasi, MG, Navarrete, FA, Xu, X, Sánchez-Cárdenas, C, de la Vega-Beltran, JL, da Ros, VG, et al. 2016. The tyrosine kinase FER is responsible for the capacitationassociated increase in tyrosine phosphorylation in murine sperm. Development (Cambridge). 143(13):2325–2333. doi.org/10.1242/DEV.136499/263999/AM/THE-TYROSINE-KINASE-FER-IS- RESPONSIBLE-FOR-THE. Amalfitano, N, Macedo Mota, LF, Rosa, GJM, Cecchinato, A & Bittante, G. 2022. Role of CSN2, CSN3, and BLG genes and the polygenic background in the cattle milk protein profile. Journal of Dairy Science. 105(7):6001–6020. doi.org/10.3168/JDS.2021-21421. Asselstine, V, Medrano, JF & Cánovas, A. 2022. Identification of novel alternative splicing associated with mastitis disease in Holstein dairy cows using large gap read mapping. BMC Genomics. 23(1):1–15. doi.org/10.1186/S12864-022-08430- X/FIGURES/5. Bahbahani, H, Salim, B, Almathen, F, Enezi, F Al, Mwacharo, JM & Hanotte, O. 2018. Signatures of positive selection in African Butana and Kenana dairy zebu cattle. PLOS ONE. 13(1):e0190446. doi.org/10.1371/JOURNAL.PONE.0190446. Bathala, P, Fereshteh, Z, Li, K, Al-Dossary, AA, Galileo, DS & Martin-DeLeon, PA. 2018. Oviductal extracellular vesicles (oviductosomes, OVS) are conserved in humans: murine OVS play a pivotal role in sperm capacitation and fertility. Molecular Human Reproduction. 24(3):143–157. doi.org/10.1093/MOLEHR/GAY003. Behl, JD, Verma, NK, Tyagi, N, Mishra, P, Behl, R & Joshi, BK. 2012. The Major Histocompatibility Complex in Bovines: A Review. International Scholarly Research Notices. 2012:1–12. doi.org/10.5402/2012/872710. Van Den Berg, I, Fritz, S, Rodriguez, S, Rocha, D, Boussaha, M, Lund, MS & Boichard, D. 2014. Concordance analysis for QTL detection in dairy cattle: A case 91 study of leg morphology. Genetics Selection Evolution. 46(1):1–14. doi.org/10.1186/1297-9686-46-31. Berryere, TG, Schmutz, SM, Schimpf, RJ, Cowan, CM & Potter, J. 2003. TYRP1 is associated with dun coat colour in Dexter cattle or how now brown cow? Animal Genetics. 34(3):169–175. doi.org/10.1046/J.1365-2052.2003.00985.X. Blott, S, Kim, JJ, Moisio, S, Schmidt-Küntzel, A, Cornet, A, Berzi, P, Cambisano, N, Ford, C, et al. 2003. Molecular Dissection of a Quantitative Trait Locus: A Phenylalanine-to-Tyrosine Substitution in the Transmembrane Domain of the Bovine Growth Hormone Receptor Is Associated With a Major Effect on Milk Yield and Composition. Genetics. 163(1):253–266. doi.org/10.1093/GENETICS/163.1.253. Bouwman, AC, Daetwyler, HD, Chamberlain, AJ, Ponce, CH, Sargolzaei, M, Schenkel, FS, Sahana, G, Govignon-Gion, A, et al. 2018. Meta-analysis of genome- wide association studies for cattle stature identifies common genes that regulate body size in mammals. Nature Genetics 2018 50:3. 50(3):362–367. doi.org/10.1038/s41588-018-0056-5. Bruford, MW, Bradley, DG & Luikart, G. 2003. DNA markers reveal the complexity of livestock domestication. Nature Reviews Genetics. 4(11):900–910. doi.org/10.1038/nrg1203. Buaban, S, Lengnudum, K, Boonkum, W & Phakdeedindan, P. 2022. Genome-wide association study on milk production and somatic cell score for Thai dairy cattle using weighted single-step approach with random regression test-day model. Journal of Dairy Science. 105(1):468–494. doi.org/10.3168/JDS.2020-19826. Campos, MA & Gazzinelli, RT. 2004. Trypanosoma cruzi and its components as exogenous mediators of inflammation recognized through Toll-like receptors. Mediators of Inflammation. 13(3):139–143. doi.org/10.1080/09511920410001713565. Carignano, HA, Roldan, DL, Beribe, MJ, Raschia, MA, Amadio, A, Nani, JP, Gutierrez, G, Alvarez, I, et al. 2018. Genome-wide scan for commons SNPs affecting bovine leukemia virus infection level in dairy cattle. BMC Genomics 2018 19:1. 19(1):1–15. doi.org/10.1186/S12864-018-4523-2. Caroli, AM, Chessa, S & Erhardt, GJ. 2009. Invited review: Milk protein polymorphisms in cattle: Effect on animal breeding and human nutrition. Journal of Dairy Science. 92(11):5335–5352. doi.org/10.3168/JDS.2009-2461. Cesarani, A, Sorbolini, S, Criscione, A, Bordonaro, S, Pulina, G, Battacone, G, Marletta, D, Gaspa, G, et al. 2018. Genome-wide variability and selection signatures in Italian island cattle breeds. Animal Genetics. 49(5):371–383. doi.org/10.1111/AGE.12697. Chen, M, Pan, D, Ren, H, Fu, J, Li, J, Su, G, Wang, A, Jiang, L, et al. 2016. Identification of selective sweeps reveals divergent selection between Chinese 92 Holstein and Simmental cattle populations. Genetics Selection Evolution. 48(1):76. doi.org/10.1186/s12711-016-0254-5. Cheruiyot, EK, Bett, RC, Amimo, JO, Zhang, Y, Mrode, R & Mujibi, FDN. 2018. Signatures of Selection in Admixed Dairy Cattle in Tanzania. Frontiers in Genetics. 9:405292. doi.org/10.3389/fgene.2018.00607. Cheruiyot, EK, Haile-Mariam, M, Cocks, BG, MacLeod, IM, Xiang, R & Pryce, JE. 2021. New loci and neuronal pathways for resilience to heat stress in cattle. Scientific Reports 2021 11:1. 11(1):1–16. doi.org/10.1038/s41598-021-95816-8. Chu, T, Dufort, I & Sirard, MA. 2012. Effect of ovarian stimulation on oocyte gene expression in cattle. Theriogenology. 77(9):1928–1938. doi.org/10.1016/J.THERIOGENOLOGY.2012.01.015. Cochran, SD, Cole, JB, Null, DJ & Hansen, PJ. 2013. Discovery of single nucleotide polymorphisms in candidate genes associated with fertility and production traits in Holstein cattle. BMC Genetics. 14(1):49. doi.org/10.1186/1471-2156-14-49. Cohen-Zinder, M, Seroussi, E, Larkin, DM, Loor, JJ, Everts-Van Der Wind, A, Lee, JH, Drackley, JK, Band, MR, et al. 2005. Identification of a missense mutation in the bovine ABCG2 gene with a major effect on the QTL on chromosome 6 affecting milk yield and composition in Holstein cattle. Genome Research. 15(7):936–944. doi.org/10.1101/GR.3806705. Cruz, VAR, Oliveira, HR, Brito, LF, Fleming, A, Larmer, S, Miglior, F & Schenkel, FS. 2019. Genome-Wide Association Study for Milk Fatty Acids in Holstein Cattle Accounting for the DGAT1 Gene Effect. Animals 2019, Vol. 9, Page 997. 9(11):997. doi.org/10.3390/ANI9110997. Do, DN, Bissonnette, N, Lacasse, P, Miglior, F, Sargolzaei, M, Zhao, X & Ibeagha- Awemu, EM. 2017. Genome-wide association analysis and pathways enrichment for lactation persistency in Canadian Holstein cattle. Journal of Dairy Science. 100(3):1955–1970. doi.org/10.3168/JDS.2016-11910. Engle, BN & Hayes, BJ. 2022. Genetic variation in PLAG1 is associated with early fertility in Australian Brahman cattle. Journal of Animal Science. 100(4). doi.org/10.1093/JAS/SKAC084. Esfahani, EN, Ansari Mahyari, S, Davoodi, P, Ghaderi-Zefrehei, M & Lesch, BJ. 2025. Decoding cattle (Bos taurus) diacylglycerol acyltransferase (DGAT) gene families: A pathway to functional understanding. Journal of Heredity. (February, 11). doi.org/10.1093/JHERED/ESAE079. FAO. 2015. The Second Report on the State of the World’s Animal Genetic Resources for Food and Agriculture. B.D. Scherf & D. Pilling, Eds. Rome: FAO Commission on Genetic Resources for Food and Agriculture Assessments. doi.org/10.4060/I4787E. 93 Fay, JC & Wu, C-I. 2000. Hitchhiking Under Positive Darwinian Selection. Genetics. 155(3):1405–1413. doi.org/10.1093/genetics/155.3.1405. Fink, T, Tiplady, K, Lopdell, T, Johnson, T, Snell, RG, Spelman, RJ, Davis, SR & Littlejohn, MD. 2017. Functional confirmation of PLAG1 as the candidate causative gene underlying major pleiotropic effects on body weight and milk characteristics. Scientific Reports 2017 7:1. 7(1):1–8. doi.org/10.1038/srep44793. Fonseca, PAS, Suárez-Vega, A, Marras, G & Cánovas, Á. 2020. GALLO: An R package for genomic annotation and integration of multiple data sources in livestock for positional candidate loci. GigaScience. 9(12):1–9. doi.org/10.1093/gigascience/giaa149. Fonseca, PAS, Suárez-Vega, A & Cánovas, A. 2020. Weighted Gene Correlation Network Meta-Analysis Reveals Functional Candidate Genes Associated with High- and Sub-Fertile Reproductive Performance in Beef Cattle. Genes 2020, Vol. 11, Page 543. 11(5):543. doi.org/10.3390/GENES11050543. Fortes, MRS, Kemper, K, Sasazaki, S, Reverter, A, Pryce, JE, Barendse, W, Bunch, R, McCulloch, R, et al. 2013. Evidence for pleiotropism and recent selection in the PLAG1 region in Australian Beef cattle. Animal Genetics. 44(6):636–647. doi.org/10.1111/AGE.12075. Gao, Y, Marceau, A, Iqbal, V, Torres-Vázquez, JA, Neupane, M, Jiang, J, Liu, GE & Ma, L. 2023. Genome-wide association analysis of heifer livability and early first calving in Holstein cattle. BMC Genomics. 24(1):628. doi.org/10.1186/s12864-023- 09736-0. Gautason, E, Schönherz, AA, Sahana, G & Guldbrandtsen, B. 2021. Genomic inbreeding and selection signatures in the local dairy breed Icelandic Cattle. Animal Genetics. 52(3):251–262. doi.org/10.1111/AGE.13058. Ghahramani, N, Shodja, J, Rafat, SA, Panahi, B & Hasanpur, K. 2021. Integrative Systems Biology Analysis Elucidates Mastitis Disease Underlying Functional Modules in Dairy Cattle. Frontiers in Genetics. 12:712306. doi.org/10.3389/FGENE.2021.712306. Ghoreishifar, SM, Eriksson, S, Johansson, AM, Khansefid, M, Moghaddaszadeh- Ahrabi, S, Parna, N, Davoudi, P & Javanmard, A. 2020. Signatures of selection reveal candidate genes involved in economic traits and cold acclimation in five Swedish cattle breeds. Genetics Selection Evolution. 52(1):1–15. doi.org/10.1186/s12711-020-00571-5. Girardot, M, Guibert, S, Laforet, MP, Gallard, Y, Larroque, H & Oulmouden, A. 2006. The insertion of a full-length Bos taurus LINE element is responsible for a transcriptional deregulation of the Normande Agouti gene. Pigment Cell Research. 19(4):346–355. doi.org/10.1111/J.1600-0749.2006.00312.X. 94 Gonzalez Berrios, CL, Bowden, CF, Saad, HM, Bishop, J V., Van Campen, H, Pinedo, P, Hansen, TR & Thomas, MG. 2024. Identification of candidate SNPs associated with embryo mortality and fertility traits in lactating Holstein cows. Frontiers in Genetics. 15:1409335. doi.org/10.3389/FGENE.2024.1409335. Gonzalez-Prendes, R, Ginja, C, Kantanen, J, Ghanem, N, Kugonza, DR, Makgahlela, ML, Groenen, MAM & Crooijmans, RPMA. 2022. Integrative QTL mapping and selection signatures in Groningen White Headed cattle inferred from whole-genome sequences. PLOS ONE. 17(10):e0276309. doi.org/10.1371/JOURNAL.PONE.0276309. Goyal, R, Van Wickle, J, Goyal, D, Matei, N & Longo, LD. 2013. Antenatal Maternal Long-Term Hypoxia: Acclimatization Responses with Altered Gene Expression in Ovine Fetal Carotid Arteries. PLOS ONE. 8(12):e82200. doi.org/10.1371/JOURNAL.PONE.0082200. Grisart, B, Coppieters, W, Farnir, F, Karim, L, Ford, C, Berzi, P, Cambisano, N, Mni, M, et al. 2002. Positional Candidate Cloning of a QTL in Dairy Cattle: Identification of a Missense Mutation in the Bovine DGAT1 Gene with Major Effect on Milk Yield and Composition. Genome Research. 12(2):222–231. doi.org/10.1101/GR.224202. Gutiérrez-Gil, B, Arranz, JJ & Wiener, P. 2015. An interpretive review of selective sweep studies in Bos taurus cattle populations: Identification of unique and shared selection signals across breeds. Frontiers in Genetics. 6(MAY):167. doi.org/10.3389/FGENE.2015.00167. Habib, WA, Brioude, F, Edouard, T, Bennett, JT, Lienhardt-Roussie, A, Tixier, F, Salem, J, Yuen, T, et al. 2018. Genetic disruption of the oncogenic HMGA2–PLAG1– IGF2 pathway causes fetal growth restriction. Genetics in Medicine. 20(2):250–258. doi.org/10.1038/GIM.2017.105. Hawken, RJ, Zhang, YD, Fortes, MRS, Collis, E, Barris, WC, Corbet, NJ, Williams, PJ, Fordyce, G, et al. 2012. Genome-wide association studies of female reproduction in tropically adapted beef cattle. Journal of Animal Science. 90(5):1398–1410. doi.org/10.2527/JAS.2011-4410. Huson, HJ, Sonstegard, TS, Godfrey, J, Hambrook, D, Wolfe, C, Wiggans, G, Blackburn, H & VanTassell, CP. 2020. A Genetic Investigation of Island Jersey Cattle, the Foundation of the Jersey Breed: Comparing Population Structure and Selection to Guernsey, Holstein, and United States Jersey Cattle. Frontiers in Genetics. 11:519719. doi.org/10.3389/FGENE.2020.00366. Illa, SK, Mukherjee, S, Nath, S & Mukherjee, A. 2021. Genome-Wide Scanning for Signatures of Selection Revealed the Putative Genomic Regions and Candidate Genes Controlling Milk Composition and Coat Color Traits in Sahiwal Cattle. Frontiers in Genetics. 12:1193. doi.org/10.3389/fgene.2021.699422. Iso-Touru, T, Tapio, M, Vilkki, J, Kiseleva, T, Ammosov, I, Ivanova, Z, Popov, R, Ozerov, M, et al. 2016. Genetic diversity and genomic signatures of selection among 95 cattle breeds from Siberia, eastern and northern Europe. Animal Genetics. 47(6):647–657. doi.org/10.1111/AGE.12473. Jensen, JD, Foll, M & Bernatchez, L. 2016. The past, present and future of genomic scans for selection. Molecular Ecology. 25(1):1–4. doi.org/https://doi.org/10.1111/mec.13493. Jin, L, Qu, K, Hanif, Q, Zhang, J, Liu, J, Chen, N, Suolang, Q, Lei, C, et al. 2022. Whole-Genome Sequencing of Endangered Dengchuan Cattle Reveals Its Genomic Diversity and Selection Signatures. Frontiers in Genetics. 13:611. doi.org/10.3389/fgene.2022.833475. Karim, L, Takeda, H, Lin, L, Druet, T, Arias, JAC, Baurain, D, Cambisano, N, Davis, SR, et al. 2011. Variants modulating the expression of a chromosome domain encompassing PLAG1 influence bovine stature. Nature Genetics 2011 43:5. 43(5):405–413. doi.org/10.1038/ng.814. Kaune, H, Montiel, JF, Fenwick, M & Williams, SA. 2022. Rapid ovarian transcript changes during the onset of premature ovarian insufficiency in a mouse model. Reproduction and Fertility. 3(3):173–186. doi.org/10.1530/RAF-22-0036. Kolbehdari, D, Wang, Z, Grant, JR, Murdoch, B, Prasad, A, Xiu, Z, Marques, E, Stothard, P, et al. 2009. A whole genome scan to map QTL for milk production traits and somatic cell score in Canadian Holstein bulls. Journal of Animal Breeding and Genetics. 126(3):216–227. doi.org/10.1111/J.1439-0388.2008.00793.X. Laodim, T, Koonawootrittriron, S, Elzo, MA, Suwanasopee, T, Jattawa, D & Sarakul, M. 2023. Genetic factors influencing milk and fat yields in tropically adapted dairy cattle: insights from quantitative trait loci analysis and gene associations. Animal Bioscience. 37(4):576–590. doi.org/10.5713/AB.23.0246. Lewontin, RC & Krakauer, J. 1973. Distribution of gene frequency as a test of the theory of the selective neutrality of polymorphisms. Genetics. 74(1):175–195. doi.org/10.1093/GENETICS/74.1.175. Li, R, Zhang, CL, Liao, XX, Chen, D, Wang, WQ, Zhu, YH, Geng, XH, Ji, DJ, et al. 2015. Transcriptome MicroRNA Profiling of Bovine Mammary Glands Infected with Staphylococcus aureus. International Journal of Molecular Sciences 2015, Vol. 16, Pages 4997-5013. 16(3):4997–5013. doi.org/10.3390/IJMS16034997. Li, RW, Rinaldi, M & Capuco, A V. 2011. Characterization of the abomasal transcriptome for mechanisms of resistance to gastrointestinal nematodes in cattle. Veterinary Research. 42(1):1–11. doi.org/10.1186/1297-9716-42-114. Lillepea, K, Juchnewitsch, AG, Kasak, L, Valkna, A, Dutta, A, Pomm, K, Poolamets, O, Nagirnaja, L, et al. 2024. Toward clinical exomes in diagnostics and management of male infertility. The American Journal of Human Genetics. 111(5):877–895. doi.org/10.1016/J.AJHG.2024.03.013. 96 Littlejohn, M, Grala, T, Sanders, K, Walker, C, Waghorn, G, MacDonald, K, Coppieters, W, Georges, M, et al. 2012. Genetic variation in PLAG1 associates with early life body weight and peripubertal weight and growth in Bos taurus. Animal Genetics. 43(5):591–594. doi.org/10.1111/J.1365-2052.2011.02293.X. Littlejohn, MD, Tiplady, K, Fink, TA, Lehnert, K, Lopdell, T, Johnson, T, Couldrey, C, Keehan, M, et al. 2016. Sequence-based Association Analysis Reveals an MGST1 eQTL with Pleiotropic Effects on Bovine Milk Composition. Scientific Reports 2016 6:1. 6(1):1–14. doi.org/10.1038/srep25376. Liu, D, Chen, Z, Zhao, W, Guo, L, Sun, H, Zhu, K, Liu, G, Shen, X, et al. 2021. Genome-wide selection signatures detection in Shanghai Holstein cattle population identified genes related to adaption, health and reproduction traits. BMC Genomics. 22(1):747. doi.org/10.1186/s12864-021-08042-x. Liu, L, Zhou, J, Chen, CJ, Zhang, J, Wen, W, Tian, J, Zhang, Z & Gu, Y. 2020. GWAS-Based Identification of New Loci for Milk Yield, Fat, and Protein in Holstein Cattle. Animals 2020, Vol. 10, Page 2048. 10(11):2048. doi.org/10.3390/ANI10112048. Loftus, RT, MacHugh, DE, Bradley, DG, Sharp, PM & Cunningham, P. 1994. Evidence for two independent domestications of cattle. Proceedings of the National Academy of Sciences. 91(7):2757–2761. doi.org/10.1073/pnas.91.7.2757. Lopez, NC, Ebensperger, G, Herrera, EA, Reyes, R V., Calaf, G, Cabello, G, Moraga, FA, Beñaldo, FA, et al. 2016. Role of the RhoA/ROCK pathway in high-altitude associated neonatal pulmonary hypertension in lambs. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology. 310(11):R1053– R1063. doi.org/10.1152/ajpregu.00177.2015. Ma, Y, Ding, X, Qanbari, S, Weigend, S, Zhang, Q & Simianer, H. 2015. Properties of different selection signature statistics and a new strategy for combining them. Heredity. 115(5):426–436. doi.org/10.1038/hdy.2015.42. MacHugh, DE, Larson, G & Orlando, L. 2017. Taming the Past: Ancient DNA and the Study of Animal Domestication. Annual Review of Animal Biosciences. 5(1):329–351. doi.org/10.1146/annurev-animal-022516-022747. Mariani, E, Malacarne, M, Cipolat-Gotet, C, Cecchinato, A, Bittante, G & Summer, A. 2022. Prediction of fresh and ripened cheese yield using detailed milk composition and udder health indicators from individual Brown Swiss cows. Frontiers in Veterinary Science. 9:1012251. doi.org/10.3389/FVETS.2022.1012251. Martin, P, Szymanowska, M, Zwierzchowski, L & Leroux, C. 2002. The impact of genetic polymorphisms on the protein composition of ruminant milks. Reproduction Nutrition Development. 42(5):433–459. doi.org/10.1051/RND:2002036. Mbarek, H, Gordon, SD, Duffy, DL, Hubers, N, Mortlock, S, Beck, JJ, Hottenga, JJ, Pool, R, et al. 2024. Genome-wide association study meta-analysis of dizygotic 97 twinning illuminates genetic regulation of female fecundity. Human Reproduction. 39(1):240–257. doi.org/10.1093/HUMREP/DEAD247. Misztal, I & Lourenco, D. 2024. Potential negative effects of genomic selection. Journal of Animal Science. 102. doi.org/10.1093/JAS/SKAE155. Mota, LFM, Lopes, FB, Fernandes Júnior, GA, Rosa, GJM, Magalhães, AFB, Carvalheiro, R & Albuquerque, LG. 2020. Genome-wide scan highlights the role of candidate genes on phenotypic plasticity for age at first calving in Nellore heifers. Scientific Reports. 10(1):6481. doi.org/10.1038/s41598-020-63516-4. Mota, LFM, Carvajal, AB, Bernardes, PA, Buzanskas, ME, Baldi, F, Lôbo, RB & Munari, DP. 2022. Integrating genome-wide association study and pathway analysis reveals physiological aspects affecting heifer early calving defined at different ages in Nelore cattle. Genomics. 114(4):110395. doi.org/10.1016/J.YGENO.2022.110395. Moyer, AL & Wagner, KR. 2015. Mammalian Mss51 is a Skeletal Muscle-Specific Gene Modulating Cellular Metabolism. Journal of Neuromuscular Diseases. 2(4):371–385. doi.org/10.3233/JND-150119. Mwenya, WNM. 1993. The impact of the introduction of exotic cattle in east and southern Africa. In: Proceedings of the Future of Livestock Industries in East and Southern Africa Workshop Held at Kadoma Ranch Hotel, Zimbabwe. Addis Ababa: International Livestock Centre for Africa. Nayak, SS, Panigrahi, M, Kumar, H, Rajawat, D, Sharma, A, Bhushan, B & Dutt, T. 2023. Evidence for selective sweeps in the MHC gene repertoire of various cattle breeds. Animal Biotechnology. 34(8):4167–4173. doi.org/10.1080/10495398.2023.2196317. Nayak, SS, Panigrahi, M, Rajawat, D, Ghildiyal, K, Sharma, A, Parida, S, Bhushan, B, Mishra, BP, et al. 2023. Comprehensive selection signature analyses in dairy cattle exploiting purebred and crossbred genomic data. Mammalian Genome. 34(4):615–631. doi.org/10.1007/S00335-023-10021-4. OECD-FAO. 2022. OECD-FAO Agricultural Outlook 2022-2031. (OECD-FAO Agricultural Outlook). Paris: OECD Publishing. doi.org/10.1787/f1b0b29c-en. Ortega, MS, Denicol, AC, Cole, JB, Null, DJ & Hansen, PJ. 2016. Use of single nucleotide polymorphisms in candidate genes associated with daughter pregnancy rate for prediction of genetic merit for reproduction in Holstein cows. Animal Genetics. 47(3):288–297. doi.org/10.1111/AGE.12420. Parra-Bracamonte, GM, Vázquez-Armijo, JF, Rosa-Reyna, XFD la, Magaña- Monforte, JG, Martínez-González, JC, Lopez-Villalobos, N, Herrera-Ojeda, JB & Pacheco-Contreras, VI. 2025. Frequency of genetic variations associated with milk yield and composition in seven Zebu breeds. The Journal of Agricultural Science. (June, 27):1–22. doi.org/10.1017/S0021859625100154. 98 Pausch, H, Wang, X, Jung, S, Krogmeier, D, Edel, C, Emmerling, R, Götz, KU & Fries, R. 2012. Identification of QTL for UV-Protective Eye Area Pigmentation in Cattle by Progeny Phenotyping and Genome-Wide Association Analysis. PLOS ONE. 7(5):e36346. doi.org/10.1371/JOURNAL.PONE.0036346. Pazhanivel, N & Umarani, R. 2022. Current Status of Bovine Ocular Squamous Cell Carcinoma. Indian Journal of Veterinary Pathology. 46(3):183–192. doi.org/10.5958/0973-970X.2022.00031.1. Pedrosa, VB, Schenkel, FS, Chen, SY, Oliveira, HR, Casey, TM, Melka, MG & Brito, LF. 2021. Genomewide association analyses of lactation persistency and milk production traits in holstein cattle based on imputed whole-genome sequence data. Genes. 12(11):1830. doi.org/10.3390/GENES12111830/S1. Pereira, M & Gazzinelli, RT. 2023. Regulation of innate immune signaling by IRAK proteins. Frontiers in Immunology. 14:1133354. doi.org/10.3389/FIMMU.2023.1133354. Persichilli, C, Senczuk, G, Mastrangelo, S, Marusi, M, van Kaam, JT, Finocchiaro, R, Di Civita, M, Cassandro, M, et al. 2023. Exploring genome-wide differentiation and signatures of selection in Italian and North American Holstein populations. Journal of Dairy Science. 106(8):5537–5553. doi.org/10.3168/JDS.2022-22159. Peterson, H, Kolberg, L, Raudvere, U, Kuzmin, I & Vilo, J. 2020. gprofiler2 -- an R package for gene list functional enrichment analysis and namespace conversion toolset g:Profiler. F1000Research 2020 9:709. 9:709. doi.org/10.12688/f1000research.24956.2. Pitt, D, Sevane, N, Nicolazzi, EL, MacHugh, DE, Park, SDE, Colli, L, Martinez, R, Bruford, MW, et al. 2019. Domestication of cattle: Two or three events? Evolutionary Applications. 12(1):123–136. doi.org/https://doi.org/10.1111/eva.12674. Qanbari, S, Pimentel, ECG, Tetens, J, Thaller, G, Lichtner, P, Sharifi, AR & Simianer, H. 2010. A genome-wide scan for signatures of recent selection in Holstein cattle. Animal Genetics. 41(4):377–389. doi.org/10.1111/J.1365-2052.2009.02016.X. Rajawat, D, Panigrahi, M, Kumar, H, Nayak, SS, Parida, S, Bhushan, B, Gaur, GK, Dutt, T, et al. 2022. Identification of important genomic footprints using eight different selection signature statistics in domestic cattle breeds. Gene. 816:146165. doi.org/10.1016/J.GENE.2021.146165. Rajawat, D, Panigrahi, M, Nayak, SS, Ghildiyal, K, Sharma, A, Kumar, H, Parida, S, Bhushan, B, et al. 2023. Uncovering genes underlying coat color variation in indigenous cattle breeds through genome-wide positive selection. Animal Biotechnology. 34(8):3920–3933. doi.org/10.1080/10495398.2023.2240387. Rajput, YS & Sharma, R. 2023. Enzymes Beyond Traditional Applications in Dairy Science and Technology. Elsevier. doi.org/10.1016/C2021-0-02498-6. 99 Ramey, HR, Decker, JE, McKay, SD, Rolf, MM, Schnabel, RD & Taylor, JF. 2013. Detection of selective sweeps in cattle using genome-wide SNP data. BMC Genomics. 14(1):1–18. doi.org/10.1186/1471-2164-14-382. Randhawa, IAS, Khatkar, MS, Thomson, PC & Raadsma, HW. 2016. A Meta- Assembly of Selection Signatures in Cattle. PLOS ONE. 11(4):e0153013. doi.org/10.1371/JOURNAL.PONE.0153013. Raney, BJ, Barber, GP, Benet-Pagès, A, Casper, J, Clawson, H, Cline, MS, Diekhans, M, Fischer, C, et al. 2024. The UCSC Genome Browser database: 2024 update. Nucleic Acids Research. 52(D1):D1082–D1088. doi.org/10.1093/NAR/GKAD987. Raven, LA, Cocks, BG & Hayes, BJ. 2014. Multibreed genome wide association can improve precision of mapping causative variants underlying milk production in dairy cattle. BMC Genomics. 15(1):1–14. doi.org/10.1186/1471-2164-15-62. Refoyo-Martínez, A, Da Fonseca, RR, Halldórsdóttir, K, Árnason, E, Mailund, T & Racimo, F. 2019. Identifying loci under positive selection in complex population histories. Genome Research. 29(9):1506–1520. doi.org/10.1101/GR.246777.118. Rhoads, RP, La Noce, AJ, Wheelock, JB & Baumgard, LH. 2011. Short communication: Alterations in expression of gluconeogenic genes during heat stress and exogenous bovine somatotropin administration. Journal of Dairy Science. 94(4):1917–1921. doi.org/10.3168/JDS.2010-3722. Rocha, R de FB, Garcia, AO, Otto, PI, da Silva, MVB, Martins, MF, Machado, MA, Panetto, JC do C & Guimarães, SEF. 2023. Runs of homozygosity and signatures of selection for number of oocytes and embryos in the Gir Indicine cattle. Mammalian Genome 2023. 1:1–15. doi.org/10.1007/S00335-023-09989-W. Ruvinskiy, D, Igoshin, A, Yurchenko, A, Ilina, A V. & Larkin, DM. 2022. Resequencing the Yaroslavl cattle genomes reveals signatures of selection and a rare haplotype on BTA28 likely to be related to breed phenotypes. Animal Genetics. 53(5):680–684. doi.org/10.1111/AGE.13230. Sabeti, PC, Reich, DE, Higgins, JM, Levine, HZP, Richter, DJ, Schaffner, SF, Gabriel, SB, Platko, J V., et al. 2002. Detecting recent positive selection in the human genome from haplotype structure. Nature. 419(6909):832–837. doi.org/10.1038/nature01140. Saravanan, KA, Panigrahi, M, Kumar, H, Parida, S, Bhushan, B, Gaur, GK, Dutt, T, Mishra, BP, et al. 2021. Genomic scans for selection signatures revealed candidate genes for adaptation and production traits in a variety of cattle breeds. Genomics. 113(3):955–963. doi.org/10.1016/J.YGENO.2021.02.009. Schiavo, G, Bertolini, F, Galimberti, G, Bovo, S, Dall’Olio, S, Nanni Costa, L, Gallo, M & Fontanesi, L. 2020. A machine learning approach for the identification of 100 population-informative markers from high-throughput genotyping data: application to several pig breeds. Animal. 14(2):223–232. doi.org/10.1017/S1751731119002167. Schmidtmann, C, Schönherz, A, Guldbrandtsen, B, Marjanovic, J, Calus, M, Hinrichs, D & Thaller, G. 2021. Assessing the genetic background and genomic relatedness of red cattle populations originating from Northern Europe. Genetics Selection Evolution 2021 53:1. 53(1):1–18. doi.org/10.1186/S12711-021-00613-6. Sharma, R, Ahlawat, S, Aggarwal, RAK, Dua, A, Sharma, V & Tantia, MS. 2018. Comparative milk metabolite profiling for exploring superiority of indigenous Indian cow milk over exotic and crossbred counterparts. Journal of Food Science and Technology. 55(10):4232–4243. doi.org/10.1007/s13197-018-3360-2. Shen, S, Zhu, L, Yang, Y, Bi, Y, Li, J, Wang, Y, Pan, C, Wang, S, et al. 2024. Exploration of the Polymorphism Distribution of Bovine HMGA2 Gene in Worldwide Breeds and Its Associations with Ovarian Traits. Animals. 14(5):796. doi.org/10.3390/ANI14050796/S1. Sigdel, A, Abdollahi-Arpanahi, R, Aguilar, I & Peñagaricano, F. 2019. Whole Genome Mapping Reveals Novel Genes and Pathways Involved in Milk Production Under Heat Stress in US Holstein Cows. Frontiers in Genetics. 10:473150. doi.org/10.3389/FGENE.2019.00928. Signer-Hasler, H, Burren, A, Neuditschko, M, Frischknecht, M, Garrick, D, Stricker, C, Gredler, B, Bapst, B, et al. 2017. Population structure and genomic inbreeding in nine Swiss dairy cattle populations. Genetics Selection Evolution. 49(1):83. doi.org/10.1186/s12711-017-0358-6. Da Silva, MVGB, Sonstegard, TS, Thallman, RM, Connor, EE, Schnabel, RD & Van Tassell, CP. 2010. Characterization of DGAT1 Allelic Effects in a Sample of North American Holstein Cattle. Animal Biotechnology. 21(2):88–99. doi.org/10.1080/10495390903504625. Singh, A, Mehrotra, A, Gondro, C, Romero, AR da S, Pandey, AK, Karthikeyan, A, Bashir, A, Mishra, BP, et al. 2020. Signatures of Selection in Composite Vrindavani Cattle of India. Frontiers in Genetics. 11:1687. doi.org/10.3389/FGENE.2020.589496. Singh, H, Kumar, R, Mazumder, A, Salahuddin, Mazumder, R & Abdullah, MohdM. 2022. Insights into Interactions of Human Cytochrome P450 17A1: A Review. Current Drug Metabolism. 23(3):172–187. doi.org/10.2174/1389200223666220401093833. Taye, M, Lee, W, Jeon, S, Yoon, J, Dessie, T, Hanotte, O, Mwai, OA, Kemp, S, et al. 2017. Exploring evidence of positive selection signatures in cattle breeds selected for different traits. Mammalian Genome. 28(11):528–541. doi.org/10.1007/s00335-017- 9715-6. Tijjani, A, Utsunomiya, YT, Ezekwe, AG, Nashiru, O & Hanotte, O. 2019. Genome sequence analysis reveals selection signatures in endangered trypanotolerant West 101 African Muturu cattle. Frontiers in Genetics. 10(JUN):434317. doi.org/10.3389/FGENE.2019.00442. Tijjani, A, Salim, B, da Silva, MVB, Eltahir, HA, Musa, TH, Marshall, K, Hanotte, O & Musa, HH. 2022. Genomic signatures for drylands adaptation at gene-rich regions in African zebu cattle. Genomics. 114(4):110423. doi.org/10.1016/J.YGENO.2022.110423. Utsunomiya, YT, do Carmo, AS, Carvalheiro, R, Neves, HH, Matos, MC, Zavarez, LB, Pérez O’Brien, AM, Sölkner, J, et al. 2013. Genome-wide association study for birth weight in Nellore cattle points to previously described orthologous genes affecting human and bovine height. BMC Genetics. 14(1):52. doi.org/10.1186/1471- 2156-14-52. Utsunomiya, YT, Pérez O’Brien, AM, Sonstegard, TS, Sölkner, J & Garcia, JF. 2015. Genomic data as the “hitchhiker’s guide” to cattle adaptation: tracking the milestones of past selection in the bovine genome. Frontiers in Genetics. 6. doi.org/10.3389/fgene.2015.00036. Utsunomiya, YT, Milanesi, M, Utsunomiya, ATH, Torrecilha, RBP, Kim, E-S, Costa, MS, Aguiar, TS, Schroeder, S, et al. 2017. A PLAG1 mutation contributed to stature recovery in modern cattle. Scientific Reports. 7(1):17140. doi.org/10.1038/s41598- 017-17127-1. Velayudhan, SM, Yin, T, Alam, S, Brügemann, K, Sejian, V, Bhatta, R, Schlecht, E & König, S. 2023. Unraveling the Genomic Association for Milk Production Traits and Signatures of Selection of Cattle in a Harsh Tropical Environment. Biology. 12(12):1483. doi.org/10.3390/BIOLOGY12121483/S1. Velayudhan, SM, Alam, S, Yin, T, Brügemann, K, Buerkert, A, Sejian, V, Bhatta, R, Schlecht, E, et al. 2023. Selective Sweeps in Cattle Genomes in Response to the Influence of Urbanization and Environmental Contamination. Genes. 14(11):2083. doi.org/10.3390/GENES14112083/S1. Waerner, T, Gardellin, P, Pfizenmaier, K, Weith, A & Kraut, N. 2001. Human RERE Is Localized to Nuclear Promyelocytic Leukemia Oncogenic Domains and Enhances Apoptosis. Cell Growth & Differentiation. 12(4):201–210. Wang, H, Gui, H, Rallo, MS, Xu, Z & Matise, MP. 2017. Atrophin protein RERE positively regulates Notch targets in the developing vertebrate spinal cord. Journal of Neurochemistry. 141(3):347–357. doi.org/10.1111/JNC.13969. Waters, SM, McCabe, MS, Howard, DJ, Giblin, L, Magee, DA, MacHugh, DE & Berry, DP. 2011. Associations between newly discovered polymorphisms in the Bos taurusgrowth hormone receptor gene and performance traits in Holstein–Friesian dairy cattle. Animal Genetics. 42(1):39–49. doi.org/10.1111/J.1365- 2052.2010.02087.X. 102 Wheelock, JB, Rhoads, RP, VanBaale, MJ, Sanders, SR & Baumgard, LH. 2010. Effects of heat stress on energetic metabolism in lactating Holstein cows. Journal of Dairy Science. 93(2):644–655. doi.org/10.3168/JDS.2009-2295. Worku, D, Gowane, GR, Mukherjee, A, Alex, R, Joshi, P & Verma, A. 2022. Associations between polymorphisms of LAP3 and SIRT1 genes with clinical mastitis and milk production traits in Sahiwal and Karan Fries dairy cattle. Veterinary Medicine and Science. 8(6):2593–2604. doi.org/10.1002/VMS3.924. Worku, D, Gowane, G & Verma, A. 2023. Genetic variation in promoter region of the bovine LAP3 gene associated with estimated breeding values of milk production traits and clinical mastitis in dairy cattle. PLOS ONE. 18(5):e0277156. doi.org/10.1371/JOURNAL.PONE.0277156. Wu, C. 1995. Heat Shock Transcription Factors: Structure and Regulation. Annual Review of Cell and Developmental Biology. 11(1):441–469. doi.org/10.1146/annurev.cb.11.110195.002301. Yuan, P, He, Z, Zheng, L, Wang, W, Li, Y, Zhao, H, Zhang, VW, Zhang, Q, et al. 2017. Genetic evidence of ‘genuine’ empty follicle syndrome: a novel effective mutation in the LHCGR gene and review of the literature. Human Reproduction. 32(4):944–953. doi.org/10.1093/HUMREP/DEX015. Zhang, Q, Calus, MPL, Bosse, M, Sahana, G, Lund, MS & Guldbrandtsen, B. 2018. Human-Mediated Introgression of Haplotypes in a Modern Dairy Cattle Breed. Genetics. 209(4):1305–1317. doi.org/10.1534/GENETICS.118.301143. Zheng, X, Ju, Z, Wang, J, Li, Q, Huang, J, Zhang, A, Zhong, J & Wang, C. 2011. Single nucleotide polymorphisms, haplotypes and combined genotypes of LAP3 gene in bovine and their association with milk production traits. Molecular Biology Reports. 38(6):4053–4061. doi.org/10.1007/s11033-010-0524-1. Zielke, LG, Bortfeldt, RH, Reissmann, M, Tetens, J, Thaller, G & Brockmann, GA. 2013. Impact of Variation at the FTO Locus on Milk Fat Yield in Holstein Dairy Cattle. PLOS ONE. 8(5):e63406. doi.org/10.1371/JOURNAL.PONE.0063406. Zinovieva, NA, Dotsev, AV, Sermyagin, AA, Deniskova, TE, Abdelmanova, AS, Kharzinova, VR, Sölkner, J, Reyer, H, et al. 2020. Selection signatures in two oldest Russian native cattle breeds revealed using high-density single nucleotide polymorphism analysis. PLOS ONE. 15(11):e0242200. doi.org/10.1371/JOURNAL.PONE.0242200. Zuleica Trujano-Chavez, M, Ruíz-Flores, A, López-Ordaz, R, Pérez-Rodríguez, P, Chapingo, A, de México, E & en Producción, P. 2022. Genetic diversity in reproductive traits of Braunvieh cattle determined with SNP markers. Veterinary Medicine and Science. 8(4):1709–1720. doi.org/10.1002/VMS3.836.