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Current IssueSpecial IssueEditorial BoardOnline First ArticlesArchive

Review Article

Role of Marker-assisted Selection for Genetic Improvement in Livestock: A Review

A
Aakriti Sharma1
B
Bula Das1
A
Arundhati Phookan1
A
Ankita Gogoi1
O
Olympica Sarma2,*
1Department of Animal Genetics and Breeding, College of Veterinary Science, Assam Agricultural University, Khanapara, Guwahati-781 001, Assam, India.
2Department of Animal Genetics and Breeding, Sanskaram College of Veterinary and Animal Science, Sanskaram University, Patauda-124 108, Haryana, India.

  • Submitted11-06-2025|

  • Accepted01-04-2026|

  • First Online 29-04-2026|

  • doi 10.18805/BKAP862

ABSTRACT

Marker Assisted Selection (MAS) is a valuable tool in livestock breeding that helps improve desirable traits by using genetic markers linked to specific genes. Unlike traditional methods that rely on visible traits, MAS allows selection based on genetic makeup, making it more accurate and efficient. Introduced in the late 20th century, MAS has evolved significantly with the integration of genomic technologies, advanced statistical modelling and gene-editing techniques. The key advantage of MAS is that it enables early selection, even before an animal’s phenotype is fully expressed, thus speeding up the breeding process and improving genetic gain. MAS has been successfully applied in various livestock species, including cattle, pigs and poultry, to enhance production traits like growth rate, feed efficiency, milk yield and disease resistance. The future of MAS lies in its integration with advanced genomic technologies, enabling precise selection for complex traits across diverse species. Continued advancements in gene editing and data analytics will enhance breeding efficiency, sustainability and genetic improvement in animal populations. Therefore, this comprehensive review explores the journey of MAS, highlighting its historical significance, the technological tools used in marker development, key properties, advantages and limitations, practical applications and future prospects.

INTRODUCTION

Selection is the process of preferring certain individuals in a population for reproduction while denying others, resulting in non-random differential mating (Lerner, 1958). Genetic improvement in livestock focuses on choosing elite males and females to breed offspring that outperform the parental average. Selection in breeding programs can be categorized based on criteria and methods used. Natural selection occurs without human intervention, favouring individuals with traits that enhance survival and reproduction, such as disease resistance. Artificial selection is guided by humans to promote desirable traits. This includes mass selection, based on observable traits like body weight or milk yield and family selection, where average family performance is evaluated especially useful for traits influenced by the environment. Progeny testing, commonly used for sex-limited traits like milk production, involves evaluating an individual’s offspring for selection. Most economically important traits in livestock and poultry are quantitative, making phenotype-based selection alone less effective. The development of molecular markers revolutionized animal breeding by leveraging quantitative trait locus (QTL) maps, which identify marker proximity to gene regions affecting traits. These maps guide molecular techniques to accelerate the attainment of desired traits. Marker-Assisted Selection (MAS) involves selecting individuals based on markers linked to traits like feed efficiency or disease resistance. Genomic selection goes further by using genome-wide data to predict genetic potential for multiple traits, enabling more precise and efficient breeding. MAS is an indirect selection method where a trait is selected based on a linked marker (Ribaut et al., 2007). It plays a pivotal role in genetic improvement by allowing early, accurate and efficient selection, unlike traditional methods that rely on observable traits influenced by the environment. MAS uses genetic markers such as DNA sequences or genes to identify chromosomal regions affecting single-gene traits. Common marker types include restriction fragment length polymorphisms (RFLPs), microsatellites and single nucleotide polymorphisms (SNPs) (Sharma et al., 2024).
       
Therefore, the review paper will cover several key topics related to Marker-Assisted Selection (MAS) in livestock.
 
Historical overview
 
The concept of marker-assisted selection (MAS), also known as marker-assisted breeding (MAB), was introduced about 25 years ago by C. Smith and P. Simpson in 1986 and by Soller and Beckmann 1983. Marker-assisted breeding is expected to become more versatile, practical, efficient and cost-effective in the future (Pheirim et al., 2022). The idea was that while most polymorphisms identified through advanced techniques are not themselves quantitative trait loci (QTL), many are closely linked to them, enabling indirect selection. With the discovery of restriction fragment length polymorphism (RFLP; Botstein et al., 1980) and other DNA markers, it was anticipated that traditional plant and animal breeding would shift from phenotype-based selection to marker-based approaches targeting specific genes. Conventional breeding, including programs like progeny testing, selects for desirable traits based on phenotype without identifying the actual genes involved (Moniruzzaman et al., 2014). Assisted reproductive technologies such as artificial insemination and embryo transfer have improved productivity (Lindhe et al., 1998; Sartori et al., 2006), but conventional methods are time-consuming and less effective for sex-limited, low heritability, or late-expressing traits. Molecular markers help overcome these limitations by enabling early selection of genetically superior animals (Singh et al., 2014). The application of molecular markers not only enables the selection of genetically superior animals for enhanced productivity but also helps overcome the limitations associated with conventional selection methods (Karuthadurai et al., 2019). MAS is particularly valuable for traits that are difficult to evaluate directly. It allows early selection, reducing the number of animals needing full evaluation and lowering breeding costs. MAS is also effective for gene pyramiding combining multiple beneficial genes. However, while it increases selection efficiency, it does not always reduce total breeding time, as selected animals must still undergo field evaluation and timelines depend on the species’ juvenile phase.
 
Identification of genes
 
Identifying genes that influence economically important traits in livestock and poultry is vital for improving productivity and efficiency. Advances in genomic technologies have enabled the discovery of key genes and markers linked to traits such as growth, meat quality, reproduction and disease resistance. In cattle, SNPs within the DLK1-DIO3 imprinted domain, polymorphisms in the GNAS gene and variations at the IGF2 locus have been associated with production and dairy performance (Magee et al., 2011; Sikora et al., 2011; Berkowicz et al., 2011). In sheep, the “callipyge” (CLPG) gene produces a large, lean rump muscle, which is valuable for meat production (Lewis and Redrup, 2005). For chickens, QTLs and candidate genes like PIT1 and FTO have been linked to growth, body weight and fatness traits (Cahyadi et al., 2014). Understanding these genetic factors supports the use of marker-assisted and genomic selection in breeding programs, enhancing animal performance. Important traits and associated markers are summarized in Table 1.

Table 1: Comparative analysis of various DNA markers used in genetic mapping.


 
Technological tools used in MAS
 
MAS uses advanced technologies to identify and utilize genetic markers for improving economically important traits in animals. Polymerase chain reaction (PCR) amplifies specific DNA sequences, enabling genotyping of markers like SNPs and microsatellites (Mullis and Faloona, 1987). QTL mapping locates genomic regions linked to traits such as growth and milk yield (Andersson, 2001). High-throughput SNP genotyping platforms, like Illumina and Affymetrix arrays, support large-scale SNP analysis for genomic selection (Rincent et al., 2012). Next-generation sequencing (NGS) offers comprehensive genome-wide data to discover novel markers and mutations (Mardis, 2008). RNA-Seq analyzes gene expression patterns associated with traits (Mortazavi et al., 2008). High-density genotyping arrays enable simultaneous analysis of thousands of SNPs to predict breeding values (Meuwissen et al., 2001). Bioinformatics tools such as PLINK, GCTA and Ensembl facilitate genetic data analysis and marker discovery (Purcell et al., 2007). Emerging gene-editing tools like CRISPR-Cas9 validate marker-trait links and enhance traits directly (Hsu et al., 2014). Genomic prediction models, including BLUP and GBLUP, estimate breeding values to accelerate selection (VanRaden, 2008). These tools have revolutionized MAS, making genetic improvement more precise and efficient.

Markers
 
Genetic marker can be defined as any stable and inherited variation (insertions, deletions, translocations, duplications and point mutations) that can be measured or detected by a suitable method and can be used subsequently to detect the presence of a specific genotype or phenotype other than itself, which otherwise is non-measurable or very difficult to detect. Such variations occurring at different levels, i.e. at the morphological, biological, chromosomal, biochemical or DNA level can serve as the genetic markers. The markers that reveal variations at the DNA level are referred to as the molecular markers. Based on the techniques used for their detection, they have been classified into two major categories: Hybridization-based markers and PCR-based markers (Mitra, 1999). Markers may also be classified based on visually evaluated traits such as morphological and productive traits, gene products as in biochemical markers and DNA analysis-based molecular markers (Teneva et al., 2010). Fig 1 provides an overview of different molecular marker technologies used in livestock.

Fig 1: Overview of different molecular marker technologies.


 
Hybridization-based markers
 
Hybridization-based marker technologies use probes such as cDNA, cloned DNA elements, or synthetic oligonucleotides. These probes are labelled with radioisotope or with conjugated enzymes that catalyse a coloured reaction, to hybridize DNA. Hybridization-based DNA markers are of two types:
 
Restriction fragment length polymorphisms (RFLPs)
 
This method was first developed by Botstein and co-workers in the year 1980. RFLPs utilize restriction enzymes that cleave DNA at specific sequences. Differences in these sequences generate varying fragment patterns upon gel electrophoresis. Hybridization with a radioactively labelled gene-specific probe reveals genetic polymorphisms at particular loci (Williams, 2005). RFLP markers are Southern blot-based (Jiang, 2013) and are effective for locating genetic variations within genes.
 
Oligonucleotide fingerprinting
 
This technique uses short, synthetic oligonucleotide probes to generate unique hybridization patterns representing specific DNA or RNA molecules. It helps detect genetic similarities or differences and is notably applied in identifying and classifying RNA viruses for epidemiological studies (Oxford, 2025; Kew and Nottay, 1986).
 
PCR-based markers
 
Random amplified polymorphic DNAs (RAPDs)
 
RAPD markers employ PCR with single short primers (8-15 nucleotides) to amplify random DNA segments (Williams et al., 1990; Welsh and Mcclelland 1990). High GC content (50-80%) in primers improves efficiency (Premkrishnan et al., 2012; Cao et al., 2015). RAPDs are useful for quickly detecting polymorphisms without prior sequence knowledge.
 
Simple sequence repeats (SSRs) or microsatellites
 
Microsatellites, also known as STRs or VNTRs, are short DNA repeats (1-6 bp) found throughout the genome, including regulatory and coding regions (Bidichandani et al., 1998). SSR markers are also referred to as DNA microsatellites (Munsaka et al., 2024).
       
Their high polymorphism and abundance make them valuable markers in livestock genetic studies (Civanova et al., 2006; Sunnucks, 2000).

Amplified fragment length polymorphisms (AFLPs)
 
The AFLP technique, developed by Vos and co-workers in 1995 and widely used in genetic analysis, combines restriction digestion with selective PCR amplification. DNA is digested using rare- and frequent-cutting enzymes (e.g., EcoRI, MseI), ligated to adaptors and selectively amplified. Fragments are separated by PAGE or CE and visualized through fluorescence or radioactive labels. AFLP detects polymorphisms from nucleotide insertions, deletions, or substitutions, offering high reproducibility and suitability for automated genotyping (Mueller and Wolfenbarger, 1999; Bensch and Akesson, 2005; Salisu et al., 2018). The comparison of various DNA markers used in genetic mapping is summarized in Table 1. Some livestock traits along with their associated markers are listed in Table 2.

Table 2: Some traits of livestock and their associated markers.


 
Application of marker assisted selection
 
The first instance of MAS in livestock was reported in 1992 for chickens. Since then, markers have been identified in nearly all farm animals, including for milk yield in dairy cattle (Ansari-Mahyari et al., 2008; Lipkin et al., 2008), buffalo (Sarika et al., 2013), growth and carcass traits in beef cattle (Carr et al., 2006) and goats (Shen et al., 2004). A 15% improvement in genetic response for growth rate in chickens when incorporating markers, compared to selection based solely on family history (Pongpisantham, 1994).
       
MAS enables large-scale screening to identify genes linked to disease resistance, allowing for accurate selection unaffected by environmental conditions (Wakchaure et al., 2015). It eliminates the need for progeny testing, saving time and resources and allows selection among related animals not expressing sex-limited traits like milk or egg production. MAS is particularly effective in introgression strategies, helping incorporate beneficial traits while eliminating undesired ones (Hillel et al., 1990). It is most beneficial for traits with high heritability and large effects that are already optimized in commercial populations.
       
MAS, especially when combined with reproductive technologies like multiple ovulation and embryo transfer (MOET), improves selection response for traits such as milk yield by 6-15% (Ruane and Colleau, 1996). It is often integrated with traditional techniques like progeny testing to enhance breeding accuracy and economic gain in dairy cattle. MAS targets genes located at quantitative trait loci (QTLs) and knowledge of these loci enhances breeding value estimation. When QTL effects are large, MAS can significantly accelerate genetic improvement, with reported gains between 5–64% depending on trait type and marker information quality (Hayes et al., 2003).
       
Beyond selection, molecular markers serve in genetic distance estimation, parentage identification, sex determination in embryos, disease diagnostics, gene mapping and twin zygosity assessments (Deb et al., 2012). SNP markers are widely used to study genetic variation and relationships among and within livestock populations, helping identify unique genomic features in indigenous breeds (Edea et al., 2012; Gorbach et al., 2010; Lin et al., 2010).
       
Markers allow for the direct identification of genes rather than gene products, aiding physical gene mapping via in situ hybridization (O’Brien, 1991). MAS is especially advantageous for traits with low heritability, limited conventional selection response, or sex-limited expression. Its success depends on factors like strong linkage disequilibrium, large population sizes, high marker density and the presence of heterozygotes at relevant loci (Salisu et al., 2018).
       
Genetic evaluation using models like best linear unbiased prediction (BLUP) and genomic BLUP (GBLUP) combines genotypic and phenotypic data to improve breeding value accuracy (Meuwissen et al., 2001). High-throughput genotyping tools such as SNP chips, SSR markers and NGS platforms enable rapid and cost-effective marker screening (Mammadov et al., 2012). Advances in bioinformatics and automation have further enhanced genotyping efficiency, supporting large-scale breeding programs (Goddard and Hayes, 2009).
 
Limitations of marker assisted selection
 
A significant constraint of MAS is the variable expression of marker-trait associations among different populations, genetic backgrounds and environments. Quantitative trait loci (QTL) identified in one population might exhibit different effects in another because of recombination, genotype-environment interactions, or variations in linkage disequilibrium patterns. This diminishes the dependability of MAS when utilized among various breeding populations. Markers associated with economically significant traits might lose their predictive capacity if the connection between the marker and the causal gene is weak or interrupted. As a result, validating across various populations and settings is essential but requires significant time (Naeem et al., 2026). Another challenge is the variability in QTL estimates, where QTL effects vary between years or populations, undermining confidence in selection decisions based on markers. Since MAS depends on a small set of markers linked to significant genes, it tends to be less efficient for polygenic traits affected by numerous loci with minor impacts, which are prevalent in livestock production traits (Wakchaure et al., 2015).
       
While MAS can enhance selection efficiency, the significant expenses related to genotyping, marker development and validation continue to pose a substantial obstacle, particularly in developing nations where breeding initiatives frequently function with restricted financial and technical resources. These financial limitations hinder smallholder farmers and public breeding programs from adopting MAS, consequently diminishing its overall effect on developing livestock production systems (Verma et al., 2025). The advancement of high-density SNP arrays and genome-wide prediction models, genomic selection (GS) has significantly outper-formed MAS in enhancing complex quantitative traits. Marker-assisted selection (MAS) is less effective than genomic selection for improving complex quantitative traits because it relies on a limited number of markers linked to major genes, whereas genomic selection uses genome-wide marker information to capture a larger proportion of genetic variation and achieve higher prediction accuracy (Meuwissen et al., 2001; Dekkers, 2004 and Naeem et al., 2026).
 
Future perspectives of marker assisted selection
 
Understanding the genes responsible for trait expression enables researchers to explore novel allele combinations for enhancing desirable traits, as such combinations may not naturally exist in production populations (Williams, 2005). Selective breeding strategies using marker-assisted selection (MAS) can evaluate these interactions to improve genetic traits. Integrating advanced technologies like genomic sequencing, gene editing and data analytics enhances MAS accuracy and effectiveness, supporting optimal reproductive performance (Soller, 1994). MAS is expanding into fields like aquaculture and unconventional breeding, improving productivity, disease resistance and adaptability in lesser-studied species (Dodgson et al., 1997). For MAS to fulfill its potential, ethical and regulatory frameworks must evolve alongside technological advances, ensuring biosafety, food security and public acceptance (Gao et al., 2020). Transparent policies and interdisciplinary efforts are essential to address societal concerns. The future of MAS lies in its integration with genomic tools, the selection of complex traits and broader species application. Addressing current challenges such as cost, access and ethical issues will be vital for making MAS a more sustainable and efficient tool in modern breeding programs (Hasan et al., 2021).

CONCLUSION

Genetic improvement of animals is a very crucial and complex process. When incorporated with traditional selection methods, MAS has been proved to be a valuable aid in selecting animals with desirable traits. In order for MAS to be devised successfully, certain issues such as identification of traits for selection. Successfully implementing MAS requires addressing several challenges, including identifying the traits to be selected and their economic significance, the limited knowledge of the genes or markers linked to these traits and understanding how these traits are associated with other important selection criteria. In the future, for MAS to be successful in large-scale breeding programs, the development of affordable, large-scale genotyping technologies and supporting infrastructure will be essential, thereby further enhancing livestock productivity, sustainability and disease management in animal breeding programs.

ACKNOWLEDGEMENT

The author conducted this work independently and sincerely acknowledges the open-access databases and scientific literature platforms that made this review possible.
 
Disclaimers
 
The views and conclusions expressed in this article are solely those of the authors and do not necessarily represent the views of their affiliated institutions. The authors are responsible for the accuracy and completeness of the information provided, but do not accept any liability for any direct or indirect losses resulting from the use of this content.
 
Informed consent
 
Since this is a review article so, there is no use of animal procedures for experiments.

CONFLICT OF INTEREST

The authors declare that there are no conflicts of interest regarding the publication of this article. No funding or sponsorship influenced the design of the study, data collection, analysis, decision to publish, or preparation of the manuscript.

REFERENCES


  1. Andersson, L. (2001). Genetic dissection of phenotypic diversity in farm animals. Nature Reviews Genetics. 2(2): 130-138.

  2. Ansari-Mahyari, S., Sorensen, A.C., Lund, M.S., Thomsen, H. and Berg, P. (2008). Across-family marker-assisted selection using selective genotyping strategies in dairy cattle breeding schemes. Journal of Dairy Science. 91(4): 1628-1639.

  3. Bensch, S. and Akesson, M. (2005). Ten years of AFLP in ecology and evolution: Why so few animals. Molecular Ecology. 14(10): 2899-2914.

  4. Berkowicz, E.W., Magee, D.A., Sikora, K.M., Berry, D.P., Howard, D.J., Mullen, M.P., Evans, R.D., Spillane, C. and MacHugh, D.E. (2011). Single nucleotide polymorphisms at the imprinted bovine insulin-like growth factor 2 (IGF2) locus are associated with dairy performance in Irish Holstein- Friesian cattle. The Journal of Dairy Research. 78(1): 1-8. 

  5. Bidichandani, S.I., Ashizawa, T. and Patel, P.I. (1998). The GAA triplet- repeat expansion in Friedreich ataxia interferes with transcription and may be associated with an unusual DNA structure. The American Journal of Human Genetics. 62(1): 111-121.

  6. Botstein, D., White, R.L., Skolnick, M. and Davis, R.W. (1980). Construction of a genetic linkage map in man using restriction fragment length polymorphisms. American Journal of Human Genetics. 32(3): 314.

  7. Cahyadi, M., Jo, C. and Lee, J.H. (2014). Quantitative trait loci and candidate genes for the economic traits in meat-type chicken. World’s Poultry Science Journal. 70(2): 329-342. 

  8. Cao, X., Wu, Z., Zhou, R., Jiang, F.L. and Yang, Z.E. (2015). A novel random amplified polymorphic DNA-based strategy for genetic diversity analysis and identification of tomatoes. Genetics and Molecular Research. 14(1): 1650-1661. 

  9. Carr, C.C., Morgan, J.B., Berg, E.P., Carter, S.D. and Ray, F.K. (2006). Growth performance, carcass composition, quality and enhancement treatment of fresh pork identified through deoxyribonucleic acid marker-assisted selection for the Rendement Napole gene. Journal of Animal Science. 84(4): 910-917.

  10. Civanova, K., Putnova, L. and Dvorak, J. (2006). Analysis of micro- satellite set for biodiversity studies in horses. Acta Fytotechnica et Zootechnica. 47: 39-40. 

  11. Deb, R., Singh, U., Kumar, S. and Sharma, A. (2012). Molecular Marker Assisted Selection for Disease Resistance Breeds of Cattle. Agricultural Research Updates. Nova Science Publishers, New York.  pp. 153.

  12. Dekkers, J.C.M. (2004). Commercial application of marker- and gene- assisted selection in livestock: Strategies and lessons. Journal of Animal Science. 82(E-Suppl): E313-E328.

  13. Dodgson, J.B., Cheng, H.H. and Okimoto, R. (1997). DNA marker technology: A revolution in animal genetics. Poultry Science. 76(8): 1108-1114.

  14. Edea, Z., Dadi, H., Kim, S.W., Dessie, T. and Kim, K.S. (2012). Comparison of SNP variation and distribution in indigenous Ethiopian and Korean Cattle (Hanwoo) populations. Genomics and Informatics. 10(3): 200-205.

  15. Erhardt, G and Weimann, C. (2007). Use of molecular markers for evaluation of genetic diversity and in animal production. Archivos Latinoamericanos de Producción Animal. 15(5): 63-66. 

  16. Gao, Y., Zhang, J., Li, J. and Wang, X. (2020). Ethical considerations and regulation of genome editing in animals. Translational Animal Science. 4(2): 854-862.

  17. Goddard, M.E. and Hayes, B.J. (2009). Mapping genes for complex traits in domestic animals and their use in breeding programmes. Nature Reviews Genetics. 10(6): 381-391.

  18. Gorbach, D., Makgahlela, M., Reecy, J., Kemp, S., Baltenweck, I., Ouma, R. and Moore, S. (2010). Use of SNP genotyping to determine pedigree and breed composition of dairy cattle in Kenya. Journal of Animal Breeding and Genetics. 127(5): 348-351. 

  19. Hasan, N., Choudhary, S., Naaz, N., Sharma, N. and Laskar, R.A. (2021). Recent advancements in molecular marker-assisted selection and applications in plant breeding programmes. Journal of Genetic Engineering and Biotechnology. 19(1): 1-26.

  20. Hayes, B. and Goddard, M.E. (2003). Evaluation of marker assisted selection in pig enterprises. Livestock Production Science. 81(2-3): 197-211.

  21. He, F., Sun, D., Zhang, Y. and Wang, Y. (2006). Association of prolactin gene polymorphisms with milk production traits in cattle. Journal of Dairy Science. 89(9): 3503-3508.

  22. Hillel, J., Schaap, T., Haberfeld, A., Jeffreys, A.J., Plotzky, Y., Cahaner, A. and Lavi, U. (1990). DNA fingerprints applied to gene introgression in breeding programs. Genetics. 124(3): 783-789.

  23. Hsu, P.D., Lander, E.S. and Zhang, F. (2014). Development and applications of CRISPR-Cas9 for genome engineering. Cell. 157(6): 1262-1278. 

  24. Ilie, D. E., Kusza, S., Sauer, M. and Gavojdian, D. (2018). Genetic characterization of indigenous goat breeds in Romania and Hungary with a special focus on genetic resistance to mastitis and gastrointestinal parasitism based on 40 SNPs. PLoS ONE. 13(5): e0197051.

  25. Jiang, G.L. (2013). Molecular markers and marker-assisted breeding. Plant Breeding from Laboratories to Fields. IntechOpen Publishers, London. pp. 45-85.

  26. Karuthadurai, T., Chakravarthy, A. K., Kumaresan, A., Kour, A., Nag, B.P., Rana, E., Ravikumar, D. and Yousuf, S. (2019). Identification of genetic marker for prolactin gene related to milk yield in pedigreed Sahiwal population. Indian Journal of Animal Research. 53(5): 566-571. doi: 10.18805/ijar.B-3578.

  27. Kawaguchi, F., Tsuchimura, M., Oyama, K., Matsuhashi, T., Maruyama, S., Mannen, H. and Sasazaki, S. (2020). Effect of DNA markers on carcass and fertility traits in Japanese Black cattle. Animal Biology and Animal Husbandry. 63(1): 9-11.

  28. Kew, O. and Nottay, B. (1986). Oligonucleotide Fingerprinting in the Investigations of Outbreaks of Viral Disease. Concepts in Viral Pathogenesis II. Springer Publishers, New York. pp. 317-323. 

  29. Lerner, I.M. (1958). The Genetic Basis of Selection. Chapman and Hall, Ltd., London and John Wiley and Sons, Inc., New York. pp. 298.

  30. Lewis, A. and Redrup, L. (2005). Genetic imprinting: Conflict at the Callipyge locus. Current Biology. 15(8): 291-294.

  31. Lin, B.Z., Sasazaki, S. and Mannen, H. (2010). Genetic diversity and structure in Bos taurus and Bos indicus populations analyzed by SNP markers. Animal Science Journal. 81(3): 281-289.

  32. Lindhe, B. and Philipsson, J. (1998). Conventional breeding programmes and genetic resistance to animal diseases. Revue Scientifique et Technique. 17(1):  291-301.

  33. Lipkin, E., Bagnato, A. and Soller, M. (2008). Expected effects on protein yield of marker-assisted selection at quantitative trait loci affecting milk yield and milk protein percentage. Journal of Dairy Science. 91(7): 2857-2863.

  34. Liu, R., Sun, Y., Zhao, G., Wang, F., Wu, D., Zheng, M. and Wen, J. (2013). Genome-wide association study identifies loci and candidate genes for egg production traits in chickens. PLoS ONE. 8(4): e61172.

  35. Magee, D.A., Berry, D.P., Berkowicz, E.W., Sikora, K.M., Howard, D.J., Mullen, M.P., Evans, R.D., Spillane, C. and MacHugh, D.E. (2011). Single nucleotide polymorphisms within the bovine DLK1-DIO3 imprinted domain are associated with economically important production traits in cattle. Journal of Heredity. 102(1): 94-101.

  36. Mammadov, J., Aggarwal, R., Buyyarapu, R. and Kumpatla, S. (2012). SNP markers and their impact on plant breeding. International Journal of Plant Genomics. 2012(1): 728398.

  37. Mardis, E.R. (2008). Next-generation DNA sequencing methods. Annual Review of Genomics and Human Genetics. 9(1): 387-402.

  38. Meuwissen, T.H., Hayes, B.J. and Goddard, M. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics. 157(4): 1819-1829.

  39. Mitra, A., Yadav, B.R., Ganai, N.A. and Balakrishnan, C.R. (1999). Molecular markers and their applications in livestock improvement. Current Science. 77(8): 1045-1053.

  40. Moniruzzaman, M., Khatun, R. and Mintoo, A.A. (2014). Application of marker assisted selection for livestock improvement in Bangladesh. Bangladesh Veterinarian. 31(1): 1-11.

  41. Mortazavi, A., Williams, B.A., McCue, K., Schaeffer, L. and Wold, B. (2008). Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nature Methods. 5(7): 621-628.

  42. Mueller, U.G. and Wolfenbarger, L.L. (1999). AFLP genotyping and fingerprinting. Trends in Ecology and Evolution. 14(10): 389-394.

  43. Mullis, K. and Faloona, F. (1987). Specific synthesis of DNA in vitro via a polymerase-catalyzed chain reaction. Methods in Enzymology. 155: 335-350.

  44. Munsaka, S., Kaimoyo, E., Arunachalam, K., Ng’Uni, D., Chikoti, P. and Sharma, N. (2024). Characterization of selected zambian rice (Oryza sativa) accessions using simple sequence repeat (SSR) Markers. Indian Journal of Agricultural Research. 58(1): 11-16. doi: 10.18805/IJARe.A-5894. 

  45. Naeem, M., Fatima, A., Raut, R., Kumar, R., Tushar, Z., Rahman, F. and Bourassa, D. (2026). Management and genetic approaches for enhancing meat quality in poultry production Systems: A Comprehensive Review. Poultry. 5(1): 4.

  46. O’Brien, S.J. (1991). Mammalian genome mapping: Lessons and prospects. Current Opinion in Genetics and Development. 1(1): 105-111.

  47. Oxford Reference. (2025). Oligonucleotide Fingerprinting. Oxford Reference. Oxford University Press. Accessed March 30, 2025. https://www.oxfordreference.com.

  48. Pheirim, R., Konjengbam, N.S. and Mahanta, M. (2022). Molecular markers for powdery mildew in pea (Pisum sativum L.): A review. Legume Research- An International Journal.  45(4): 399-409. doi: 10.18805/LR-4740.

  49. Pongpisantham, B. (1994). Applying genotype and marker assisted selection for the improvement of quantitative traits in poultry. M.S. Thesis. University of New England: 121.

  50. Premkrishnan, B.V. and Arunachalam, V. (2012). Silico RAPD priming sites in expressed sequences and iSCAR markers for oil palm. International Journal of Genomics. 2012(1): 913709. 

  51. 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. and 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.

  52. Ribaut, J.M. and Ragot, M. (2007). Marker-assisted selection to improve drought adaptation in maize: The backcross approach, perspectives, limitations and alternatives. Journal of Experimental Botany. 58(2): 351-360.

  53. Rincent, R., Laloe, D., Nicolas, S., Altmann, T., Brunel, D., Revilla, P., Rodriguez, V.M., Moreno-Gonzalez, J., Melchinger, A., Bauer, E., Schoen, C.C. (2012). Maximizing the reliability of genomic selection by optimizing the calibration set of reference individuals: Comparison of methods in two diverse groups of maize inbreds (Zea mays L.). Genetics. 192(2): 715-728.

  54. Rothschild, M.F., Jacobson, C., Vaske, D., Tuggle, C., Wang, L., Short, T., Eckardt, G., Sasaki, S., Vincent, A., McLaren, D., Southwood, O., van der Steen, H., Mileham, A. and Plastow, G. (1996). The estrogen receptor locus is associated with a major gene influencing litter size in pigs. Proceedings of the National Academy of Sciences. 93(1): 201-205.

  55. Ruane, J. and Colleau, J.J. (1996). Marker-assisted selection for a sex-limited character in a nucleus breeding population. Journal of Dairy Science. 79(9): 1666-1678.

  56. Salisu, I.B., Olawale, A.S., Jabbar, B., Koloko, B.L., Abdurrahaman, S.L., Amin, A.B. and Ali, Q. (2018). Molecular markers and their Potentials in Animal Breeding and Genetics. Nigerian Journal of Animal Science. 20(3): 29-48.

  57. Sarika, Arora, V., Iquebal, M.A., Rai, A. and Kumar, D. (2013). In silico mining of putative microsatellite markers from whole genome sequence of water buffalo (Bubalus bubalis) and development of first BuffSatDB. BMC genomics. 14: 1-8.

  58. Sartori, R., Gümen, A.H.M.E.T., Guenther, J.N., Souza, A.H., Caraviello, D.Z. and Wiltbank, M.C. (2006). Comparison of artificial insemination versus embryo transfer in lactating dairy cows. Theriogenology. 65(7): 1311-1321.

  59. Sharma, P., Doultani, S., Hadiya, K.K., George, L.B. and Highland, H.N. (2024). Overview of marker-assisted selection in animal breeding. Journal of Advances in Biology and Biotechnology. 27(5): 303-318.

  60. Shen, W., Li, L., Pan, Q.J., Qin, G.Q. and Geng, S.M. (2004). Genetic effect of the marker assisted selection on economic traits of goats. Yi chuan. 26(5): 625-630.

  61. Sikora, K.M., Magee, D.A., Berkowicz, E.W., Berry, D.P., Howard, D.J., Mullen, M.P., Evans, R.D., MacHugh, D.E. and Spillane, C. (2011). DNA sequence polymorphisms within the bovine guanine nucleotide-binding protein Gs subunit alpha (Gsα)- encoding (GNAS) genomic imprinting domain are associated with performance traits. BMC Genetics. 12(4): 1-6.

  62. Singh, B.D. (2007). Fundamentals of Genetics. Kalyani Publishers, Ludhiana, Punjab, India. pp. 161.

  63. Singh, U., Deb, R., Alyethodi, R.R., Alex, R., Kumar, S., Chakraborty, S., Dhama, K. and Sharma, A. (2014). Molecular markers and their applications in cattle genetic research: A review. Biomarkers and Genomic Medicine. 6(2): 49-58.

  64. Smith, C. and Simpson, S.P. (1986). The use of genetic polymorphisms in livestock improvement. Journal of Animal Breeding and Genetics. 103(3): 205-217.

  65. Soller, M. (1994). Marker assisted selection an overview. Animal Biotechnology. 5(2): 193-207. 

  66. Soller, M. and Beckmann, J.S. (1983). Genetic polymorphism in varietal identification and genetic improvement. Theoretical and Applied Genetics. 67: 25-33.

  67. Song, S., Yao, N., Yang, M., Liu, X., Dong, K., Zhao, Q., Yabin, P., Xiaohong, H., Weijun, G., Ning, Y., Yuehui, M. and Wang, X. (2016). Exome sequencing reveals genetic differentiation due to high-altitude adaptation in the Tibetan cashmere goat (Capra hircus). BMC Genomics. 17: 1-12.

  68. Sunnucks, P. (2000). Efficient genetic markers for population biology. Trends in Ecology and Evolution. 15(5): 199-203.

  69. Teneva, A. and Petrovic, M.P. (2010). Application of molecular markers in livestock improvement. Biotechnology in Animal Husbandry. 26(3-4): 135-154.

  70. VanRaden, P.M. (2008). Efficient methods to compute genomic predictions. Journal of Dairy Science. 91(11): 4414-4423.

  71. Verma, A., Parmar, S., Patel, N. and Janmeda, M. (2025). Impact of marker assisted selection (MAS) on economy of production trait in animals: A review. International Journal of Veterinary Sciences and Animal Husbandry. 10(11): 340-344.

  72. Vos, P., Hogers, R., Bleeker, M., Reijans, M., Van de Lee, T., Hornes, M., Frijters, A., Pot, J., Peleman, J., Kuiper, M. and Zabeau, M. (1995). AFLP: A new technique for DNA fingerprinting. Nucleic Acids Research. 23(21): 4407-4414.

  73. Wakchaure, R., Ganguly, S., Praveen, P.K., Kumar, A., Sharma, S. and Mahajan, T.J. J.D.M.T. (2015). Marker assisted selection (MAS) in animal breeding: A review. Journal of Drug Metabolism and Toxicology. 6(5): 127.

  74. Wang, X., Cai, B., Zhou, J., Zhu, H., Niu, Y., Ma, B., Honghao, Y., Anmin, L., Hailong, Y., Qiaoyan, S., Lei, S., Xiaoe, Z., Jinlian, H., Xingxu, H., Lei, Q. and Chen, Y. (2016). Disruption of FGF5 in cashmere goats using CRISPR/Cas9 results in more secondary hair follicles and longer fibers. PLoS ONE. 11(10): e0164640.

  75. Welsh, J. and Mcclelland, M. (1990). Fingerprinting genomes using PCR with arbitrary primers. Nucleic Acids Research. 18(24): 7213-7218. 

  76. Williams, J.G.K., Kubelik, A.R., Livak, K.J., Rafalski, J.A. and Tingey, S.V. (1990). DNA polimorphism amplified by arbitrary primers are useful as genetic markers. Nucleic Acids Research. 18(22): 6531-6535. 

  77. Williams, J.L. (2005). The use of marker-assisted selection in animal breeding and biotechnology. Revue Scientifique et Technique- Office International des Epizooties. 24(1): 379.
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    Review Article

    Role of Marker-assisted Selection for Genetic Improvement in Livestock: A Review

    A
    Aakriti Sharma1
    B
    Bula Das1
    A
    Arundhati Phookan1
    A
    Ankita Gogoi1
    O
    Olympica Sarma2,*
    1Department of Animal Genetics and Breeding, College of Veterinary Science, Assam Agricultural University, Khanapara, Guwahati-781 001, Assam, India.
    2Department of Animal Genetics and Breeding, Sanskaram College of Veterinary and Animal Science, Sanskaram University, Patauda-124 108, Haryana, India.

    • Submitted11-06-2025|

    • Accepted01-04-2026|

    • First Online 29-04-2026|

    • doi 10.18805/BKAP862

    ABSTRACT

    Marker Assisted Selection (MAS) is a valuable tool in livestock breeding that helps improve desirable traits by using genetic markers linked to specific genes. Unlike traditional methods that rely on visible traits, MAS allows selection based on genetic makeup, making it more accurate and efficient. Introduced in the late 20th century, MAS has evolved significantly with the integration of genomic technologies, advanced statistical modelling and gene-editing techniques. The key advantage of MAS is that it enables early selection, even before an animal’s phenotype is fully expressed, thus speeding up the breeding process and improving genetic gain. MAS has been successfully applied in various livestock species, including cattle, pigs and poultry, to enhance production traits like growth rate, feed efficiency, milk yield and disease resistance. The future of MAS lies in its integration with advanced genomic technologies, enabling precise selection for complex traits across diverse species. Continued advancements in gene editing and data analytics will enhance breeding efficiency, sustainability and genetic improvement in animal populations. Therefore, this comprehensive review explores the journey of MAS, highlighting its historical significance, the technological tools used in marker development, key properties, advantages and limitations, practical applications and future prospects.

    INTRODUCTION

    Selection is the process of preferring certain individuals in a population for reproduction while denying others, resulting in non-random differential mating (Lerner, 1958). Genetic improvement in livestock focuses on choosing elite males and females to breed offspring that outperform the parental average. Selection in breeding programs can be categorized based on criteria and methods used. Natural selection occurs without human intervention, favouring individuals with traits that enhance survival and reproduction, such as disease resistance. Artificial selection is guided by humans to promote desirable traits. This includes mass selection, based on observable traits like body weight or milk yield and family selection, where average family performance is evaluated especially useful for traits influenced by the environment. Progeny testing, commonly used for sex-limited traits like milk production, involves evaluating an individual’s offspring for selection. Most economically important traits in livestock and poultry are quantitative, making phenotype-based selection alone less effective. The development of molecular markers revolutionized animal breeding by leveraging quantitative trait locus (QTL) maps, which identify marker proximity to gene regions affecting traits. These maps guide molecular techniques to accelerate the attainment of desired traits. Marker-Assisted Selection (MAS) involves selecting individuals based on markers linked to traits like feed efficiency or disease resistance. Genomic selection goes further by using genome-wide data to predict genetic potential for multiple traits, enabling more precise and efficient breeding. MAS is an indirect selection method where a trait is selected based on a linked marker (Ribaut et al., 2007). It plays a pivotal role in genetic improvement by allowing early, accurate and efficient selection, unlike traditional methods that rely on observable traits influenced by the environment. MAS uses genetic markers such as DNA sequences or genes to identify chromosomal regions affecting single-gene traits. Common marker types include restriction fragment length polymorphisms (RFLPs), microsatellites and single nucleotide polymorphisms (SNPs) (Sharma et al., 2024).
           
    Therefore, the review paper will cover several key topics related to Marker-Assisted Selection (MAS) in livestock.
     
    Historical overview
     
    The concept of marker-assisted selection (MAS), also known as marker-assisted breeding (MAB), was introduced about 25 years ago by C. Smith and P. Simpson in 1986 and by Soller and Beckmann 1983. Marker-assisted breeding is expected to become more versatile, practical, efficient and cost-effective in the future (Pheirim et al., 2022). The idea was that while most polymorphisms identified through advanced techniques are not themselves quantitative trait loci (QTL), many are closely linked to them, enabling indirect selection. With the discovery of restriction fragment length polymorphism (RFLP; Botstein et al., 1980) and other DNA markers, it was anticipated that traditional plant and animal breeding would shift from phenotype-based selection to marker-based approaches targeting specific genes. Conventional breeding, including programs like progeny testing, selects for desirable traits based on phenotype without identifying the actual genes involved (Moniruzzaman et al., 2014). Assisted reproductive technologies such as artificial insemination and embryo transfer have improved productivity (Lindhe et al., 1998; Sartori et al., 2006), but conventional methods are time-consuming and less effective for sex-limited, low heritability, or late-expressing traits. Molecular markers help overcome these limitations by enabling early selection of genetically superior animals (Singh et al., 2014). The application of molecular markers not only enables the selection of genetically superior animals for enhanced productivity but also helps overcome the limitations associated with conventional selection methods (Karuthadurai et al., 2019). MAS is particularly valuable for traits that are difficult to evaluate directly. It allows early selection, reducing the number of animals needing full evaluation and lowering breeding costs. MAS is also effective for gene pyramiding combining multiple beneficial genes. However, while it increases selection efficiency, it does not always reduce total breeding time, as selected animals must still undergo field evaluation and timelines depend on the species’ juvenile phase.
     
    Identification of genes
     
    Identifying genes that influence economically important traits in livestock and poultry is vital for improving productivity and efficiency. Advances in genomic technologies have enabled the discovery of key genes and markers linked to traits such as growth, meat quality, reproduction and disease resistance. In cattle, SNPs within the DLK1-DIO3 imprinted domain, polymorphisms in the GNAS gene and variations at the IGF2 locus have been associated with production and dairy performance (Magee et al., 2011; Sikora et al., 2011; Berkowicz et al., 2011). In sheep, the “callipyge” (CLPG) gene produces a large, lean rump muscle, which is valuable for meat production (Lewis and Redrup, 2005). For chickens, QTLs and candidate genes like PIT1 and FTO have been linked to growth, body weight and fatness traits (Cahyadi et al., 2014). Understanding these genetic factors supports the use of marker-assisted and genomic selection in breeding programs, enhancing animal performance. Important traits and associated markers are summarized in Table 1.

    Table 1: Comparative analysis of various DNA markers used in genetic mapping.


     
    Technological tools used in MAS
     
    MAS uses advanced technologies to identify and utilize genetic markers for improving economically important traits in animals. Polymerase chain reaction (PCR) amplifies specific DNA sequences, enabling genotyping of markers like SNPs and microsatellites (Mullis and Faloona, 1987). QTL mapping locates genomic regions linked to traits such as growth and milk yield (Andersson, 2001). High-throughput SNP genotyping platforms, like Illumina and Affymetrix arrays, support large-scale SNP analysis for genomic selection (Rincent et al., 2012). Next-generation sequencing (NGS) offers comprehensive genome-wide data to discover novel markers and mutations (Mardis, 2008). RNA-Seq analyzes gene expression patterns associated with traits (Mortazavi et al., 2008). High-density genotyping arrays enable simultaneous analysis of thousands of SNPs to predict breeding values (Meuwissen et al., 2001). Bioinformatics tools such as PLINK, GCTA and Ensembl facilitate genetic data analysis and marker discovery (Purcell et al., 2007). Emerging gene-editing tools like CRISPR-Cas9 validate marker-trait links and enhance traits directly (Hsu et al., 2014). Genomic prediction models, including BLUP and GBLUP, estimate breeding values to accelerate selection (VanRaden, 2008). These tools have revolutionized MAS, making genetic improvement more precise and efficient.

    Markers
     
    Genetic marker can be defined as any stable and inherited variation (insertions, deletions, translocations, duplications and point mutations) that can be measured or detected by a suitable method and can be used subsequently to detect the presence of a specific genotype or phenotype other than itself, which otherwise is non-measurable or very difficult to detect. Such variations occurring at different levels, i.e. at the morphological, biological, chromosomal, biochemical or DNA level can serve as the genetic markers. The markers that reveal variations at the DNA level are referred to as the molecular markers. Based on the techniques used for their detection, they have been classified into two major categories: Hybridization-based markers and PCR-based markers (Mitra, 1999). Markers may also be classified based on visually evaluated traits such as morphological and productive traits, gene products as in biochemical markers and DNA analysis-based molecular markers (Teneva et al., 2010). Fig 1 provides an overview of different molecular marker technologies used in livestock.

    Fig 1: Overview of different molecular marker technologies.


     
    Hybridization-based markers
     
    Hybridization-based marker technologies use probes such as cDNA, cloned DNA elements, or synthetic oligonucleotides. These probes are labelled with radioisotope or with conjugated enzymes that catalyse a coloured reaction, to hybridize DNA. Hybridization-based DNA markers are of two types:
     
    Restriction fragment length polymorphisms (RFLPs)
     
    This method was first developed by Botstein and co-workers in the year 1980. RFLPs utilize restriction enzymes that cleave DNA at specific sequences. Differences in these sequences generate varying fragment patterns upon gel electrophoresis. Hybridization with a radioactively labelled gene-specific probe reveals genetic polymorphisms at particular loci (Williams, 2005). RFLP markers are Southern blot-based (Jiang, 2013) and are effective for locating genetic variations within genes.
     
    Oligonucleotide fingerprinting
     
    This technique uses short, synthetic oligonucleotide probes to generate unique hybridization patterns representing specific DNA or RNA molecules. It helps detect genetic similarities or differences and is notably applied in identifying and classifying RNA viruses for epidemiological studies (Oxford, 2025; Kew and Nottay, 1986).
     
    PCR-based markers
     
    Random amplified polymorphic DNAs (RAPDs)
     
    RAPD markers employ PCR with single short primers (8-15 nucleotides) to amplify random DNA segments (Williams et al., 1990; Welsh and Mcclelland 1990). High GC content (50-80%) in primers improves efficiency (Premkrishnan et al., 2012; Cao et al., 2015). RAPDs are useful for quickly detecting polymorphisms without prior sequence knowledge.
     
    Simple sequence repeats (SSRs) or microsatellites
     
    Microsatellites, also known as STRs or VNTRs, are short DNA repeats (1-6 bp) found throughout the genome, including regulatory and coding regions (Bidichandani et al., 1998). SSR markers are also referred to as DNA microsatellites (Munsaka et al., 2024).
           
    Their high polymorphism and abundance make them valuable markers in livestock genetic studies (Civanova et al., 2006; Sunnucks, 2000).

    Amplified fragment length polymorphisms (AFLPs)
     
    The AFLP technique, developed by Vos and co-workers in 1995 and widely used in genetic analysis, combines restriction digestion with selective PCR amplification. DNA is digested using rare- and frequent-cutting enzymes (e.g., EcoRI, MseI), ligated to adaptors and selectively amplified. Fragments are separated by PAGE or CE and visualized through fluorescence or radioactive labels. AFLP detects polymorphisms from nucleotide insertions, deletions, or substitutions, offering high reproducibility and suitability for automated genotyping (Mueller and Wolfenbarger, 1999; Bensch and Akesson, 2005; Salisu et al., 2018). The comparison of various DNA markers used in genetic mapping is summarized in Table 1. Some livestock traits along with their associated markers are listed in Table 2.

    Table 2: Some traits of livestock and their associated markers.


     
    Application of marker assisted selection
     
    The first instance of MAS in livestock was reported in 1992 for chickens. Since then, markers have been identified in nearly all farm animals, including for milk yield in dairy cattle (Ansari-Mahyari et al., 2008; Lipkin et al., 2008), buffalo (Sarika et al., 2013), growth and carcass traits in beef cattle (Carr et al., 2006) and goats (Shen et al., 2004). A 15% improvement in genetic response for growth rate in chickens when incorporating markers, compared to selection based solely on family history (Pongpisantham, 1994).
           
    MAS enables large-scale screening to identify genes linked to disease resistance, allowing for accurate selection unaffected by environmental conditions (Wakchaure et al., 2015). It eliminates the need for progeny testing, saving time and resources and allows selection among related animals not expressing sex-limited traits like milk or egg production. MAS is particularly effective in introgression strategies, helping incorporate beneficial traits while eliminating undesired ones (Hillel et al., 1990). It is most beneficial for traits with high heritability and large effects that are already optimized in commercial populations.
           
    MAS, especially when combined with reproductive technologies like multiple ovulation and embryo transfer (MOET), improves selection response for traits such as milk yield by 6-15% (Ruane and Colleau, 1996). It is often integrated with traditional techniques like progeny testing to enhance breeding accuracy and economic gain in dairy cattle. MAS targets genes located at quantitative trait loci (QTLs) and knowledge of these loci enhances breeding value estimation. When QTL effects are large, MAS can significantly accelerate genetic improvement, with reported gains between 5–64% depending on trait type and marker information quality (Hayes et al., 2003).
           
    Beyond selection, molecular markers serve in genetic distance estimation, parentage identification, sex determination in embryos, disease diagnostics, gene mapping and twin zygosity assessments (Deb et al., 2012). SNP markers are widely used to study genetic variation and relationships among and within livestock populations, helping identify unique genomic features in indigenous breeds (Edea et al., 2012; Gorbach et al., 2010; Lin et al., 2010).
           
    Markers allow for the direct identification of genes rather than gene products, aiding physical gene mapping via in situ hybridization (O’Brien, 1991). MAS is especially advantageous for traits with low heritability, limited conventional selection response, or sex-limited expression. Its success depends on factors like strong linkage disequilibrium, large population sizes, high marker density and the presence of heterozygotes at relevant loci (Salisu et al., 2018).
           
    Genetic evaluation using models like best linear unbiased prediction (BLUP) and genomic BLUP (GBLUP) combines genotypic and phenotypic data to improve breeding value accuracy (Meuwissen et al., 2001). High-throughput genotyping tools such as SNP chips, SSR markers and NGS platforms enable rapid and cost-effective marker screening (Mammadov et al., 2012). Advances in bioinformatics and automation have further enhanced genotyping efficiency, supporting large-scale breeding programs (Goddard and Hayes, 2009).
     
    Limitations of marker assisted selection
     
    A significant constraint of MAS is the variable expression of marker-trait associations among different populations, genetic backgrounds and environments. Quantitative trait loci (QTL) identified in one population might exhibit different effects in another because of recombination, genotype-environment interactions, or variations in linkage disequilibrium patterns. This diminishes the dependability of MAS when utilized among various breeding populations. Markers associated with economically significant traits might lose their predictive capacity if the connection between the marker and the causal gene is weak or interrupted. As a result, validating across various populations and settings is essential but requires significant time (Naeem et al., 2026). Another challenge is the variability in QTL estimates, where QTL effects vary between years or populations, undermining confidence in selection decisions based on markers. Since MAS depends on a small set of markers linked to significant genes, it tends to be less efficient for polygenic traits affected by numerous loci with minor impacts, which are prevalent in livestock production traits (Wakchaure et al., 2015).
           
    While MAS can enhance selection efficiency, the significant expenses related to genotyping, marker development and validation continue to pose a substantial obstacle, particularly in developing nations where breeding initiatives frequently function with restricted financial and technical resources. These financial limitations hinder smallholder farmers and public breeding programs from adopting MAS, consequently diminishing its overall effect on developing livestock production systems (Verma et al., 2025). The advancement of high-density SNP arrays and genome-wide prediction models, genomic selection (GS) has significantly outper-formed MAS in enhancing complex quantitative traits. Marker-assisted selection (MAS) is less effective than genomic selection for improving complex quantitative traits because it relies on a limited number of markers linked to major genes, whereas genomic selection uses genome-wide marker information to capture a larger proportion of genetic variation and achieve higher prediction accuracy (Meuwissen et al., 2001; Dekkers, 2004 and Naeem et al., 2026).
     
    Future perspectives of marker assisted selection
     
    Understanding the genes responsible for trait expression enables researchers to explore novel allele combinations for enhancing desirable traits, as such combinations may not naturally exist in production populations (Williams, 2005). Selective breeding strategies using marker-assisted selection (MAS) can evaluate these interactions to improve genetic traits. Integrating advanced technologies like genomic sequencing, gene editing and data analytics enhances MAS accuracy and effectiveness, supporting optimal reproductive performance (Soller, 1994). MAS is expanding into fields like aquaculture and unconventional breeding, improving productivity, disease resistance and adaptability in lesser-studied species (Dodgson et al., 1997). For MAS to fulfill its potential, ethical and regulatory frameworks must evolve alongside technological advances, ensuring biosafety, food security and public acceptance (Gao et al., 2020). Transparent policies and interdisciplinary efforts are essential to address societal concerns. The future of MAS lies in its integration with genomic tools, the selection of complex traits and broader species application. Addressing current challenges such as cost, access and ethical issues will be vital for making MAS a more sustainable and efficient tool in modern breeding programs (Hasan et al., 2021).

    CONCLUSION

    Genetic improvement of animals is a very crucial and complex process. When incorporated with traditional selection methods, MAS has been proved to be a valuable aid in selecting animals with desirable traits. In order for MAS to be devised successfully, certain issues such as identification of traits for selection. Successfully implementing MAS requires addressing several challenges, including identifying the traits to be selected and their economic significance, the limited knowledge of the genes or markers linked to these traits and understanding how these traits are associated with other important selection criteria. In the future, for MAS to be successful in large-scale breeding programs, the development of affordable, large-scale genotyping technologies and supporting infrastructure will be essential, thereby further enhancing livestock productivity, sustainability and disease management in animal breeding programs.

    ACKNOWLEDGEMENT

    The author conducted this work independently and sincerely acknowledges the open-access databases and scientific literature platforms that made this review possible.
     
    Disclaimers
     
    The views and conclusions expressed in this article are solely those of the authors and do not necessarily represent the views of their affiliated institutions. The authors are responsible for the accuracy and completeness of the information provided, but do not accept any liability for any direct or indirect losses resulting from the use of this content.
     
    Informed consent
     
    Since this is a review article so, there is no use of animal procedures for experiments.

    CONFLICT OF INTEREST

    The authors declare that there are no conflicts of interest regarding the publication of this article. No funding or sponsorship influenced the design of the study, data collection, analysis, decision to publish, or preparation of the manuscript.

    REFERENCES


    1. Andersson, L. (2001). Genetic dissection of phenotypic diversity in farm animals. Nature Reviews Genetics. 2(2): 130-138.

    2. Ansari-Mahyari, S., Sorensen, A.C., Lund, M.S., Thomsen, H. and Berg, P. (2008). Across-family marker-assisted selection using selective genotyping strategies in dairy cattle breeding schemes. Journal of Dairy Science. 91(4): 1628-1639.

    3. Bensch, S. and Akesson, M. (2005). Ten years of AFLP in ecology and evolution: Why so few animals. Molecular Ecology. 14(10): 2899-2914.

    4. Berkowicz, E.W., Magee, D.A., Sikora, K.M., Berry, D.P., Howard, D.J., Mullen, M.P., Evans, R.D., Spillane, C. and MacHugh, D.E. (2011). Single nucleotide polymorphisms at the imprinted bovine insulin-like growth factor 2 (IGF2) locus are associated with dairy performance in Irish Holstein- Friesian cattle. The Journal of Dairy Research. 78(1): 1-8. 

    5. Bidichandani, S.I., Ashizawa, T. and Patel, P.I. (1998). The GAA triplet- repeat expansion in Friedreich ataxia interferes with transcription and may be associated with an unusual DNA structure. The American Journal of Human Genetics. 62(1): 111-121.

    6. Botstein, D., White, R.L., Skolnick, M. and Davis, R.W. (1980). Construction of a genetic linkage map in man using restriction fragment length polymorphisms. American Journal of Human Genetics. 32(3): 314.

    7. Cahyadi, M., Jo, C. and Lee, J.H. (2014). Quantitative trait loci and candidate genes for the economic traits in meat-type chicken. World’s Poultry Science Journal. 70(2): 329-342. 

    8. Cao, X., Wu, Z., Zhou, R., Jiang, F.L. and Yang, Z.E. (2015). A novel random amplified polymorphic DNA-based strategy for genetic diversity analysis and identification of tomatoes. Genetics and Molecular Research. 14(1): 1650-1661. 

    9. Carr, C.C., Morgan, J.B., Berg, E.P., Carter, S.D. and Ray, F.K. (2006). Growth performance, carcass composition, quality and enhancement treatment of fresh pork identified through deoxyribonucleic acid marker-assisted selection for the Rendement Napole gene. Journal of Animal Science. 84(4): 910-917.

    10. Civanova, K., Putnova, L. and Dvorak, J. (2006). Analysis of micro- satellite set for biodiversity studies in horses. Acta Fytotechnica et Zootechnica. 47: 39-40. 

    11. Deb, R., Singh, U., Kumar, S. and Sharma, A. (2012). Molecular Marker Assisted Selection for Disease Resistance Breeds of Cattle. Agricultural Research Updates. Nova Science Publishers, New York.  pp. 153.

    12. Dekkers, J.C.M. (2004). Commercial application of marker- and gene- assisted selection in livestock: Strategies and lessons. Journal of Animal Science. 82(E-Suppl): E313-E328.

    13. Dodgson, J.B., Cheng, H.H. and Okimoto, R. (1997). DNA marker technology: A revolution in animal genetics. Poultry Science. 76(8): 1108-1114.

    14. Edea, Z., Dadi, H., Kim, S.W., Dessie, T. and Kim, K.S. (2012). Comparison of SNP variation and distribution in indigenous Ethiopian and Korean Cattle (Hanwoo) populations. Genomics and Informatics. 10(3): 200-205.

    15. Erhardt, G and Weimann, C. (2007). Use of molecular markers for evaluation of genetic diversity and in animal production. Archivos Latinoamericanos de Producción Animal. 15(5): 63-66. 

    16. Gao, Y., Zhang, J., Li, J. and Wang, X. (2020). Ethical considerations and regulation of genome editing in animals. Translational Animal Science. 4(2): 854-862.

    17. Goddard, M.E. and Hayes, B.J. (2009). Mapping genes for complex traits in domestic animals and their use in breeding programmes. Nature Reviews Genetics. 10(6): 381-391.

    18. Gorbach, D., Makgahlela, M., Reecy, J., Kemp, S., Baltenweck, I., Ouma, R. and Moore, S. (2010). Use of SNP genotyping to determine pedigree and breed composition of dairy cattle in Kenya. Journal of Animal Breeding and Genetics. 127(5): 348-351. 

    19. Hasan, N., Choudhary, S., Naaz, N., Sharma, N. and Laskar, R.A. (2021). Recent advancements in molecular marker-assisted selection and applications in plant breeding programmes. Journal of Genetic Engineering and Biotechnology. 19(1): 1-26.

    20. Hayes, B. and Goddard, M.E. (2003). Evaluation of marker assisted selection in pig enterprises. Livestock Production Science. 81(2-3): 197-211.

    21. He, F., Sun, D., Zhang, Y. and Wang, Y. (2006). Association of prolactin gene polymorphisms with milk production traits in cattle. Journal of Dairy Science. 89(9): 3503-3508.

    22. Hillel, J., Schaap, T., Haberfeld, A., Jeffreys, A.J., Plotzky, Y., Cahaner, A. and Lavi, U. (1990). DNA fingerprints applied to gene introgression in breeding programs. Genetics. 124(3): 783-789.

    23. Hsu, P.D., Lander, E.S. and Zhang, F. (2014). Development and applications of CRISPR-Cas9 for genome engineering. Cell. 157(6): 1262-1278. 

    24. Ilie, D. E., Kusza, S., Sauer, M. and Gavojdian, D. (2018). Genetic characterization of indigenous goat breeds in Romania and Hungary with a special focus on genetic resistance to mastitis and gastrointestinal parasitism based on 40 SNPs. PLoS ONE. 13(5): e0197051.

    25. Jiang, G.L. (2013). Molecular markers and marker-assisted breeding. Plant Breeding from Laboratories to Fields. IntechOpen Publishers, London. pp. 45-85.

    26. Karuthadurai, T., Chakravarthy, A. K., Kumaresan, A., Kour, A., Nag, B.P., Rana, E., Ravikumar, D. and Yousuf, S. (2019). Identification of genetic marker for prolactin gene related to milk yield in pedigreed Sahiwal population. Indian Journal of Animal Research. 53(5): 566-571. doi: 10.18805/ijar.B-3578.

    27. Kawaguchi, F., Tsuchimura, M., Oyama, K., Matsuhashi, T., Maruyama, S., Mannen, H. and Sasazaki, S. (2020). Effect of DNA markers on carcass and fertility traits in Japanese Black cattle. Animal Biology and Animal Husbandry. 63(1): 9-11.

    28. Kew, O. and Nottay, B. (1986). Oligonucleotide Fingerprinting in the Investigations of Outbreaks of Viral Disease. Concepts in Viral Pathogenesis II. Springer Publishers, New York. pp. 317-323. 

    29. Lerner, I.M. (1958). The Genetic Basis of Selection. Chapman and Hall, Ltd., London and John Wiley and Sons, Inc., New York. pp. 298.

    30. Lewis, A. and Redrup, L. (2005). Genetic imprinting: Conflict at the Callipyge locus. Current Biology. 15(8): 291-294.

    31. Lin, B.Z., Sasazaki, S. and Mannen, H. (2010). Genetic diversity and structure in Bos taurus and Bos indicus populations analyzed by SNP markers. Animal Science Journal. 81(3): 281-289.

    32. Lindhe, B. and Philipsson, J. (1998). Conventional breeding programmes and genetic resistance to animal diseases. Revue Scientifique et Technique. 17(1):  291-301.

    33. Lipkin, E., Bagnato, A. and Soller, M. (2008). Expected effects on protein yield of marker-assisted selection at quantitative trait loci affecting milk yield and milk protein percentage. Journal of Dairy Science. 91(7): 2857-2863.

    34. Liu, R., Sun, Y., Zhao, G., Wang, F., Wu, D., Zheng, M. and Wen, J. (2013). Genome-wide association study identifies loci and candidate genes for egg production traits in chickens. PLoS ONE. 8(4): e61172.

    35. Magee, D.A., Berry, D.P., Berkowicz, E.W., Sikora, K.M., Howard, D.J., Mullen, M.P., Evans, R.D., Spillane, C. and MacHugh, D.E. (2011). Single nucleotide polymorphisms within the bovine DLK1-DIO3 imprinted domain are associated with economically important production traits in cattle. Journal of Heredity. 102(1): 94-101.

    36. Mammadov, J., Aggarwal, R., Buyyarapu, R. and Kumpatla, S. (2012). SNP markers and their impact on plant breeding. International Journal of Plant Genomics. 2012(1): 728398.

    37. Mardis, E.R. (2008). Next-generation DNA sequencing methods. Annual Review of Genomics and Human Genetics. 9(1): 387-402.

    38. Meuwissen, T.H., Hayes, B.J. and Goddard, M. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics. 157(4): 1819-1829.

    39. Mitra, A., Yadav, B.R., Ganai, N.A. and Balakrishnan, C.R. (1999). Molecular markers and their applications in livestock improvement. Current Science. 77(8): 1045-1053.

    40. Moniruzzaman, M., Khatun, R. and Mintoo, A.A. (2014). Application of marker assisted selection for livestock improvement in Bangladesh. Bangladesh Veterinarian. 31(1): 1-11.

    41. Mortazavi, A., Williams, B.A., McCue, K., Schaeffer, L. and Wold, B. (2008). Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nature Methods. 5(7): 621-628.

    42. Mueller, U.G. and Wolfenbarger, L.L. (1999). AFLP genotyping and fingerprinting. Trends in Ecology and Evolution. 14(10): 389-394.

    43. Mullis, K. and Faloona, F. (1987). Specific synthesis of DNA in vitro via a polymerase-catalyzed chain reaction. Methods in Enzymology. 155: 335-350.

    44. Munsaka, S., Kaimoyo, E., Arunachalam, K., Ng’Uni, D., Chikoti, P. and Sharma, N. (2024). Characterization of selected zambian rice (Oryza sativa) accessions using simple sequence repeat (SSR) Markers. Indian Journal of Agricultural Research. 58(1): 11-16. doi: 10.18805/IJARe.A-5894. 

    45. Naeem, M., Fatima, A., Raut, R., Kumar, R., Tushar, Z., Rahman, F. and Bourassa, D. (2026). Management and genetic approaches for enhancing meat quality in poultry production Systems: A Comprehensive Review. Poultry. 5(1): 4.

    46. O’Brien, S.J. (1991). Mammalian genome mapping: Lessons and prospects. Current Opinion in Genetics and Development. 1(1): 105-111.

    47. Oxford Reference. (2025). Oligonucleotide Fingerprinting. Oxford Reference. Oxford University Press. Accessed March 30, 2025. https://www.oxfordreference.com.

    48. Pheirim, R., Konjengbam, N.S. and Mahanta, M. (2022). Molecular markers for powdery mildew in pea (Pisum sativum L.): A review. Legume Research- An International Journal.  45(4): 399-409. doi: 10.18805/LR-4740.

    49. Pongpisantham, B. (1994). Applying genotype and marker assisted selection for the improvement of quantitative traits in poultry. M.S. Thesis. University of New England: 121.

    50. Premkrishnan, B.V. and Arunachalam, V. (2012). Silico RAPD priming sites in expressed sequences and iSCAR markers for oil palm. International Journal of Genomics. 2012(1): 913709. 

    51. 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. and 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.

    52. Ribaut, J.M. and Ragot, M. (2007). Marker-assisted selection to improve drought adaptation in maize: The backcross approach, perspectives, limitations and alternatives. Journal of Experimental Botany. 58(2): 351-360.

    53. Rincent, R., Laloe, D., Nicolas, S., Altmann, T., Brunel, D., Revilla, P., Rodriguez, V.M., Moreno-Gonzalez, J., Melchinger, A., Bauer, E., Schoen, C.C. (2012). Maximizing the reliability of genomic selection by optimizing the calibration set of reference individuals: Comparison of methods in two diverse groups of maize inbreds (Zea mays L.). Genetics. 192(2): 715-728.

    54. Rothschild, M.F., Jacobson, C., Vaske, D., Tuggle, C., Wang, L., Short, T., Eckardt, G., Sasaki, S., Vincent, A., McLaren, D., Southwood, O., van der Steen, H., Mileham, A. and Plastow, G. (1996). The estrogen receptor locus is associated with a major gene influencing litter size in pigs. Proceedings of the National Academy of Sciences. 93(1): 201-205.

    55. Ruane, J. and Colleau, J.J. (1996). Marker-assisted selection for a sex-limited character in a nucleus breeding population. Journal of Dairy Science. 79(9): 1666-1678.

    56. Salisu, I.B., Olawale, A.S., Jabbar, B., Koloko, B.L., Abdurrahaman, S.L., Amin, A.B. and Ali, Q. (2018). Molecular markers and their Potentials in Animal Breeding and Genetics. Nigerian Journal of Animal Science. 20(3): 29-48.

    57. Sarika, Arora, V., Iquebal, M.A., Rai, A. and Kumar, D. (2013). In silico mining of putative microsatellite markers from whole genome sequence of water buffalo (Bubalus bubalis) and development of first BuffSatDB. BMC genomics. 14: 1-8.

    58. Sartori, R., Gümen, A.H.M.E.T., Guenther, J.N., Souza, A.H., Caraviello, D.Z. and Wiltbank, M.C. (2006). Comparison of artificial insemination versus embryo transfer in lactating dairy cows. Theriogenology. 65(7): 1311-1321.

    59. Sharma, P., Doultani, S., Hadiya, K.K., George, L.B. and Highland, H.N. (2024). Overview of marker-assisted selection in animal breeding. Journal of Advances in Biology and Biotechnology. 27(5): 303-318.

    60. Shen, W., Li, L., Pan, Q.J., Qin, G.Q. and Geng, S.M. (2004). Genetic effect of the marker assisted selection on economic traits of goats. Yi chuan. 26(5): 625-630.

    61. Sikora, K.M., Magee, D.A., Berkowicz, E.W., Berry, D.P., Howard, D.J., Mullen, M.P., Evans, R.D., MacHugh, D.E. and Spillane, C. (2011). DNA sequence polymorphisms within the bovine guanine nucleotide-binding protein Gs subunit alpha (Gsα)- encoding (GNAS) genomic imprinting domain are associated with performance traits. BMC Genetics. 12(4): 1-6.

    62. Singh, B.D. (2007). Fundamentals of Genetics. Kalyani Publishers, Ludhiana, Punjab, India. pp. 161.

    63. Singh, U., Deb, R., Alyethodi, R.R., Alex, R., Kumar, S., Chakraborty, S., Dhama, K. and Sharma, A. (2014). Molecular markers and their applications in cattle genetic research: A review. Biomarkers and Genomic Medicine. 6(2): 49-58.

    64. Smith, C. and Simpson, S.P. (1986). The use of genetic polymorphisms in livestock improvement. Journal of Animal Breeding and Genetics. 103(3): 205-217.

    65. Soller, M. (1994). Marker assisted selection an overview. Animal Biotechnology. 5(2): 193-207. 

    66. Soller, M. and Beckmann, J.S. (1983). Genetic polymorphism in varietal identification and genetic improvement. Theoretical and Applied Genetics. 67: 25-33.

    67. Song, S., Yao, N., Yang, M., Liu, X., Dong, K., Zhao, Q., Yabin, P., Xiaohong, H., Weijun, G., Ning, Y., Yuehui, M. and Wang, X. (2016). Exome sequencing reveals genetic differentiation due to high-altitude adaptation in the Tibetan cashmere goat (Capra hircus). BMC Genomics. 17: 1-12.

    68. Sunnucks, P. (2000). Efficient genetic markers for population biology. Trends in Ecology and Evolution. 15(5): 199-203.

    69. Teneva, A. and Petrovic, M.P. (2010). Application of molecular markers in livestock improvement. Biotechnology in Animal Husbandry. 26(3-4): 135-154.

    70. VanRaden, P.M. (2008). Efficient methods to compute genomic predictions. Journal of Dairy Science. 91(11): 4414-4423.

    71. Verma, A., Parmar, S., Patel, N. and Janmeda, M. (2025). Impact of marker assisted selection (MAS) on economy of production trait in animals: A review. International Journal of Veterinary Sciences and Animal Husbandry. 10(11): 340-344.

    72. Vos, P., Hogers, R., Bleeker, M., Reijans, M., Van de Lee, T., Hornes, M., Frijters, A., Pot, J., Peleman, J., Kuiper, M. and Zabeau, M. (1995). AFLP: A new technique for DNA fingerprinting. Nucleic Acids Research. 23(21): 4407-4414.

    73. Wakchaure, R., Ganguly, S., Praveen, P.K., Kumar, A., Sharma, S. and Mahajan, T.J. J.D.M.T. (2015). Marker assisted selection (MAS) in animal breeding: A review. Journal of Drug Metabolism and Toxicology. 6(5): 127.

    74. Wang, X., Cai, B., Zhou, J., Zhu, H., Niu, Y., Ma, B., Honghao, Y., Anmin, L., Hailong, Y., Qiaoyan, S., Lei, S., Xiaoe, Z., Jinlian, H., Xingxu, H., Lei, Q. and Chen, Y. (2016). Disruption of FGF5 in cashmere goats using CRISPR/Cas9 results in more secondary hair follicles and longer fibers. PLoS ONE. 11(10): e0164640.

    75. Welsh, J. and Mcclelland, M. (1990). Fingerprinting genomes using PCR with arbitrary primers. Nucleic Acids Research. 18(24): 7213-7218. 

    76. Williams, J.G.K., Kubelik, A.R., Livak, K.J., Rafalski, J.A. and Tingey, S.V. (1990). DNA polimorphism amplified by arbitrary primers are useful as genetic markers. Nucleic Acids Research. 18(22): 6531-6535. 

    77. Williams, J.L. (2005). The use of marker-assisted selection in animal breeding and biotechnology. Revue Scientifique et Technique- Office International des Epizooties. 24(1): 379.
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