Nidhi Sukhija¹, Kanaka K K¹, Jayakumar Sivalingam²*, Komal Jaglan³, Pallavi Rathi¹, Rangasai Chandra Goli¹ and Chethan Raj⁴

¹ICAR-National Dairy Research Institute, Karnal, Haryana, India
²ICAR-Directorate of Poultry Research, Hyderabad, India
³Lala Lajpat Rai University of Veterinary and Animal Sciences, Hisar, Haryana, India
⁴ICAR-Indian Veterinary Research Institute, Izatnagar, UP, India

*Corresponding Author: Jayakumar Sivalingam, ICAR-Directorate of Poultry Research, Hyderabad, India.

Received: May 23, 2023; Published: June 03, 2023

Abstract

Advancements in Next Generation Sequencing have led to an increased exploration of the genome and transcriptome to uncover genetic variants associated with phenotypic traits. Genome-wide association studies (GWAS) and genomic predictions play a crucial role in identifying significant genetic variants that contribute to complex traits. However, most of these variants are found in non-coding regions of the genome, making their functional annotation and interpretation challenging. This review highlights the importance of characterizing and prioritizing non-coding variants and their effects on regulatory elements in livestock genomics. Regulatory elements such as promoters, enhancers, silencers, and non-coding RNAs coordinate gene expression and are critical for understanding the underlying mechanisms of traits. The abstract also discusses various tools and methods for annotating and predicting the effects of regulatory variants, as well as validation platforms for studying their functional impact. Comprehensive functional annotation of non-coding variants is essential for gaining insights into the genetic architecture of complex traits and improving genetic selection strategies in livestock breeding programs.

Keywords: GWAS; Regulatory Variants, SNPs; Tools; RNA

References

1. Weng L., et al. “SNP-based pathway enrichment analysis for genome-wide association studies”. BMC Bioinformatics1 (2011): 1-9.
2. Glazier A M., et al. “Finding genes that underlie complex traits”. Science5602 (2002): 2345-2349.
3. Tak Y G and Farnham P J. “Making sense of GWAS: using epigenomics and genome engineering to understand the functional relevance of SNPs in non-coding regions of the human genome”. Epigenetics and Chromatin1 (2015): 1-18.
4. Edwards S L., et al. “Beyond GWASs: illuminating the dark road from association to function”. The American Journal of Human Genetics 5 (2013): 779-797.
5. Schaub M A., et al. “Linking disease associations with regulatory information in the human genome”. Genome Research 9 (2012): 1748-1759.
6. Mackay TF., et al. “The genetics of quantitative traits: challenges and prospects”. Nature Reviews Genetics 8 (2009): 565-577.
7. Gallagher MD and Chen-Plotkin AS. “The post-GWAS era: from association to function”. The American Journal of Human Genetics 5 (2018): 717-730.
8. Koufariotis L., et al. “Regulatory and coding genome regions are enriched for trait associated variants in dairy and beef cattle”. BMC Genomics 1 (2014): 1-16.
9. Riethoven J J M. “Regulatory regions in DNA: promoters, enhancers, silencers, and insulators”. Computational Biology of Transcription Factor Binding (2010): 33-42.
10. Daetwyler H D., et al. “Genomic prediction in animals and plants: simulation of data, validation, reporting, and benchmarking”. Genetics 2 (2013): 347-365.
11. Bickhart D M and Liu GE. “Identification of candidate transcription factor binding sites in the cattle genome”. Genomics, Proteomics and Bioinformatics3 (2013): 195-198.
12. Wagner VA., et al. “DNA variants within the 5'-flanking region of milk-protein-encoding genes II. The β-lactoglobulin-encoding gene. TAG. Theoretical and applied genetics”. Theoretische und angewandte Genetik1 (1994): 121–126.
13. Khurana E., et al. “Role of non-coding sequence variants in cancer”. Nature Reviews Genetics 17.2 (2016): 93-108.
14. Boyle A P., et al. “Annotation of functional variation in personal genomes using RegulomeDB”. Genome Research9 (2012): 1790-1797.
15. Ward LD and Kellis M. “HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants”. Nucleic Acids ResearchD1 (2012): D930-D934.
16. Coetzee S G., et al. “FunciSNP: an R/bioconductor tool integrating functional non-coding data sets with genetic association studies to identify candidate regulatory SNPs”. Nucleic Acids Research18 (2012): e139-e139.
17. Guo Y., et al. “Enlight: web-based integration of GWAS results with biological annotations”. Bioinformatics2 (2015): 275-276.
18. Ritchie G R and Flicek P. “Computational approaches to interpreting genomic sequence variation”. Genome Medicine 6 (2014): 1-11.
19. Kircher M., et al. “A general framework for estimating the relative pathogenicity of human genetic variants”. Nature Genetics3 (2014): 310-315.
20. Quang D., et al. “DANN: a deep learning approach for annotating the pathogenicity of genetic variants”. Bioinformatics 5 (2014): 761-763.
21. Huang YF., et al. “Fast, scalable prediction of deleterious noncoding variants from functional and population genomic data”. Nature Genetics4 (2017): 618-624.
22. Rogers M F., et al. “FATHMM-XF: accurate prediction of pathogenic point mutations via extended features”. Bioinformatics3 (2018): 511-513.
23. Cai Z., et al. “Dissecting closely linked association signals in combination with the mammalian phenotype database can identify candidate genes in dairy cattle”. BMC Genetics 20 (2019): 1-12.
24. Nishizaki S S and Boyle AP. “Mining the unknown: assigning function to noncoding single nucleotide polymorphisms”. Trends in Genetics1 (2017): 34-45.
25. Lum L S., et al. “Polymorphisms of bovine β-lactoglobulin promoter and differences in the binding affinity of activator protein-2 transcription factor”. Journal of Dairy Science7 (1997): 1389-1397.
26. Drögemüller C., et al. “Genetic mapping of the belt pattern in Brown Swiss cattle to BTA3”. Animal Genetics 2 (2009): 225-229.
27. Fontanesi L., et al. “Haplotype variability in the bovine MITF gene and association with piebaldism in Holstein and Simmental cattle breeds”. Animal Genetics3 (2012): 250-256.
28. Jansen S., et al. “Assessment of the genomic variation in a cattle population by re-sequencing of key animals at low to medium coverage”. BMC Genomics 14 (2013): 1-9.
29. Hauswirth R., et al. “Mutations in MITF and PAX3 cause “splashed white” and other white spotting phenotypes in horses”. PLoS Genetics 4 (2012): e1002653.
30. Baranowska Körberg I., et al. “A simple repeat polymorphism in the MITF-M promoter is a key regulator of white spotting in dogs”. PLoS One 8 (2014): e104363.
31. Negro S., et al. “Association analysis of KIT, MITF, and PAX3 variants with white markings in Spanish horses”. Animal Genetics 3 (2017): 349-352.
32. Hofstetter S., et al. “A non‐coding regulatory variant in the 5′‐region of the MITF gene is associated with white‐spotted coat in Brown Swiss cattle”. Animal genetics 1 (2019): 27-32.
33. Gui L S., et al. “Detection of polymorphisms in the promoter of bovine SIRT1 gene and their effects on intramuscular fat content in Chinese indigenous cattle”. Gene 700 (2019): 47-51.

Citation

Citation: Jayakumar Sivalingam., et al. “Solutions to Post-GWAS Regulatory Variants in Bovine".Acta Scientific Veterinary Sciences 5.7 (2023): 11-15.

Copyright

Copyright: © 2023 Jayakumar Sivalingam., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.