본문 바로가기
KERD
논문

Single nucleotide polymorphism marker combinations for classifying Yeonsan Ogye chicken using a machine learning approach

2

조회

0

다운로드

0

인용

0

참고문헌

키워드

주제분류축산학

초록

Genetic analysis has great potential as a tool to differentiate between different species andbreeds of livestock. In this study, the optimal combinations of single nucleotide polymorphism(SNP) markers for discriminating the Yeonsan Ogye chicken (Gallus gallus domesticus)breed were identified using high-density 600K SNP array data. In 3,904 individuals from 198chicken breeds, SNP markers specific to the target population were discovered through acase-control genome-wide association study (GWAS) and filtered out based on the linkagedisequilibrium blocks. Significant SNP markers were selected by feature selection applyingtwo machine learning algorithms: Random Forest (RF) and AdaBoost (AB). Using a machinelearning approach, the 38 (RF) and 43 (AB) optimal SNP marker combinations for the YeonsanOgye chicken population demonstrated 100% accuracy. Hence, the GWAS and machinelearning models used in this study can be efficiently utilized to identify the optimal combinationof markers for discriminating target populations using multiple SNP markers.

Abstract

Genetic analysis has great potential as a tool to differentiate between different species andbreeds of livestock. In this study, the optimal combinations of single nucleotide polymorphism(SNP) markers for discriminating the Yeonsan Ogye chicken (Gallus gallus domesticus)breed were identified using high-density 600K SNP array data. In 3,904 individuals from 198chicken breeds, SNP markers specific to the target population were discovered through acase-control genome-wide association study (GWAS) and filtered out based on the linkagedisequilibrium blocks. Significant SNP markers were selected by feature selection applyingtwo machine learning algorithms: Random Forest (RF) and AdaBoost (AB). Using a machinelearning approach, the 38 (RF) and 43 (AB) optimal SNP marker combinations for the YeonsanOgye chicken population demonstrated 100% accuracy. Hence, the GWAS and machinelearning models used in this study can be efficiently utilized to identify the optimal combinationof markers for discriminating target populations using multiple SNP markers.

참고문헌 (33)

  1. 박미현, "초위성체를 이용한 한국 재래닭의 원산지 추적 및 식별 방법에 관한 연구", 한국유기농업학회지, 12(4), pp.451~461 (2004), 한국유기농업학회
  2. 서상원, "MS 마커를 이용한 토종닭 브랜드의 유전적 특성 및 개체 식별력 분석", 한국축산학회지, 55(3), pp.185~194 (2013), 한국축산학회
  3. Dharmayanthi AB, "The origin and evolution of fibromelanosis in domesticated chickens : genomic comparison of Indonesian Cemani and Chinese Silkie breeds", PLOS ONE, 12, pp.e0173147~ (2017)[DOI]
  4. Dorshorst B, "A complex genomic rearrangement involving the endothelin 3 locus causes dermal hyperpigmentation in the chicken", PLOS Genet, 7, pp.e1002412~ (2011)[DOI]
  5. 최누리, "Discrimination of the commercial Korean native chicken population using microsatellite markers", 한국축산학회지, 57(2), pp.1~8 (2015), 한국축산학회[DOI]
  6. 오재돈, "Genetic Traceability of Black Pig Meats Using Microsatellite Markers", Animal Bioscience, 27(7), pp.926~931 (2014), 아세아·태평양축산학회[DOI]
  7. Serrano M, "Microsatellite based genetic diversity and population structure of the endangered Spanish Guadarrama goat breed", BMC Genet, 10, pp.61~ (2009)[DOI]
  8. Fischer MC, "Estimating genomic diversity and population differentiation : an empirical comparison of microsatellite and SNP variation in Arabidopsis halleri", BMC Genomics, 18, pp.69~ (2017)[DOI]
  9. Karniol B, "Development of a 25-plex SNP assay for traceability in cattle", Anim Genet, 40, pp.353~356 (2009)[DOI]
  10. Kranis A, "Development of a high density 600K SNP genotyping array for chicken", BMC Genomics, 14, pp.59~ (2013)[DOI]
  11. Malomane DK, "The SYNBREED chicken diversity panel : a global resource to assess chicken diversity at high genomic resolution", BMC Genomics, 20, pp.345~ (2019)[DOI]
  12. Groenen MAM, "The development and characterization of a 60K SNP chip for chicken", BMC Genomics, 12, pp.274~ (2011)[DOI]
  13. Purcell S, "PLINK : a tool set for whole-genome association and population-based linkage analyses", Am J Hum Genet, 81, pp.559~575 (2007)[DOI]
  14. Breiman L, "Random forests", Mach Learn, 45, pp.5~32 (2001)[DOI]
  15. Alfaro E, "adabag : an R package for classification with boosting and bagging", J Stat Softw, 54, pp.1~35 (2013)[DOI]
  16. Crisci C, "A review of supervised machine learning algorithms and their applications to ecological data", Ecol Model, 240, pp.113~122 (2012)[DOI]
  17. Uddin S, "Comparing different supervised machine learning algorithms for disease prediction", BMC Med Inform Decis Mak, 19(1), pp.281~ (2019)[DOI]
  18. Zhang MQ., "Identification of protein coding regions in the human genome by quadratic discriminant analysis", Proc Natl Acad Sci USA, 94(2), pp.565~568 (1997)[DOI]
  19. Kuhn M, "Building predictive models in R using the caret package", J Stat Softw, 28, pp.1~26 (2008)[DOI]
  20. Das S, "Next-generation genotype imputation service and methods", Nature Genet, 48, pp.1284~1287 (2016)[DOI]
  21. Hoffmann I, "Climate change and the characterization, breeding and conservation of animal genetic resources", Anim Genet, 41, pp.32~46 (2010)[DOI]
  22. 박서영, "The Effect of Korean Native Chicken Breed Information on Consumer Sensory Evaluation and Purchase Behavior", 한국축산식품학회지, 42(1), pp.111~127 (2022), 한국축산식품학회[DOI]
  23. Heaton MP, "Selection and use of SNP markers for animal identification and paternity analysis in U. S. beef cattle", Mamm Genome, 13, pp.272~281 (2002)[DOI]
  24. Suekawa Y, "Development of breed identification markers based on a bovine 50K SNP array", Meat Sci, 85, pp.285~288 (2010)[DOI]
  25. Wilkinson S, "Evaluation of approaches for identifying population informative markers from high density SNP chips", BMC Genet, 12, pp.45~ (2011)[DOI]
  26. Johnson RC, "Accounting for multiple comparisons in a genome-wide association study(GWAS)", BMC Genomics, 11, pp.724~ (2010)[DOI]
  27. Wallace JG, "Association mapping across numerous traits reveals patterns of functional variation in maize", PLOS Genet, 10, pp.e1004845~ (2014)[DOI]
  28. Bakshi A, "Fast set-based association analysis using summary data from GWAS identifies novel gene loci for human complex traits", Sci Rep, 6, pp.32894~ (2016)[DOI]
  29. Seo D, "Identification of target chicken populations by machine learning models using the minimum number of SNPs", Animals, 11, pp.241~ (2021)[DOI]
  30. Matukumalli LK, "Application of machine learning in SNP discovery", BMC Bioinformatics, 7, pp.4~ (2006)[DOI]
  31. Schiavo G, "A machine learning approach for the identification of population-informative markers from highthroughput genotyping data : application to several pig breeds", Animal, 14, pp.223~232 (2020)[DOI]
  32. Xu Z, "Breed identification of meat using machine learning and breed tag SNPs", Food Control, 125, pp.107971~ (2021)[DOI]
  33. Bermingham ML, "Application of high-dimensional feature selection : evaluation for genomic prediction in man", Sci Rep, 5, pp.10312~ (2015)[DOI]

관련 논문AI추천

Evaluation of imputation performance based on the single nucleotide polymorphism panel density and the reference population size in Korean native chicken서동원, 이준헌, Choo Hyo Jun, Cho Sunghyun, Kim Minjun, Lee Doo Ho아세아·태평양축산학회 · Animal Bioscience2025 · 제38권 제9호Application of genomic big data to analyze the genetic diversity and population structure of Korean domestic chickens김승창, 이준헌, 진대혁, Dae Cheol Kim, Eunjin Cho, Hee-Jong Roh, Jae-Hwan Kim, Minjun Kim한국축산학회 · 한국축산학회지2023 · 제65권 제5호고밀도 SNP 칩 유전자형 데이터 기계학습 기반 반려견 품종 식별 유전마커 선발강병철, 김형용, 오태윤, 최봉환농업생명과학연구원 · 농업생명과학연구2019 · 제53권 제4호Characterisation of runs of homozygosity and inbreeding coefficients in the red-brown Korean native chickens이준헌, Cho Eunjin, Kim Jaewon, Kim Minjun, MACHARIA JOHN KARIUKI, MUNYANEZA JEAN PIERRE아세아·태평양축산학회 · Animal Bioscience2024 · 제37권 제8호Analysis of runs of homozygosity in Yeonsan Ogye chickens using 600K single nucleotide polymorphism arrays진대혁, Eunjin Cho, Jaewon Kim, Jun Heon Lee, Minjun Kim, Seungchang Kim, Seung-sook Lee한국축산학회 · 한국축산학회지2025 · 제67권 제3호Population structure analysis of Yeonsan Ogye using microsatellite markers조성현, Dongwon Seo, Jun Heon Lee, Minjun Kim, Prabuddha Manjula, Seung Hwan Lee, Seung-Sook Lee한국축산학회 · 한국축산학회지2020 · 제62권 제6호

이 간행물의 다른 논문 (15건)