BACKGROUND: Genetic markers can be used to identify and verify the origin of individuals. Motivation for the inference of ancestry ranges from conservation genetics to forensic analysis. High density assays featuring Single Nucleotide Polymorphism (SNP) markers can be exploited to create a reduced panel containing the most informative markers for these purposes. The objectives of this study were to evaluate methods of marker selection and determine the minimum number of markers from the BovineSNP50 BeadChip required to verify the origin of individuals in European cattle breeds. Delta, Wright's FST, Weir & Cockerham's FST and PCA methods for population differentiation were compared. The level of informativeness of each SNP was estimated from the breed specific allele frequencies. Individual assignment analysis was performed using the ranked informative markers. Stringency levels were applied by log-likelihood ratio to assess the confidence of the assignment test. RESULTS: A 95% assignment success rate for the 384 individually genotyped animals was achieved with <80, <100, <140 and <200 SNP markers (with increasing stringency threshold levels) across all the examined methods for marker selection. No further gain in power of assignment was achieved by sampling in excess of 200 SNP markers. The marker selection method that required the lowest number of SNP markers to verify the animal's breed origin was Wright's FST (60 to 140 SNPs depending on the chosen degree of confidence). Certain breeds required fewer markers (<100) to achieve 100% assignment success. In contrast, closely related breeds require more markers (~200) to achieve>95% assignment success. The power of assignment success, and therefore the number of SNP markers required, is dependent on the levels of genetic heterogeneity and pool of samples considered. CONCLUSIONS: While all SNP selection methods produced marker panels capable of breed identification, the power of assignment varied markedly among analysis methods. Thus, with effective exploration of available high density genetic markers, a diagnostic panel of highly informative markers can be produced.
BACKGROUND: Genetic markers can be used to identify and verify the origin of individuals. Motivation for the inference of ancestry ranges from conservation genetics to forensic analysis. High density assays featuring Single Nucleotide Polymorphism (SNP) markers can be exploited to create a reduced panel containing the most informative markers for these purposes. The objectives of this study were to evaluate methods of marker selection and determine the minimum number of markers from the BovineSNP50 BeadChip required to verify the origin of individuals in European cattle breeds. Delta, Wright's FST, Weir & Cockerham's FST and PCA methods for population differentiation were compared. The level of informativeness of each SNP was estimated from the breed specific allele frequencies. Individual assignment analysis was performed using the ranked informative markers. Stringency levels were applied by log-likelihood ratio to assess the confidence of the assignment test. RESULTS: A 95% assignment success rate for the 384 individually genotyped animals was achieved with <80, <100, <140 and <200 SNP markers (with increasing stringency threshold levels) across all the examined methods for marker selection. No further gain in power of assignment was achieved by sampling in excess of 200 SNP markers. The marker selection method that required the lowest number of SNP markers to verify the animal's breed origin was Wright's FST (60 to 140 SNPs depending on the chosen degree of confidence). Certain breeds required fewer markers (<100) to achieve 100% assignment success. In contrast, closely related breeds require more markers (~200) to achieve>95% assignment success. The power of assignment success, and therefore the number of SNP markers required, is dependent on the levels of genetic heterogeneity and pool of samples considered. CONCLUSIONS: While all SNP selection methods produced marker panels capable of breed identification, the power of assignment varied markedly among analysis methods. Thus, with effective exploration of available high density genetic markers, a diagnostic panel of highly informative markers can be produced.
Authors: R Ciampolini; V Cetica; E Ciani; E Mazzanti; X Fosella; F Marroni; M Biagetti; C Sebastiani; P Papa; G Filippini; D Cianci; S Presciuttini Journal: J Anim Sci Date: 2006-01 Impact factor: 3.159
Authors: R Negrini; L Nicoloso; P Crepaldi; E Milanesi; L Colli; F Chegdani; L Pariset; S Dunner; H Leveziel; J L Williams; P Ajmone Marsan Journal: Anim Genet Date: 2008-11-11 Impact factor: 3.169
Authors: Gane Ka-Shu Wong; Bin Liu; Jun Wang; Yong Zhang; Xu Yang; Zengjin Zhang; Qingshun Meng; Jun Zhou; Dawei Li; Jingjing Zhang; Peixiang Ni; Songgang Li; Longhua Ran; Heng Li; Jianguo Zhang; Ruiqiang Li; Shengting Li; Hongkun Zheng; Wei Lin; Guangyuan Li; Xiaoling Wang; Wenming Zhao; Jun Li; Chen Ye; Mingtao Dai; Jue Ruan; Yan Zhou; Yuanzhe Li; Ximiao He; Yunze Zhang; Jing Wang; Xiangang Huang; Wei Tong; Jie Chen; Jia Ye; Chen Chen; Ning Wei; Guoqing Li; Le Dong; Fengdi Lan; Yongqiao Sun; Zhenpeng Zhang; Zheng Yang; Yingpu Yu; Yanqing Huang; Dandan He; Yan Xi; Dong Wei; Qiuhui Qi; Wenjie Li; Jianping Shi; Miaoheng Wang; Fei Xie; Jianjun Wang; Xiaowei Zhang; Pei Wang; Yiqiang Zhao; Ning Li; Ning Yang; Wei Dong; Songnian Hu; Changqing Zeng; Weimou Zheng; Bailin Hao; Ladeana W Hillier; Shiaw-Pyng Yang; Wesley C Warren; Richard K Wilson; Mikael Brandström; Hans Ellegren; Richard P M A Crooijmans; Jan J van der Poel; Henk Bovenhuis; Martien A M Groenen; Ivan Ovcharenko; Laurie Gordon; Lisa Stubbs; Susan Lucas; Tijana Glavina; Andrea Aerts; Pete Kaiser; Lisa Rothwell; John R Young; Sally Rogers; Brian A Walker; Andy van Hateren; Jim Kaufman; Nat Bumstead; Susan J Lamont; Huaijun Zhou; Paul M Hocking; David Morrice; Dirk-Jan de Koning; Andy Law; Neil Bartley; David W Burt; Henry Hunt; Hans H Cheng; Ulrika Gunnarsson; Per Wahlberg; Leif Andersson; Ellen Kindlund; Martti T Tammi; Björn Andersson; Caleb Webber; Chris P Ponting; Ian M Overton; Paul E Boardman; Haizhou Tang; Simon J Hubbard; Stuart A Wilson; Jun Yu; Jian Wang; Huanming Yang Journal: Nature Date: 2004-12-09 Impact factor: 49.962
Authors: Lakshmi K Matukumalli; Cynthia T Lawley; Robert D Schnabel; Jeremy F Taylor; Mark F Allan; Michael P Heaton; Jeff O'Connell; Stephen S Moore; Timothy P L Smith; Tad S Sonstegard; Curtis P Van Tassell Journal: PLoS One Date: 2009-04-24 Impact factor: 3.240
Authors: Samantha Wilkinson; Zen H Lu; Hendrik-Jan Megens; Alan L Archibald; Chris Haley; Ian J Jackson; Martien A M Groenen; Richard P M A Crooijmans; Rob Ogden; Pamela Wiener Journal: PLoS Genet Date: 2013-04-25 Impact factor: 5.917
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Authors: Samantha Wilkinson; Alan L Archibald; Chris S Haley; Hendrik-Jan Megens; Richard P M A Crooijmans; Martien A M Groenen; Pamela Wiener; Rob Ogden Journal: BMC Genomics Date: 2012-11-15 Impact factor: 3.969