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Current Genomics

Editor-in-Chief

ISSN (Print): 1389-2029
ISSN (Online): 1875-5488

Review Article

Global and Local Ancestry and its Importance: A Review

Author(s): Rangasai Chandra Goli, Kiyevi G. Chishi, Indrajit Ganguly, Sanjeev Singh, S.P. Dixit, Pallavi Rathi, Vikas Diwakar, Chandana Sree C, Omkar Maharudra Limbalkar, Nidhi Sukhija* and K.K Kanaka*

Volume 25, Issue 4, 2024

Published on: 09 May, 2024

Page: [237 - 260] Pages: 24

DOI: 10.2174/0113892029298909240426094055

Price: $65

Abstract

The fastest way to significantly change the composition of a population is through admixture, an evolutionary mechanism. In animal breeding history, genetic admixture has provided both short-term and long-term advantages by utilizing the phenomenon of complementarity and heterosis in several traits and genetic diversity, respectively. The traditional method of admixture analysis by pedigree records has now been replaced greatly by genome-wide marker data that enables more precise estimations. Among these markers, SNPs have been the popular choice since they are cost-effective, not so laborious, and automation of genotyping is easy. Certain markers can suggest the possibility of a population's origin from a sample of DNA where the source individual is unknown or unwilling to disclose their lineage, which are called Ancestry-Informative Markers (AIMs). Revealing admixture level at the locus-specific level is termed as local ancestry and can be exploited to identify signs of recent selective response and can account for genetic drift. Considering the importance of genetic admixture and local ancestry, in this mini-review, both concepts are illustrated, encompassing basics, their estimation/identification methods, tools/- software used and their applications.

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[1]
Tang, H.; Coram, M.; Wang, P.; Zhu, X.; Risch, N. Reconstructing genetic ancestry blocks in admixed individuals. Am. J. Hum. Genet., 2006, 79(1), 1-12.
[http://dx.doi.org/10.1086/504302] [PMID: 16773560]
[2]
Popejoy, A.B.; Ritter, D.I.; Crooks, K.; Currey, E.; Fullerton, S.M.; Hindorff, L.A.; Koenig, B.; Ramos, E.M.; Sorokin, E.P.; Wand, H.; Wright, M.W.; Zou, J.; Gignoux, C.R.; Bonham, V.L.; Plon, S.E.; Bustamante, C.D. The clinical imperative for inclusivity: Race, ethnicity, and ancestry (REA) in genomics. Hum. Mutat., 2018, 39(11), 1713-1720.
[http://dx.doi.org/10.1002/humu.23644] [PMID: 30311373]
[3]
Long, J.C. The genetic structure of admixed populations. Genetics, 1991, 127(2), 417-428.
[http://dx.doi.org/10.1093/genetics/127.2.417] [PMID: 2004712]
[4]
Pfaff, C.L.; Parra, E.J.; Bonilla, C.; Hiester, K.; McKeigue, P.M.; Kamboh, M.I.; Hutchinson, R.G.; Ferrell, R.E.; Boerwinkle, E.; Shriver, M.D. Population structure in admixed populations: Effect of admixture dynamics on the pattern of linkage disequilibrium. Am. J. Hum. Genet., 2001, 68(1), 198-207.
[http://dx.doi.org/10.1086/316935] [PMID: 11112661]
[5]
Facon, B.; Jarne, P.; Pointier, J.P.; David, P. Hybridization and invasiveness in the freshwater snail Melanoides tuberculata : Hybrid vigour is more important than increase in genetic variance. J. Evol. Biol., 2005, 18(3), 524-535.
[http://dx.doi.org/10.1111/j.1420-9101.2005.00887.x] [PMID: 15842482]
[6]
Martin, A.R.; Gignoux, C.R.; Walters, R.K.; Wojcik, G.L.; Neale, B.M.; Gravel, S.; Daly, M.J.; Bustamante, C.D.; Kenny, E.E. Human demographic history impacts genetic risk prediction across diverse populations. Am. J. Hum. Genet., 2017, 100(4), 635-649.
[http://dx.doi.org/10.1016/j.ajhg.2017.03.004] [PMID: 28366442]
[7]
Solkner, J.; Frkonja, A.; Raadsma, H.W.; Jonas, E.; Thaller, G.; Gootwine, E.; Seroussi, C.; Fuerst, C.; Danner, E.C.; Gredler, B. Estimation of individual levels of admixture in crossbred populations from SNP chip data: Examples with sheep and cattle populations. Interbull Bull., 2010, 42, 62-66.
[8]
Anderson, E.C. Bayesian inference of species hybrids using multilocus dominant genetic markers. Philos. Trans. R. Soc. Lond. B Biol. Sci., 2008, 363(1505), 2841-2850.
[http://dx.doi.org/10.1098/rstb.2008.0043] [PMID: 18508754]
[9]
Larmer, S.; Ventura, R.; Buzanskas, M.E.; Sargolzaei, M.; Schenkel, F.S. Assessing admixture by quantifying breed composition to gain historical perspective on dairy cattle in Canada. 10th World Congress on Genetics Applied to Livestock Production, August 17 - 22, 2014Vancouver, Canada2014, pp. 1-3.
[10]
Chakraborty, R. Gene admixture in human populations: Models and predictions. Am. J. Phys. Anthropol., 1986, 29(S7), 1-43.
[http://dx.doi.org/10.1002/ajpa.1330290502]
[11]
Bryc, K.; Auton, A.; Nelson, M.R.; Oksenberg, J.R.; Hauser, S.L.; Williams, S.; Froment, A.; Bodo, J.M.; Wambebe, C.; Tishkoff, S.A.; Bustamante, C.D. Genome-wide patterns of population structure and admixture in West Africans and African Americans. Proc. Natl. Acad. Sci., 2010, 107(2), 786-791.
[http://dx.doi.org/10.1073/pnas.0909559107] [PMID: 20080753]
[12]
Makina, S.O.; Muchadeyi, F.C.; van Köster, M.E.; MacNeil, M.D.; Maiwashe, A. Genetic diversity and population structure among six cattle breeds in South Africa using a whole genome SNP panel. Front. Genet., 2014, 5, 333.
[http://dx.doi.org/10.3389/fgene.2014.00333] [PMID: 25295053]
[13]
Khayatzadeh, N.; Mészáros, G.; Gredler, B.; Schnyder, U.; Curik, I.; Sölkner, J. Prediction of global and local simmental and red holstein friesian admixture levels in swiss fleckvieh cattle. Poljoprivreda, 2015, 21(1 sup), 63-67.
[http://dx.doi.org/10.18047/poljo.21.1.sup.14]
[14]
Kumar, P.; Freeman, A.R.; Loftus, R.T.; Gaillard, C.; Fuller, D.Q.; Bradley, D.G. Admixture analysis of South Asian cattle. Heredity, 2003, 91(1), 43-50.
[http://dx.doi.org/10.1038/sj.hdy.6800277] [PMID: 12815452]
[15]
Dadi, H.; Tibbo, M.; Takahashi, Y.; Nomura, K.; Hanada, H.; Amano, T. Microsatellite analysis reveals high genetic diversity but low genetic structure in Ethiopian indigenous cattle populations. Anim. Genet., 2008, 39(4), 425-431.
[http://dx.doi.org/10.1111/j.1365-2052.2008.01748.x] [PMID: 18565163]
[16]
Schlötterer, C.; Tautz, D. Slippage synthesis of simple sequence DNA. Nucleic Acids Res., 1992, 20(2), 211-215.
[http://dx.doi.org/10.1093/nar/20.2.211] [PMID: 1741246]
[17]
Innan, H.; Terauchi, R.; Miyashita, N.T. Microsatellite polymorphism in natural populations of the wild plant Arabidopsis thaliana. Genetics, 1997, 146(4), 1441-1452.
[http://dx.doi.org/10.1093/genetics/146.4.1441] [PMID: 9258686]
[18]
McConnell, R.; Middlemist, S.; Scala, C.; Strassmann, J.E.; Queller, D.C. An unusually low microsatellite mutation rate in Dictyostelium discoideum, an organism with unusually abundant microsatellites. Genetics, 2007, 177(3), 1499-1507.
[http://dx.doi.org/10.1534/genetics.107.076067] [PMID: 17947436]
[19]
Mukesh, M.; Sodhi, M.; Bhatia, S. Microsatellite-based diversity analysis and genetic relationships of three Indian sheep breeds. J. Anim. Breed. Genet., 2006, 123(4), 258-264.
[http://dx.doi.org/10.1111/j.1439-0388.2006.00599.x] [PMID: 16882092]
[20]
Hill, E.W.; Gu, J.; Eivers, S.S.; Fonseca, R.G.; McGivney, B.A.; Govindarajan, P.; Orr, N.; Katz, L.M.; MacHugh, D. A sequence polymorphism in MSTN predicts sprinting ability and racing stamina in thoroughbred horses. PLoS One, 2010, 5(1), e8645.
[http://dx.doi.org/10.1371/journal.pone.0008645] [PMID: 20098749]
[21]
Charlier, C.; Coppieters, W.; Rollin, F.; Desmecht, D.; Agerholm, J.S.; Cambisano, N.; Carta, E.; Dardano, S.; Dive, M.; Fasquelle, C.; Frennet, J.C.; Hanset, R.; Hubin, X.; Jorgensen, C.; Karim, L.; Kent, M.; Harvey, K.; Pearce, B.R.; Simon, P.; Tama, N.; Nie, H.; Vandeputte, S.; Lien, S.; Longeri, M.; Fredholm, M.; Harvey, R.J.; Georges, M. Highly effective SNP-based association mapping and management of recessive defects in livestock. Nat. Genet., 2008, 40(4), 449-454.
[http://dx.doi.org/10.1038/ng.96] [PMID: 18344998]
[22]
Sukhija, N.; Malik, A.A.; Devadasan, J.M.; Dash, A.; Bidyalaxmi, K.; Kumar, R.D. Genome-wide selection signatures address trait specific candidate genes in cattle indigenous to arid regions of India. Anim. Biotechnol., 2023, 35, 1-15.
[PMID: 38088885]
[23]
Goli, R.C.; Sukhija, N.; Rathi, P.; Chishi, K.G.; Koloi, S.; Malik, A.A.; Sree C, C.; Purohit, P.B.; Shetkar, M.; K K, K. Unraveling the genetic tapestry of Indian chicken: A comprehensive study of molecular variations and diversity. Ecol. Genet. Genom., 2024, 30, 100220.
[http://dx.doi.org/10.1016/j.egg.2024.100220]
[24]
Kanaka, K.K.; Sukhija, N.; Goli, R.C.; Singh, S.; Ganguly, I.; Dixit, S.P.; Dash, A.; Malik, A.A. On the concepts and measures of diversity in the genomics era. Curr. Plant Biol., 2023, 33, 100278.
[http://dx.doi.org/10.1016/j.cpb.2023.100278]
[25]
Nievergelt, C.M.; Maihofer, A.X.; Shekhtman, T.; Libiger, O.; Wang, X.; Kidd, K.K.; Kidd, J.R. Inference of human continental origin and admixture proportions using a highly discriminative ancestry informative 41-SNP panel. Investig. Genet., 2013, 4(1), 13.
[http://dx.doi.org/10.1186/2041-2223-4-13] [PMID: 23815888]
[26]
Goddard, M.E.; Hayes, B.J. Genomic selection. J. Anim. Breed. Genet., 2007, 124(6), 323-330.
[http://dx.doi.org/10.1111/j.1439-0388.2007.00702.x] [PMID: 18076469]
[27]
Zhang, K.; Sun, F. Assessing the power of tag SNPs in the mapping of quantitative trait loci (QTL) with extremal and random samples. BMC Genet., 2005, 6(1), 51.
[http://dx.doi.org/10.1186/1471-2156-6-51] [PMID: 16236175]
[28]
Hayes, B.J.; Chamberlain, A.J.; McPARTLAN, H.; MacLeod, I.; Sethuraman, L.; Goddard, M.E. Accuracy of marker-assisted selection with single markers and marker haplotypes in cattle. Genet. Res., 2007, 89(4), 215-220.
[http://dx.doi.org/10.1017/S0016672307008865] [PMID: 18208627]
[29]
Eusebi, P.G.; Martinez, A.; Cortes, O. Genomic tools for effective conservation of livestock breed diversity. Diversity, 2019, 12(1), 8.
[http://dx.doi.org/10.3390/d12010008]
[30]
Price, A.L.; Spencer, C.C.; Donnelly, P. Progress and promise in understanding the genetic basis of common diseases. Proc. Royal. Soc. B, 2015, 282(1821)
[http://dx.doi.org/10.1098/rspb.2015.1684]
[31]
Freeman, A.R.; Bradley, D.G.; Nagda, S.; Gibson, J.P.; Hanotte, O. Combination of multiple microsatellite data sets to investigate genetic diversity and admixture of domestic cattle. Anim. Genet., 2006, 37(1), 1-9.
[http://dx.doi.org/10.1111/j.1365-2052.2005.01363.x] [PMID: 16441289]
[32]
Behar, D.M.; Yunusbayev, B.; Metspalu, M.; Metspalu, E.; Rosset, S.; Parik, J.; Rootsi, S.; Chaubey, G.; Kutuev, I.; Yudkovsky, G.; Khusnutdinova, E.K.; Balanovsky, O.; Semino, O.; Pereira, L.; Comas, D.; Gurwitz, D.; Tamir, B.B.; Parfitt, T.; Hammer, M.F.; Skorecki, K.; Villems, R. The genome-wide structure of the Jewish people. Nature, 2010, 466(7303), 238-242.
[http://dx.doi.org/10.1038/nature09103] [PMID: 20531471]
[33]
Shi, W.; Ayub, Q.; Vermeulen, M.; Shao, R.; Zuniga, S.; van der Gaag, K.; de Knijff, P.; Kayser, M.; Xue, Y.; Tyler-Smith, C. A worldwide survey of human male demographic history based on Y-SNP and Y-STR data from the HGDP-CEPH populations. Mol. Biol. Evol., 2010, 27(2), 385-393.
[http://dx.doi.org/10.1093/molbev/msp243] [PMID: 19822636]
[34]
Frkonja, A.; Gredler, B.; Schnyder, U.; Curik, I.; Sölkner, J. Prediction of breed composition in an admixed cattle population. Anim. Genet., 2012, 43(6), 696-703.
[http://dx.doi.org/10.1111/j.1365-2052.2012.02345.x] [PMID: 23061480]
[35]
Lenstra, J.A.; Groeneveld, L.F.; Eding, H.; Kantanen, J.; Williams, J.L.; Taberlet, P.; Nicolazzi, E.L.; Sölkner, J.; Simianer, H.; Ciani, E.; Garcia, J.F.; Bruford, M.W.; Ajmone-Marsan, P.; Weigend, S. Molecular tools and analytical approaches for the characterization of farm animal genetic diversity. Anim. Genet., 2012, 43(5), 483-502.
[http://dx.doi.org/10.1111/j.1365-2052.2011.02309.x] [PMID: 22497351]
[36]
McKay, S.D.; Schnabel, R.D.; Murdoch, B.M.; Matukumalli, L.K.; Aerts, J.; Coppieters, W.; Crews, D.; Neto, E.D.; Gill, C.A.; Gao, C.; Mannen, H.; Wang, Z.; Van Tassell, C.P.; Williams, J.L.; Taylor, J.F.; Moore, S.S. An assessment of population structure in eight breeds of cattle using a whole genome SNP panel. BMC Genet., 2008, 9(1), 37.
[http://dx.doi.org/10.1186/1471-2156-9-37] [PMID: 18492244]
[37]
Dawson, E. SNP maps: More markers needed? Mol. Med. Today, 1999, 5(10), 419-420.
[http://dx.doi.org/10.1016/S1357-4310(99)01564-6] [PMID: 10498908]
[38]
Vignal, A.; Milan, D.; SanCristobal, M.; Eggen, A. A review on SNP and other types of molecular markers and their use in animal genetics. Genet. Sel. Evol., 2002, 34(3), 275-305.
[http://dx.doi.org/10.1186/1297-9686-34-3-275] [PMID: 12081799]
[39]
Hong, E.P.; Park, J.W. Sample size and statistical power calculation in genetic association studies. Genomics Inform., 2012, 10(2), 117-122.
[http://dx.doi.org/10.5808/GI.2012.10.2.117] [PMID: 23105939]
[40]
Prasad, A.; Schnabel, R.D.; McKay, S.D.; Murdoch, B.; Stothard, P.; Kolbehdari, D.; Wang, Z.; Taylor, J.F.; Moore, S.S. Linkage disequilibrium and signatures of selection on chromosomes 19 and 29 in beef and dairy cattle. Anim. Genet., 2008, 39(6), 597-605.
[http://dx.doi.org/10.1111/j.1365-2052.2008.01772.x] [PMID: 18717667]
[41]
de Roos, A.P.W.; Hayes, B.J.; Spelman, R.J.; Goddard, M.E. Linkage disequilibrium and persistence of phase in Holstein-Friesian, Jersey and Angus cattle. Genetics, 2008, 179(3), 1503-1512.
[http://dx.doi.org/10.1534/genetics.107.084301] [PMID: 18622038]
[42]
Ishii, A.; Yamaji, K.; Uemoto, Y.; Sasago, N.; Kobayashi, E.; Kobayashi, N.; Matsuhashi, T.; Maruyama, S.; Matsumoto, H.; Sasazaki, S.; Mannen, H. Genome-wide association study for fatty acid composition in J apanese B lack cattle. Anim. Sci. J., 2013, 84(10), 675-682.
[http://dx.doi.org/10.1111/asj.12063] [PMID: 23607548]
[43]
Gunia, M.; Saintilan, R.; Venot, E.; Hozé, C.; Fouilloux, M.N.; Phocas, F. Genomic prediction in French charolais beef cattle using high-density single nucleotide polymorphism markers1. J. Anim. Sci., 2014, 92(8), 3258-3269.
[http://dx.doi.org/10.2527/jas.2013-7478] [PMID: 24948648]
[44]
Elsik, C.G.; Tellam, R.L.; Worley, K.C.; Gibbs, R.A.; Muzny, D.M.; Weinstock, G.M.; Adelson, D.L.; Eichler, E.E.; Elnitski, L.; Guigó, R.; Hamernik, D.L.; Kappes, S.M.; Lewin, H.A.; Lynn, D.J.; Nicholas, F.W.; Reymond, A.; Rijnkels, M.; Skow, L.C.; Zdobnov, E.M.; Schook, L.; Womack, J.; Alioto, T.; Antonarakis, S.E.; Astashyn, A.; Chapple, C.E.; Chen, H.C.; Chrast, J.; Câmara, F.; Ermolaeva, O.; Henrichsen, C.N.; Hlavina, W.; Kapustin, Y.; Kiryutin, B.; Kitts, P.; Kokocinski, F.; Landrum, M.; Maglott, D.; Pruitt, K.; Sapojnikov, V.; Searle, S.M.; Solovyev, V.; Souvorov, A.; Ucla, C.; Wyss, C.; Anzola, J.M.; Gerlach, D.; Elhaik, E.; Graur, D.; Reese, J.T.; Edgar, R.C.; McEwan, J.C.; Payne, G.M.; Raison, J.M.; Junier, T.; Kriventseva, E.V.; Eyras, E.; Plass, M.; Donthu, R.; Larkin, D.M.; Reecy, J.; Yang, M.Q.; Chen, L.; Cheng, Z.; Chitko-McKown, C.G.; Liu, G.E.; Matukumalli, L.K.; Song, J.; Zhu, B.; Bradley, D.G.; Brinkman, F.S.L.; Lau, L.P.L.; Whiteside, M.D.; Walker, A.; Wheeler, T.T.; Casey, T.; German, J.B.; Lemay, D.G.; Maqbool, N.J.; Molenaar, A.J.; Seo, S.; Stothard, P.; Baldwin, C.L.; Baxter, R.; Brinkmeyer-Langford, C.L.; Brown, W.C.; Childers, C.P.; Connelley, T.; Ellis, S.A.; Fritz, K.; Glass, E.J.; Herzig, C.T.A.; Iivanainen, A.; Lahmers, K.K.; Bennett, A.K.; Dickens, C.M.; Gilbert, J.G.R.; Hagen, D.E.; Salih, H.; Aerts, J.; Caetano, A.R.; Dalrymple, B.; Garcia, J.F.; Gill, C.A.; Hiendleder, S.G.; Memili, E.; Spurlock, D.; Williams, J.L.; Alexander, L.; Brownstein, M.J.; Guan, L.; Holt, R.A.; Jones, S.J.M.; Marra, M.A.; Moore, R.; Moore, S.S.; Roberts, A.; Taniguchi, M.; Waterman, R.C.; Chacko, J.; Chandrabose, M.M.; Cree, A.; Dao, M.D.; Dinh, H.H.; Gabisi, R.A.; Hines, S.; Hume, J.; Jhangiani, S.N.; Joshi, V.; Kovar, C.L.; Lewis, L.R.; Liu, Y.; Lopez, J.; Morgan, M.B.; Nguyen, N.B.; Okwuonu, G.O.; Ruiz, S.J.; Santibanez, J.; Wright, R.A.; Buhay, C.; Ding, Y.; Dugan-Rocha, S.; Herdandez, J.; Holder, M.; Sabo, A.; Egan, A.; Goodell, J.; Wilczek-Boney, K.; Fowler, G.R.; Hitchens, M.E.; Lozado, R.J.; Moen, C.; Steffen, D.; Warren, J.T.; Zhang, J.; Chiu, R.; Schein, J.E.; Durbin, K.J.; Havlak, P.; Jiang, H.; Liu, Y.; Qin, X.; Ren, Y.; Shen, Y.; Song, H.; Bell, S.N.; Davis, C.; Johnson, A.J.; Lee, S.; Nazareth, L.V.; Patel, B.M.; Pu, L.L.; Vattathil, S.; Williams, R.L., Jr; Curry, S.; Hamilton, C.; Sodergren, E.; Wheeler, D.A.; Barris, W.; Bennett, G.L.; Eggen, A.; Green, R.D.; Harhay, G.P.; Hobbs, M.; Jann, O.; Keele, J.W.; Kent, M.P.; Lien, S.; McKay, S.D.; McWilliam, S.; Ratnakumar, A.; Schnabel, R.D.; Smith, T.; Snelling, W.M.; Sonstegard, T.S.; Stone, R.T.; Sugimoto, Y.; Takasuga, A.; Taylor, J.F.; Van Tassell, C.P.; MacNeil, M.D.; Abatepaulo, A.R.R.; Abbey, C.A.; Ahola, V.; Almeida, I.G.; Amadio, A.F.; Anatriello, E.; Bahadue, S.M.; Biase, F.H.; Boldt, C.R.; Carroll, J.A.; Carvalho, W.A.; Cervelatti, E.P.; Chacko, E.; Chapin, J.E.; Cheng, Y.; Choi, J.; Colley, A.J.; de Campos, T.A.; De Donato, M.; Santos, I.K.F.M.; de Oliveira, C.J.F.; Deobald, H.; Devinoy, E.; Donohue, K.E.; Dovc, P.; Eberlein, A.; Fitzsimmons, C.J.; Franzin, A.M.; Garcia, G.R.; Genini, S.; Gladney, C.J.; Grant, J.R.; Greaser, M.L.; Green, J.A.; Hadsell, D.L.; Hakimov, H.A.; Halgren, R.; Harrow, J.L.; Hart, E.A.; Hastings, N.; Hernandez, M.; Hu, Z.L.; Ingham, A.; Iso-Touru, T.; Jamis, C.; Jensen, K.; Kapetis, D.; Kerr, T.; Khalil, S.S.; Khatib, H.; Kolbehdari, D.; Kumar, C.G.; Kumar, D.; Leach, R.; Lee, J.C.M.; Li, C.; Logan, K.M.; Malinverni, R.; Marques, E.; Martin, W.F.; Martins, N.F.; Maruyama, S.R.; Mazza, R.; McLean, K.L.; Medrano, J.F.; Moreno, B.T.; Moré, D.D.; Muntean, C.T.; Nandakumar, H.P.; Nogueira, M.F.G.; Olsaker, I.; Pant, S.D.; Panzitta, F.; Pastor, R.C.P.; Poli, M.A.; Poslusny, N.; Rachagani, S.; Ranganathan, S.; Razpet, A.; Riggs, P.K.; Rincon, G.; Osorio, R.N.; Zas, R.S.L.; Romero, N.E.; Rosenwald, A.; Sando, L.; Schmutz, S.M.; Shen, L.; Sherman, L.; Southey, B.R.; Lutzow, Y.S.; Sweedler, J.V.; Tammen, I.; Telugu, B.P.V.L.; Urbanski, J.M.; Utsunomiya, Y.T.; Verschoor, C.P.; Waardenberg, A.J.; Wang, Z.; Ward, R.; Weikard, R.; Welsh, T.H., Jr; White, S.N.; Wilming, L.G.; Wunderlich, K.R.; Yang, J.; Zhao, F.Q. The genome sequence of taurine cattle: A window to ruminant biology and evolution. Science, 2009, 324(5926), 522-528.
[http://dx.doi.org/10.1126/science.1169588] [PMID: 19390049]
[45]
Gibbs, R.A.; Taylor, J.F.; Van Tassell, C.P.; Barendse, W.; Eversole, K.A.; Gill, C.A.; Green, R.D.; Hamernik, D.L.; Kappes, S.M.; Lien, S.; Matukumalli, L.K.; McEwan, J.C.; Nazareth, L.V.; Schnabel, R.D.; Weinstock, G.M.; Wheeler, D.A.; Ajmone-Marsan, P.; Boettcher, P.J.; Caetano, A.R.; Garcia, J.F.; Hanotte, O.; Mariani, P.; Skow, L.C.; Sonstegard, T.S.; Williams, J.L.; Diallo, B.; Hailemariam, L.; Martinez, M.L.; Morris, C.A.; Silva, L.O.C.; Spelman, R.J.; Mulatu, W.; Zhao, K.; Abbey, C.A.; Agaba, M.; Araujo, F.R.; Bunch, R.J.; Burton, J.; Gorni, C.; Olivier, H.; Harrison, B.E.; Luff, B.; Machado, M.A.; Mwakaya, J.; Plastow, G.; Sim, W.; Smith, T.; Thomas, M.B.; Valentini, A.; Williams, P.; Womack, J.; Woolliams, J.A.; Liu, Y.; Qin, X.; Worley, K.C.; Gao, C.; Jiang, H.; Moore, S.S.; Ren, Y.; Song, X.Z.; Bustamante, C.D.; Hernandez, R.D.; Muzny, D.M.; Patil, S.; San Lucas, A.; Fu, Q.; Kent, M.P.; Vega, R.; Matukumalli, A.; McWilliam, S.; Sclep, G.; Bryc, K.; Choi, J.; Gao, H.; Grefenstette, J.J.; Murdoch, B.; Stella, A.; Villa-Angulo, R.; Wright, M.; Aerts, J.; Jann, O.; Negrini, R.; Goddard, M.E.; Hayes, B.J.; Bradley, D.G.; Barbosa da Silva, M.; Lau, L.P.L.; Liu, G.E.; Lynn, D.J.; Panzitta, F.; Dodds, K.G. Genome-wide survey of SNP variation uncovers the genetic structure of cattle breeds. Science, 2009, 324(5926), 528-532.
[http://dx.doi.org/10.1126/science.1167936] [PMID: 19390050]
[46]
Phillips, C.; Salas, A.; Sánchez, J.J.; Fondevila, M.; Tato, G.A.; Dios, A.J.; Calaza, M.; de Cal, M.C.; Ballard, D.; Lareu, M.V.; Carracedo, Á. Inferring ancestral origin using a single multiplex assay of ancestry-informative marker SNPs. Forensic Sci. Int. Genet., 2007, 1(3-4), 273-280.
[http://dx.doi.org/10.1016/j.fsigen.2007.06.008] [PMID: 19083773]
[47]
Halder, I.; Shriver, M.; Thomas, M.; Fernandez, J.R.; Frudakis, T. A panel of ancestry informative markers for estimating individual biogeographical ancestry and admixture from four continents: Utility and applications. Hum. Mutat., 2008, 29(5), 648-658.
[http://dx.doi.org/10.1002/humu.20695] [PMID: 18286470]
[48]
Morin, P.A.; Luikart, G.; Wayne, R.K. SNPs in ecology, evolution and conservation. Trends Ecol. Evol., 2004, 19(4), 208-216.
[http://dx.doi.org/10.1016/j.tree.2004.01.009]
[49]
Lewis, J.; Abas, Z.; Dadousis, C.; Lykidis, D.; Paschou, P.; Drineas, P. Tracing cattle breeds with principal components analysis ancestry informative SNPs. PLoS One, 2011, 6(4), e18007.
[http://dx.doi.org/10.1371/journal.pone.0018007] [PMID: 21490966]
[50]
Winkler, C.A.; Nelson, G.W.; Smith, M.W. Admixture mapping comes of age. Annu. Rev. Genomics Hum. Genet., 2010, 11(1), 65-89.
[http://dx.doi.org/10.1146/annurev-genom-082509-141523] [PMID: 20594047]
[51]
Banks, M.A.; Eichert, W.; Olsen, J.B. Which genetic loci have greater population assignment power? Bioinformatics, 2003, 19(11), 1436-1438.
[http://dx.doi.org/10.1093/bioinformatics/btg172] [PMID: 12874058]
[52]
Bromaghin, J.F. BELS : Backward elimination locus selection for studies of mixture composition or individual assignment. Mol. Ecol. Resour., 2008, 8(3), 568-571.
[http://dx.doi.org/10.1111/j.1471-8286.2007.02010.x] [PMID: 21585834]
[53]
Helyar, S.J.; Hemmer-Hansen, J.; Bekkevold, D.; Taylor, M.I.; Ogden, R.; Limborg, M.T.; Cariani, A.; Maes, G.E.; Diopere, E.; Carvalho, G.R.; Nielsen, E.E. Application of SNPs for population genetics of nonmodel organisms: new opportunities and challenges. Mol. Ecol. Resour., 2011, 11(S1), 123-136.
[http://dx.doi.org/10.1111/j.1755-0998.2010.02943.x] [PMID: 21429169]
[54]
Rosenberg, N.A.; Li, L.M.; Ward, R.; Pritchard, J.K. Informativeness of genetic markers for inference of ancestry. Am. J. Hum. Genet., 2003, 73(6), 1402-1422.
[http://dx.doi.org/10.1086/380416] [PMID: 14631557]
[55]
Shannon, C.E. A mathematical theory of communication. Bell Syst. Tech. J., 1948, 27(3), 379-423.
[http://dx.doi.org/10.1002/j.1538-7305.1948.tb01338.x]
[56]
Shriver, M.D.; Smith, M.W.; Jin, L.; Marcini, A.; Akey, J.M.; Deka, R.; Ferrell, R.E. Ethnic-affiliation estimation by use of population-specific DNA markers. Am. J. Hum. Genet., 1997, 60(4), 957-964.
[PMID: 9106543]
[57]
Wright, S. The genetical structure of populations. Ann. Eugen., 1951, 15(4), 323-354.
[PMID: 24540312]
[58]
Weir, B.S.; Cockerham, C.C. Estimating F-statistics for the analysis of population structure. Evolution, 1984, 38(6), 1358-1370.
[PMID: 28563791]
[59]
Kavakiotis, I.; Samaras, P.; Triantafyllidis, A.; Vlahavas, I. FIFS: A data mining method for informative marker selection in high dimensional population genomic data. Comput. Biol. Med., 2017, 90, 146-154.
[http://dx.doi.org/10.1016/j.compbiomed.2017.09.020] [PMID: 28992453]
[60]
Shriner, D. Overview of admixture mapping. Curr. Protoc. Hum. Genet., 2013, 2013, 23.
[PMID: 23315925]
[61]
Padhukasahasram, B. Inferring ancestry from population genomic data and its applications. Front. Genet., 2014, 5, 204.
[http://dx.doi.org/10.3389/fgene.2014.00204] [PMID: 25071832]
[62]
Falush, D.; Stephens, M.; Pritchard, J.K. Inference of population structure using multilocus genotype data: Linked loci and correlated allele frequencies. Genetics, 2003, 164(4), 1567-1587.
[http://dx.doi.org/10.1093/genetics/164.4.1567] [PMID: 12930761]
[63]
Pritchard, J.K.; Stephens, M.; Donnelly, P. Inference of population structure using multilocus genotype data. Genetics, 2000, 155(2), 945-959.
[http://dx.doi.org/10.1093/genetics/155.2.945] [PMID: 10835412]
[64]
Alexander, D.H.; Novembre, J.; Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res., 2009, 19(9), 1655-1664.
[http://dx.doi.org/10.1101/gr.094052.109] [PMID: 19648217]
[65]
Liu, Y.; Nyunoya, T.; Leng, S.; Belinsky, S.A.; Tesfaigzi, Y.; Bruse, S. Softwares and methods for estimating genetic ancestry in human populations. Hum. Genomics, 2013, 7(1), 1.
[http://dx.doi.org/10.1186/1479-7364-7-1] [PMID: 23289408]
[66]
Skotte, L.; Korneliussen, T.S.; Albrechtsen, A. Estimating individual admixture proportions from next generation sequencing data. Genetics, 2013, 195(3), 693-702.
[http://dx.doi.org/10.1534/genetics.113.154138] [PMID: 24026093]
[67]
Bertorelle, G.; Excoffier, L. Inferring admixture proportions from molecular data. Mol. Biol. Evol., 1998, 15(10), 1298-1311.
[http://dx.doi.org/10.1093/oxfordjournals.molbev.a025858] [PMID: 9787436]
[68]
Rosenberg, N.A.; Pritchard, J.K.; Weber, J.L.; Cann, H.M.; Kidd, K.K.; Zhivotovsky, L.A.; Feldman, M.W. Genetic structure of human populations. Science, 2002, 298(5602), 2381-2385.
[http://dx.doi.org/10.1126/science.1078311] [PMID: 12493913]
[69]
Edea, Z.; Dadi, H.; Kim, S.W.; Dessie, T.; Lee, T.; Kim, H.; Kim, J.J.; Kim, K.S. Genetic diversity, population structure and relationships in indigenous cattle populations of Ethiopia and Korean Hanwoo breeds using SNP markers. Front. Genet., 2013, 4, 35.
[http://dx.doi.org/10.3389/fgene.2013.00035] [PMID: 23518904]
[70]
Patterson, N.; Price, A.L.; Reich, D. Population structure and eigenanalysis. PLoS Genet., 2006, 2(12), e190.
[http://dx.doi.org/10.1371/journal.pgen.0020190] [PMID: 17194218]
[71]
Gao, X.; Starmer, J. Human population structure detection via multilocus genotype clustering. BMC Genet., 2007, 8(1), 34.
[http://dx.doi.org/10.1186/1471-2156-8-34] [PMID: 17592628]
[72]
Menozzi, P.; Piazza, A.; Cavalli-Sforza, L. Synthetic maps of human gene frequencies in Europeans. Science, 1978, 201(4358), 786-792.
[http://dx.doi.org/10.1126/science.356262] [PMID: 356262]
[73]
Bouaziz, M.; Ambroise, C.; Guedj, M. Accounting for population stratification in practice: A comparison of the main strategies dedicated to genome-wide association studies. PLoS One, 2011, 6(12), e28845.
[http://dx.doi.org/10.1371/journal.pone.0028845] [PMID: 22216125]
[74]
Siegel, S. Nonparametric Statistics. Am. Stat., 1957, 11(3), 13-19.
[http://dx.doi.org/10.1080/00031305.1957.10501091]
[75]
Beasley, T.M.; Erickson, S.; Allison, D.B. Rank-based inverse normal transformations are increasingly used, but are they merited? Behav. Genet., 2009, 39(5), 580-595.
[http://dx.doi.org/10.1007/s10519-009-9281-0] [PMID: 19526352]
[76]
Girma, M.; Banerjee, S.; Birhanu, T. Breeding practice and phenotypic characteristics of indigenous Woyito-Guji goat breeds reared in Nyangatom and Malle pastoral and agro-pastoral districts of SNNPR, Ethiopia. Int. J. Animal Sci., 2020, 4(8)
[77]
Potvin, C.; Roff, D.A. Distribution-free and robust statistical methods: viable alternatives to parametric statistics. Ecology, 1993, 74(6), 1617-1628.
[http://dx.doi.org/10.2307/1939920]
[78]
Gianola, D.; Fernando, R.L.; Stella, A. Genomic-assisted prediction of genetic value with semiparametric procedures. Genetics, 2006, 173(3), 1761-1776.
[http://dx.doi.org/10.1534/genetics.105.049510] [PMID: 16648593]
[79]
Nordborg, M.; Tavaré, S. Linkage disequilibrium: What history has to tell us. Trends Genet., 2002, 18(2), 83-90.
[http://dx.doi.org/10.1016/S0168-9525(02)02557-X] [PMID: 11818140]
[80]
Vila, M.; Romaní, V.J.R.; Björklund, M. The importance of time scale and multiple refugia: Incipient speciation and admixture of lineages in the butterfly Erebia triaria (Nymphalidae). Mol. Phylogenet. Evol., 2005, 36(2), 249-260.
[http://dx.doi.org/10.1016/j.ympev.2005.02.019] [PMID: 15955508]
[81]
Moorjani, P.; Patterson, N.; Hirschhorn, J.N.; Keinan, A.; Hao, L.; Atzmon, G.; Burns, E.; Ostrer, H.; Price, A.L.; Reich, D. The history of African gene flow into Southern Europeans, Levantines, and Jews. PLoS Genet., 2011, 7(4), e1001373.
[http://dx.doi.org/10.1371/journal.pgen.1001373] [PMID: 21533020]
[82]
Pugach, I.; Matveyev, R.; Wollstein, A.; Kayser, M.; Stoneking, M. Dating the age of admixture via wavelet transform analysis of genome-wide data. Genome Biol., 2011, 12(2), R19.
[http://dx.doi.org/10.1186/gb-2011-12-2-r19] [PMID: 21352535]
[83]
Sankararaman, S.; Patterson, N.; Li, H.; Pääbo, S.; Reich, D. The date of interbreeding between Neandertals and modern humans. PLoS Genet, 2012, 8(10), e1002947.
[http://dx.doi.org/10.1371/journal.pgen.1002947]
[84]
Loh, P.R.; Lipson, M.; Patterson, N.; Moorjani, P.; Pickrell, J.K.; Reich, D.; Berger, B. Inferring admixture histories of human populations using linkage disequilibrium. Genetics, 2013, 193(4), 1233-1254.
[http://dx.doi.org/10.1534/genetics.112.147330] [PMID: 23410830]
[85]
McTavish, E.J.; Hillis, D.M. A genomic approach for distinguishing between recent and ancient admixture as applied to cattle. J. Hered., 2014, 105(4), 445-456.
[http://dx.doi.org/10.1093/jhered/esu001] [PMID: 24510946]
[86]
Hellenthal, G.; Busby, G.B.; Band, G.; Wilson, J.F.; Capelli, C.; Falush, D.; Myers, S. A genetic atlas of human admixture history. science, 2014, 343(6172), 747-751.
[87]
Avadhanam, S.; Williams, A.L. Simultaneous inference of parental admixture proportions and admixture times from unphased local ancestry calls. Am. J. Hum. Genet., 2022, 109(8), 1405-1420.
[http://dx.doi.org/10.1016/j.ajhg.2022.06.016] [PMID: 35908549]
[88]
Chakraborty, R.; Weiss, K.M. Admixture as a tool for finding linked genes and detecting that difference from allelic association between loci. Proc. Natl. Acad. Sci., 1988, 85(23), 9119-9123.
[http://dx.doi.org/10.1073/pnas.85.23.9119] [PMID: 3194414]
[89]
McKeigue, P.M. Mapping genes that underlie ethnic differences in disease risk: methods for detecting linkage in admixed populations, by conditioning on parental admixture. Am. J. Hum. Genet., 1998, 63(1), 241-251.
[http://dx.doi.org/10.1086/301908] [PMID: 9634509]
[90]
Hoggart, C.J.; Shriver, M.D.; Kittles, R.A.; Clayton, D.G.; McKeigue, P.M. Design and analysis of admixture mapping studies. Am. J. Hum. Genet., 2004, 74(5), 965-978.
[http://dx.doi.org/10.1086/420855] [PMID: 15088268]
[91]
Zhang, C.; Chen, K.; Seldin, M.F.; Li, H. A hidden Markov modeling approach for admixture mapping based on case-control data. Genet. Epidemiol., 2004, 27(3), 225-239.
[http://dx.doi.org/10.1002/gepi.20021] [PMID: 15389926]
[92]
Zhu, X.; Zhang, S.; Tang, H.; Cooper, R. A classical likelihood based approach for admixture mapping using EM algorithm. Hum. Genet., 2006, 120(3), 431-445.
[http://dx.doi.org/10.1007/s00439-006-0224-z] [PMID: 16896924]
[93]
Patterson, N.; Hattangadi, N.; Lane, B.; Lohmueller, K.E.; Hafler, D.A.; Oksenberg, J.R.; Hauser, S.L.; Smith, M.W.; O’Brien, S.J.; Altshuler, D.; Daly, M.J.; Reich, D. Methods for high-density admixture mapping of disease genes. Am. J. Hum. Genet., 2004, 74(5), 979-1000.
[http://dx.doi.org/10.1086/420871] [PMID: 15088269]
[94]
Hoggart, C.J.; Parra, E.J.; Shriver, M.D.; Bonilla, C.; Kittles, R.A.; Clayton, D.G.; McKeigue, P.M. Control of confounding of genetic associations in stratified populations. Am. J. Hum. Genet., 2003, 72(6), 1492-1504.
[http://dx.doi.org/10.1086/375613] [PMID: 12817591]
[95]
Tang, H.; Choudhry, S.; Mei, R.; Morgan, M.; Rodriguez-Cintron, W.; Burchard, E.G.; Risch, N.J. Recent genetic selection in the ancestral admixture of Puerto Ricans. Am. J. Hum. Genet., 2007, 81(3), 626-633. a
[http://dx.doi.org/10.1086/520769] [PMID: 17701908]
[96]
Jin, W.; Xu, S.; Wang, H.; Yu, Y.; Shen, Y.; Wu, B.; Jin, L. Genome-wide detection of natural selection in African Americans pre- and post-admixture. Genome Res., 2012, 22(3), 519-527.
[http://dx.doi.org/10.1101/gr.124784.111] [PMID: 22128132]
[97]
Jones, O.R.; Wang, J. A comparison of four methods for detecting weak genetic structure from marker data. Ecol. Evol., 2012, 2(5), 1048-1055.
[http://dx.doi.org/10.1002/ece3.237] [PMID: 22837848]
[98]
Bertorelle, G.; Raffini, F.; Bosse, M.; Bortoluzzi, C.; Iannucci, A.; Trucchi, E.; Morales, H.E.; van Oosterhout, C. Genetic load: genomic estimates and applications in non-model animals. Nat. Rev. Genet., 2022, 23(8), 492-503.
[http://dx.doi.org/10.1038/s41576-022-00448-x] [PMID: 35136196]
[99]
Oleksyk, T.K.; Smith, M.W.; O’Brien, S.J. Genome-wide scans for footprints of natural selection. Philos. Trans. R. Soc. Lond. B Biol. Sci., 2010, 365(1537), 185-205.
[http://dx.doi.org/10.1098/rstb.2009.0219] [PMID: 20008396]
[100]
Payseur, B.A.; Rieseberg, L.H. A genomic perspective on hybridization and speciation. Mol. Ecol., 2016, 25(11), 2337-2360.
[http://dx.doi.org/10.1111/mec.13557] [PMID: 26836441]
[101]
Yougbaré, B.; Ouédraogo, D.; Tapsoba, A.S.R.; Soudré, A.; Zoma, B.L.; terWengel, O.P.; Moumouni, S.; Koné, O.S.; Wurzinger, M.; Tamboura, H.H.; Traoré, A.; Mwai, O.A.; Sölkner, J.; Khayatzadeh, N.; Mészáros, G.; Burger, P.A. Local ancestry to identify selection in response to trypanosome infection in Baoulé x zebu crossbred cattle in Burkina Faso. Front. Genet., 2021, 12, 670390.
[http://dx.doi.org/10.3389/fgene.2021.670390] [PMID: 34646296]
[102]
Gautier, M.; Naves, M. Footprints of selection in the ancestral admixture of a New World Creole cattle breed. Mol. Ecol., 2011, 20(15), 3128-3143.
[http://dx.doi.org/10.1111/j.1365-294X.2011.05163.x] [PMID: 21689193]
[103]
Detig, C.R.; Nielsen, R. A hidden Markov model approach for simultaneously estimating local ancestry and admixture time using next generation sequence data in samples of arbitrary ploidy. PLoS Genet., 2017, 13(1), e1006529.
[http://dx.doi.org/10.1371/journal.pgen.1006529] [PMID: 28045893]
[104]
Sankararaman, S.; Sridhar, S.; Kimmel, G.; Halperin, E. Estimating local ancestry in admixed populations. Am. J. Hum. Genet., 2008, 82(2), 290-303.
[http://dx.doi.org/10.1016/j.ajhg.2007.09.022] [PMID: 18252211]
[105]
Baran, Y.; Pasaniuc, B.; Sankararaman, S.; Torgerson, D.G.; Gignoux, C.; Eng, C.; Cintron, R.W.; Chapela, R.; Ford, J.G.; Avila, P.C.; Santana, R.J.; Burchard, E.G.; Halperin, E. Fast and accurate inference of local ancestry in Latino populations. Bioinformatics, 2012, 28(10), 1359-1367.
[http://dx.doi.org/10.1093/bioinformatics/bts144] [PMID: 22495753]
[106]
Paşaniuc, B.; Sankararaman, S.; Kimmel, G.; Halperin, E. Inference of locus-specific ancestry in closely related populations. Bioinformatics, 2009, 25(12), i213-i221.
[http://dx.doi.org/10.1093/bioinformatics/btp197] [PMID: 19477991]
[107]
Li, N.; Stephens, M. Modeling linkage disequilibrium and identifying recombination hotspots using single-nucleotide polymorphism data. Genetics, 2003, 165(4), 2213-2233.
[http://dx.doi.org/10.1093/genetics/165.4.2213] [PMID: 14704198]
[108]
Yang, J.J.; Cheng, C.; Devidas, M.; Cao, X.; Fan, Y.; Campana, D.; Yang, W.; Neale, G.; Cox, N.J.; Scheet, P.; Borowitz, M.J.; Winick, N.J.; Martin, P.L.; Willman, C.L.; Bowman, W.P.; Camitta, B.M.; Carroll, A.; Reaman, G.H.; Carroll, W.L.; Loh, M.; Hunger, S.P.; Pui, C.H.; Evans, W.E.; Relling, M.V. Ancestry and pharmacogenomics of relapse in acute lymphoblastic leukemia. Nat. Genet., 2011, 43(3), 237-241.
[http://dx.doi.org/10.1038/ng.763] [PMID: 21297632]
[109]
Brisbin, A.; Bryc, K.; Byrnes, J.; Zakharia, F.; Omberg, L.; Degenhardt, J.; Reynolds, A.; Ostrer, H.; Mezey, J.G.; Bustamante, C.D. PCAdmix: Principal components-based assignment of ancestry along each chromosome in individuals with admixed ancestry from two or more populations. Hum. Biol., 2012, 84(4), 343-364.
[http://dx.doi.org/10.1353/hub.2012.a493568] [PMID: 23249312]
[110]
Omberg, L.; Salit, J.; Hackett, N.; Fuller, J.; Matthew, R.; Chouchane, L.; Rodriguez-Flores, J.L.; Bustamante, C.; Crystal, R.G.; Mezey, J.G. Inferring genome-wide patterns of admixture in Qataris using fifty-five ancestral populations. BMC Genet., 2012, 13(1), 49.
[http://dx.doi.org/10.1186/1471-2156-13-49] [PMID: 22734698]
[111]
Mendizabal, I.; Lao, O.; Marigorta, U.M.; Wollstein, A.; Gusmão, L.; Ferak, V.; Ioana, M.; Jordanova, A.; Kaneva, R.; Kouvatsi, A.; Kučinskas, V.; Makukh, H.; Metspalu, A.; Netea, M.G.; de Pablo, R.; Pamjav, H.; Radojkovic, D.; Rolleston, S.J.H.; Sertic, J.; Macek, M., Jr; Comas, D.; Kayser, M. Reconstructing the population history of European Romani from genome-wide data. Curr. Biol., 2012, 22(24), 2342-2349.
[http://dx.doi.org/10.1016/j.cub.2012.10.039] [PMID: 23219723]
[112]
Lawson, D.J.; Hellenthal, G.; Myers, S.; Falush, D. Inference of population structure using dense haplotype data. PLoS Genet., 2012, 8(1), e1002453.
[http://dx.doi.org/10.1371/journal.pgen.1002453] [PMID: 22291602]
[113]
Spangenberg, L.; Fariello, M.I.; Arce, D.; Illanes, G.; Greif, G.; Shin, J.Y.; Yoo, S.K.; Seo, J.S.; Robello, C.; Kim, C.; Novembre, J.; Sans, M.; Naya, H. Indigenous ancestry and admixture in the Uruguayan population. Front. Genet., 2021, 12, 733195.
[http://dx.doi.org/10.3389/fgene.2021.733195] [PMID: 34630523]
[114]
Maples, B.K.; Gravel, S.; Kenny, E.E.; Bustamante, C.D. RFMix: A discriminative modeling approach for rapid and robust local-ancestry inference. Am. J. Hum. Genet., 2013, 93(2), 278-288.
[http://dx.doi.org/10.1016/j.ajhg.2013.06.020] [PMID: 23910464]
[115]
Yang, J. J.; Li, J.; Buu, A.; Williams, L. K.; Yang, M. J. J. Efficient inference of local ancestry. Bioinformatics, 2013, 29, 2750-2756.
[116]
Moreno-Estrada, A.; Gravel, S.; Zakharia, F.; McCauley, J.L.; Byrnes, J.K.; Gignoux, C.R.; Tello, O.P.A.; Martínez, R.J.; Hedges, D.J.; Morris, R.W.; Eng, C.; Sandoval, K.; Acevedo, A.S.; Norman, P.J.; Layrisse, Z.; Parham, P.; Martínez-Cruzado, J.C.; Burchard, E.G.; Cuccaro, M.L.; Martin, E.R.; Bustamante, C.D. Reconstructing the population genetic history of the Caribbean. PLoS Genet., 2013, 9(11), e1003925.
[http://dx.doi.org/10.1371/journal.pgen.1003925] [PMID: 24244192]
[117]
Kumar, L.; Farias, K.; Prakash, S.; Mishra, A.; Mustak, M.S.; Rai, N.; Thangaraj, K. Dissecting the genetic history of the roman catholic populations of West Coast India. Hum. Genet., 2021, 140(10), 1487-1498.
[http://dx.doi.org/10.1007/s00439-021-02346-4] [PMID: 34424406]
[118]
Dias-Alves, T.; Mairal, J.; Blum, M.G.B. Loter: A software package to infer local ancestry for a wide range of species. Mol. Biol. Evol., 2018, 35(9), 2318-2326.
[http://dx.doi.org/10.1093/molbev/msy126] [PMID: 29931083]
[119]
Daya, M.; van der Merwe, L.; Gignoux, C.R.; van Helden, P.D.; Möller, M.; Hoal, E.G. Using multi-way admixture mapping to elucidate TB susceptibility in the South African Coloured population. BMC Genomics, 2014, 15(1), 1021.
[http://dx.doi.org/10.1186/1471-2164-15-1021] [PMID: 25422094]
[120]
Wu, M.Y.; Forcina, G.; Low, G.W.; Sadanandan, K.R.; Gwee, C.Y.; van Grouw, H.; Wu, S.; Edwards, S.V.; Baldwin, M.W.; Rheindt, F.E. Historic samples reveal loss of wild genotype through domestic chicken introgression during the Anthropocene. PLoS Genet., 2023, 19(1), e1010551.
[http://dx.doi.org/10.1371/journal.pgen.1010551] [PMID: 36656838]
[121]
Lucas-Sánchez, M.; Fadhlaoui-Zid, K.; Comas, D. The genomic analysis of current-day North African populations reveals the existence of trans-Saharan migrations with different origins and dates. Hum. Genet., 2023, 142(2), 305-320.
[http://dx.doi.org/10.1007/s00439-022-02503-3] [PMID: 36441222]
[122]
Wedger, M.J.; Roma-Burgos, N.; Olsen, K.M. Genomic revolution of US weedy rice in response to 21st century agricultural technologies. Commun. Biol., 2022, 5(1), 885.
[http://dx.doi.org/10.1038/s42003-022-03803-0] [PMID: 36076028]
[123]
Browning, S.R.; Waples, R.K.; Browning, B.L. Fast, accurate local ancestry inference with FLARE. Am. J. Hum. Genet., 2023, 110(2), 326-335.
[http://dx.doi.org/10.1016/j.ajhg.2022.12.010] [PMID: 36610402]
[124]
Lawrence, E.S.; Gu, W.; Bohlender, R.J.; Ramirez, A.C.; Cole, A.M.; Yu, J.J.; Hu, H.; Heinrich, E.C.; O’Brien, K.A.; Vasquez, C.A.; Cowan, Q.T.; Bruck, P.T.; Mercader, K.; Alotaibi, M.; Long, T.; Hall, J.E.; Moya, E.A.; Bauk, M.A.; Reeves, J.J.; Kong, M.C.; Salem, R.M.; Vizcardo-Galindo, G.; Macarlupu, J.L.; Mujíca, F.R.; Bermudez, D.; Corante, N.; Gaio, E.; Fox, K.P.; Salomaa, V.; Havulinna, A.S.; Murray, A.J.; Malhotra, A.; Powel, F.L.; Jain, M.; Komor, A.C.; Cavalleri, G.L.; Huff, C.D.; Villafuerte, F.C.; Simonson, T.S. Functional EPAS1/HIF2A missense variant is associated with hematocrit in Andean highlanders. Sci. Adv., 2024, 10(6), eadj5661.
[http://dx.doi.org/10.1126/sciadv.adj5661] [PMID: 38335297]
[125]
Sabat, O.B.; Montserrat, M.D.; Nieto, G.X.; Ioannidis, A.G. SALAI-Net: Species-agnostic local ancestry inference network. Bioinformatics, 2022, 38(S2), ii27-ii33.
[http://dx.doi.org/10.1093/bioinformatics/btac464] [PMID: 36124792]
[126]
Garrigan, D.; Huff, J.; Foran, C.R. BCSYS: An accurate and scalable local ancestry classifier. 2023. Available from: https://www.wisdompanel.com/downloads/wp-breed-detection.pdf
[127]
Freyer, J.; Labadie, J.D.; Huff, J.T.; Denyer, M.; Forman, O.P.; Foran, C.R.; Donner, J. Association of FGF4L1 retrogene insertion with prolapsed gland of the nictitans (Cherry Eye) in dogs. Genes, 2024, 15(2), 198.
[http://dx.doi.org/10.3390/genes15020198] [PMID: 38397188]
[128]
Hershkovitz, G.E.; Xia, R.; Yang, Y.; Spitzer, B.; Tarraf, W.; Vásquez, P.M.; Lipton, R.B.; Daviglus, M.; Argos, M.; Cai, J.; Kaplan, R.; Fornage, M.; DeCarli, C.; Gonzalez, H.M.; Sofer, T. Interaction analysis of ancestry-enriched variants with APOE-ɛ4 on MCI in the Study of Latinos-Investigation of Neurocognitive Aging. Sci. Rep., 2023, 13(1), 5114.
[http://dx.doi.org/10.1038/s41598-023-32028-2] [PMID: 36991100]
[129]
Quillen, E.E.; Bauchet, M.; Bigham, A.W.; Burbano, D.M.E.; Faust, F.X.; Klimentidis, Y.C.; Mao, X.; Stoneking, M.; Shriver, M.D. OPRM1 and EGFR contribute to skin pigmentation differences between Indigenous Americans and Europeans. Hum. Genet., 2012, 131(7), 1073-1080.
[http://dx.doi.org/10.1007/s00439-011-1135-1] [PMID: 22198722]
[130]
Cerqueira, C.C.S.; Paixão-Côrtes, V.R.; Zambra, F.M.B.; Salzano, F.M.; Hünemeier, T.; Bortolini, M.C. Predicting homo pigmentation phenotype through genomic data: From neanderthal to James Watson. Am. J. Hum. Biol., 2012, 24(5), 705-709.
[http://dx.doi.org/10.1002/ajhb.22263] [PMID: 22411106]
[131]
Gerstenblith, M.R.; Shi, J.; Landi, M.T. Genome-wide association studies of pigmentation and skin cancer: A review and meta-analysis. Pigment Cell Melanoma Res., 2010, 23(5), 587-606.
[http://dx.doi.org/10.1111/j.1755-148X.2010.00730.x] [PMID: 20546537]
[132]
Sturm, R.A.; Duffy, D.L. Human pigmentation genes under environmental selection. Genome Biol., 2012, 13(9), 248.
[http://dx.doi.org/10.1186/gb-2012-13-9-248] [PMID: 23110848]
[133]
Sukhija, N.; Kanaka, K.K.; Goli, R.C.; Kapoor, P.; Sivalingam, J.; Verma, A.; Sharma, R.; Tripathi, S.B.; Malik, A.A. The flight of chicken genomics and allied omics-a mini review. Ecol. Genet. Genom., 2023, 29, 100201. a
[http://dx.doi.org/10.1016/j.egg.2023.100201]
[134]
Kopp, J.B.; Smith, M.W.; Nelson, G.W.; Johnson, R.C.; Freedman, B.I.; Bowden, D.W.; Oleksyk, T.; McKenzie, L.M.; Kajiyama, H.; Ahuja, T.S.; Berns, J.S.; Briggs, W.; Cho, M.E.; Dart, R.A.; Kimmel, P.L.; Korbet, S.M.; Michel, D.M.; Mokrzycki, M.H.; Schelling, J.R.; Simon, E.; Trachtman, H.; Vlahov, D.; Winkler, C.A. MYH9 is a major-effect risk gene for focal segmental glomerulosclerosis. Nat. Genet., 2008, 40(10), 1175-1184.
[http://dx.doi.org/10.1038/ng.226] [PMID: 18794856]
[135]
Norton, H.L.; Kittles, R.A.; Parra, E.; McKeigue, P.; Mao, X.; Cheng, K.; Canfield, V.A.; Bradley, D.G.; McEvoy, B.; Shriver, M.D. Genetic evidence for the convergent evolution of light skin in Europeans and East Asians. Mol. Biol. Evol., 2006, 24(3), 710-722.
[http://dx.doi.org/10.1093/molbev/msl203] [PMID: 17182896]
[136]
Beleza, S.; Johnson, N.A.; Candille, S.I.; Absher, D.M.; Coram, M.A.; Lopes, J.; Campos, J.; Araújo, I.I.; Anderson, T.M.; Vilhjálmsson, B.J.; Nordborg, M.; Correia e Silva, A.; Shriver, M.D.; Rocha, J.; Barsh, G.S.; Tang, H.; Tang, H. Genetic architecture of skin and eye color in an African-European admixed population. PLoS Genet., 2013, 9(3), e1003372.
[http://dx.doi.org/10.1371/journal.pgen.1003372] [PMID: 23555287]
[137]
Pickrell, J.K.; Reich, D. Toward a new history and geography of human genes informed by ancient DNA. Trends Genet., 2014, 30(9), 377-389.
[http://dx.doi.org/10.1016/j.tig.2014.07.007] [PMID: 25168683]
[138]
Harding, R.M.; Tomlinson, J.B.; Ray, A.J.; Wakamatsu, K.; Rees, J.L.; McKenzie, C.A. Phenotypic expression of melanocortin-1 receptor mutations in Black Jamaicans. J. Invest. Dermatol., 2003, 121(1), 207-208.
[http://dx.doi.org/10.1046/j.1523-1747.2003.12314.x] [PMID: 12839583]
[139]
Chaitanya, L.; Ralf, A.; Oven, M.; Kupiec, T.; Chang, J.; Lagacé, R.; Kayser, M. Simultaneous whole mitochondrial genome sequencing with short overlapping amplicons suitable for degraded DNA using the ion torrent personal genome machine. Hum. Mutat., 2015, 36(12), 1236-1247.
[http://dx.doi.org/10.1002/humu.22905] [PMID: 26387877]
[140]
Ralf, A.; van Oven, M.; González, M.D.; de Knijff, P.; van der Beek, K.; Wootton, S.; Lagacé, R.; Kayser, M. Forensic Y-SNP analysis beyond SNaPshot: High-resolution Y-chromosomal haplogrouping from low quality and quantity DNA using Ion AmpliSeq and targeted massively parallel sequencing. Forensic Sci. Int. Genet., 2019, 41, 93-106.
[http://dx.doi.org/10.1016/j.fsigen.2019.04.001] [PMID: 31063905]
[141]
Phillips, C. Forensic genetic analysis of bio-geographical ancestry. Forensic Sci. Int. Genet., 2015, 18, 49-65.
[http://dx.doi.org/10.1016/j.fsigen.2015.05.012] [PMID: 26013312]
[142]
Phillips, C.; Devesse, L.; Ballard, D.; van Weert, L.; de la Puente, M.; Melis, S.; Iglesias, A.V.; Aradas, F.A.; Oldroyd, N.; Holt, C.; Court, S.D.; Carracedo, Á.; Lareu, M.V. Global patterns of STR sequence variation: Sequencing the CEPH human genome diversity panel for 58 forensic STRs using the Illumina ForenSeq DNA Signature Prep Kit. Electrophoresis, 2018, 39(21), 2708-2724.
[http://dx.doi.org/10.1002/elps.201800117] [PMID: 30101987]
[143]
Pitt, D.; Bruford, M.W.; Barbato, M.; terWengel, O.P.; Martínez, R.; Sevane, N. Demography and rapid local adaptation shape Creole cattle genome diversity in the tropics. Evol. Appl., 2019, 12(1), 105-122.
[http://dx.doi.org/10.1111/eva.12641] [PMID: 30622639]
[144]
Noyes, H.; Brass, A.; Obara, I.; Anderson, S.; Archibald, A.L.; Bradley, D.G.; Fisher, P.; Freeman, A.; Gibson, J.; Gicheru, M.; Hall, L.; Hanotte, O.; Hulme, H.; McKeever, D.; Murray, C.; Oh, S.J.; Tate, C.; Smith, K.; Tapio, M.; Wambugu, J.; Williams, D.J.; Agaba, M.; Kemp, S.J. Genetic and expression analysis of cattle identifies candidate genes in pathways responding to Trypanosoma congolense infection. Proc. Natl. Acad. Sci., 2011, 108(22), 9304-9309.
[http://dx.doi.org/10.1073/pnas.1013486108] [PMID: 21593421]
[145]
Ward, J.A.; McHugo, G.P.; Dover, M.J.; Hall, T.J.; Ng’ang’a, S.I.; Sonstegard, T.S.; Bradley, D.G.; Frantz, L.A.F.; Townshend, S.M.; MacHugh, D.E. Genome-wide local ancestry and evidence for mitonuclear coadaptation in African hybrid cattle populations. iScience, 2022, 25(7), 104672.
[http://dx.doi.org/10.1016/j.isci.2022.104672] [PMID: 35832892]
[146]
Griffiths, R.C.; Marjoram, P. An ancestral recombination graph. In: Progress in Population Genetics and Human Evolution; Donnelly, P.; Tavare, S., Eds.; Springer-Verlag: Berlin, Germany, 1997; pp. 257-270.
[http://dx.doi.org/10.1007/978-1-4757-2609-1_16]
[147]
Rasmussen, M.D.; Hubisz, M.J.; Gronau, I.; Siepel, A. Genome-wide inference of ancestral recombination graphs. PLoS Genet., 2014, 10(5), e1004342.
[http://dx.doi.org/10.1371/journal.pgen.1004342] [PMID: 24831947]
[148]
Martin, D.P.; Lemey, P.; Posada, D. Analysing recombination in nucleotide sequences. Mol. Ecol. Resour., 2011, 11(6), 943-955.
[http://dx.doi.org/10.1111/j.1755-0998.2011.03026.x] [PMID: 21592314]
[149]
Hubisz, M.; Siepel, A. Inference of ancestral recombination graphs using ARGweaver. Methods Mol Biol, 2020, 2090, 231-266.
[150]
Marjoram, P.; Wall, J.D. Fast “coalescent” simulation. BMC Genet., 2006, 7(1), 16.
[http://dx.doi.org/10.1186/1471-2156-7-16] [PMID: 16539698]
[151]
Schaefer, N.K.; Shapiro, B.; Green, R.E. An ancestral recombination graph of human, Neanderthal, and Denisovan genomes. Sci. Adv., 2021, 7(29), eabc0776.
[http://dx.doi.org/10.1126/sciadv.abc0776] [PMID: 34272242]
[152]
Buendia, P.; Narasimhan, G. Serial NetEvolve: A flexible utility for generating serially-sampled sequences along a tree or recombinant network. Bioinformatics, 2006, 22(18), 2313-2314.
[http://dx.doi.org/10.1093/bioinformatics/btl387] [PMID: 16844708]
[153]
McGill, J.R.; Walkup, E.A.; Kuhner, M.K. GraphML specializations to codify ancestral recombinant graphs. Front. Genet., 2013, 4, 146.
[http://dx.doi.org/10.3389/fgene.2013.00146] [PMID: 23967010]
[154]
Javed, A.; Pybus, M.; Melé, M.; Utro, F.; Bertranpetit, J.; Calafell, F.; Parida, L. IRiS: Construction of ARG networks at genomic scales. Bioinformatics, 2011, 27(17), 2448-2450.
[http://dx.doi.org/10.1093/bioinformatics/btr423] [PMID: 21765095]
[155]
O’Fallon, B.D. ACG: Rapid inference of population history from recombining nucleotide sequences. BMC Bioinformatics, 2013, 14(1), 40.
[http://dx.doi.org/10.1186/1471-2105-14-40] [PMID: 23379678]
[156]
Rasmussen, M. D.; Siepel, A. Genome-wide inference of ancestral recombination graphs. arXiv1306.5110v2, 2013.
[157]
Mirzaei, S.; Wu, Y. RENT+: An improved method for inferring local genealogical trees from haplotypes with recombination. Bioinformatics, 2017, 33(7), 1021-1030.
[http://dx.doi.org/10.1093/bioinformatics/btw735] [PMID: 28065901]
[158]
Duchemin, W.; Anselmetti, Y.; Patterson, M.; Ponty, Y.; Bérard, S.; Chauve, C.; Scornavacca, C.; Daubin, V.; Tannier, E. DeCoSTAR: Reconstructing the ancestral organization of genes or genomes using reconciled phylogenies. Genome Biol. Evol., 2017, 9(5), 1312-1319.
[http://dx.doi.org/10.1093/gbe/evx069] [PMID: 28402423]
[159]
Speidel, L.; Forest, M.; Shi, S.; Myers, S.R. A method for genome-wide genealogy estimation for thousands of samples. Nat. Genet., 2019, 51(9), 1321-1329.
[http://dx.doi.org/10.1038/s41588-019-0484-x] [PMID: 31477933]
[160]
Zhang, B.C.; Biddanda, A.; Palamara, P.F. 2021.Biobank-scale inference of ancestral recombination graphs enables genealogy-based mixed model association of complex traits. bioRxiv,
[http://dx.doi.org/10.1101/2021.11.03.466843]
[161]
Ignatieva, A.; Lyngsø, R.B.; Jenkins, P.A.; Hein, J. KwARG: Parsimonious reconstruction of ancestral recombination graphs with recurrent mutation. Bioinformatics, 2021, 37(19), 3277-3284.
[http://dx.doi.org/10.1093/bioinformatics/btab351] [PMID: 33970217]
[162]
Cámara, P.G.; Levine, A.J.; Rabadán, R. Inference of ancestral recombination graphs through topological data analysis. PLOS Comput. Biol., 2016, 12(8), e1005071.
[http://dx.doi.org/10.1371/journal.pcbi.1005071] [PMID: 27532298]
[163]
Shull, G.H. Duplicate genes for capsule-form inBursa bursa-pastoris. Mol. Genet. Genomics, 1914, 12(1), 97-149.
[http://dx.doi.org/10.1007/BF01837282]
[164]
Davenport, C.B. Degeneration, albinism and inbreeding. Science, 1908, 28(718), 454-455.
[http://dx.doi.org/10.1126/science.28.718.454.c] [PMID: 17771943]
[165]
East, E.M. Report of The Connecticut Agricultural Experiment Station; Robinson Street Books, IOBA: Binghamton, NY, U.S.A., 1908.
[166]
Shull, G.H The composition of a field of maize. J. Heredity, 1908, 4(1), 296-301.
[http://dx.doi.org/10.1093/jhered/os-4.1.296]

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