Assessment of genetic differentiation of populations by analysis of molecular variance (analytical review)
https://doi.org/10.30766/2072-9081.2021.22.2.167-187
Abstract
Different approaches to using the analysis of molecular variance (AMOVA) to assess the genetic differentiation of populations have been compared in the research. Data on 11 microsatellite loci of 84 bulls of seven breeds were used. The results were compared for three options of the AMOVA module of the GenAlEx 6.502 program: the allele distance matrix (calculated FST(W&C) (=θ) statistics – variant AMOVA1); the genotype distance matrix (ΦPT – AMOVA2); and the allele size difference matrix (RST – AMOVA3). Similar summary estimates of the genetic differentiation of breeds were obtained: FST(W&C) = 0.108, ΦPT = 0.115, RST = 0.110 (all with pperm ≤ 0.001). Between the estimates of FST(W&C) and ΦPT for each locus, the correlation coefficient was 0.99 (pvalue <0.0001); no statistically significant correlations with RST were found. A high correlation of FST(W&C) and ΦPT with the estimates of differentiation according to Nei’s (0.96) was found. Programs other than GenAlEx (Arlequin v.3.5, GenePop v.4.7.3, RST22) gave similar AMOVA estimates. The negative linear dependence of FST(W&C) and ΦPT on the level of the average heterozygosity of the breed samples was established (R2 = 0.6, rS = -0.75 for pvalue < 0.02) and the absence of such dependence for RST (R2 = 0.04, rS = -0.23 for pvalue = 0.47). The standardization of the FST(W&C) and ΦPT estimates according to Hedrick’s eliminated this dependence and raised the initial estimates to 0.35 and 0.37, respectively. The latter were comparable to the estimates obtained by the Nei-Hedrick’s (0.364-0.375), Jost’s (0.292), and Morisit-Horn’s (0.308) methods. The Mantel correlations between the matrices of paired genetic distances (GD) calculated by different measures were >0.9 in most cases. The projections of the GD matrices in the principal coordinate analysis (PCoA) on the 2D plane were generally similar. The PCoA identified a cluster of Holstein «ecotypes», a cluster of «Red» breeds, and a branch of the Jersey breed. In the two-factor AMOVA of data on clusters (as two «regions»), the interregional GD was 0.357; the differentiation of breeds within the «regions» did not exceed 0.027. Modeling the association of breeds with close to zero GD resulted in an increase in the number of alleles per locus in the «new» breeds by 29 %, and an increase in the combined estimate of genetic differentiation by 29-46 %. The results obtained can be used in the development of measures for the conservation of endangered breeds.
About the Author
V. M. KuznetsovRussian Federation
Vasiliy M. Kuznetsov, DSc in Agricultural Science, professor, Head of the Laboratory of Population Genetics in Animal Husbandry
Lenin str., 166a, Kirov, 610007
References
1. Wright S. Isolation by distance. Genetics. 1943;28(2):114-138. URL: https://www.genetics.org/content/28/2/114
2. Wright S. The genetical structure of populations. Ann. Eugen. 1951;15(1):323-354. https://doi.org/10.1111/j.1469-1809.1949.tb02451.x
3. Kuznetsov V. M. F-statistiki Rayta: otsenka i interpretatsiya. [Wright’s F-statistics: estimation and interpretation]. Problemy biologii produktivnykh zhivotnykh. 2014;(4):80-104. (In Russ.). URL: https://elibrary.ru/item.asp?id=22833217
4. Jost L., Archer F., Flanagan S., Gaggiotti O., Hoban S., Latch E. Differentiation measures for conservation genetics. Evol. Appl. 2018;11(7):1139-1148. https://doi.org/10.1111/eva.12590
5. Balloux F., Lugon-Moulin N. The estimation of population differentiation with microsatellite markers. Mol. Ecol. 2002;11(2):155-165. https://doi.org/10.1046/j.0962-1083.2001.01436.x
6. Wright S. Evolution and the genetics of populations. Vol. 4. Variability within and among natural populations. Univ. Chicago, 1978. 590 p.
7. Nei M. Analysis of gene diversity in subdivided populations. Proc. Nat. Acad. Sci. USA. 1973;70(12/1):3321-3323. https://doi.org/10.1073/pnas.70.12.3321
8. Nei M., Chesser R. K. Estimation of fixation indexes and gene diversities. Ann. Hum. Genet. 1983;47(3):253-259. https://doi.org/10.1111/j.1469-1809.1983.tb00993.x
9. Kuznetsov V. M. Metody Neya dlya analiza geneticheskikh razlichiy mezhdu populyatsiyami. [Nei’s methods for analyzing genetic differences between populations]. Problemy biologii produktivnykh zhivotnykh = Problems of Productive Animal Biology. 2020;(1):91-110. (In Russ.). URL: https://elibrary.ru/item.asp?id=43811467
10. Hedrick P. W. A standardized genetic differentiation measure. Evolution. 2005;59(8):1633-1638. URL: https://www.jstor.org/stable/3449070
11. Meirmans P. G., Hedrick P. W. Assessing population structure: FST and related measures. Mol. Ecol. Res. 2011;11(1):5-18. https://doi.org/10.1111/j.1755-0998.2010.02927.x
12. Jost L. G ST and its relatives do not measure differentiation. Mol. Ecol. 2008;17(18):4015-4026. https://doi.org/10.1111/j.1365-294X.2008.03887.x
13. Whitlock M. C. G′ ST and D do not replace FST . Mol. Ecol. 2011;20(6):1083-1091. https://doi.org/10.1111/j.1365-294X.2010.04996.x
14. Putman A. I., Carbone I. Challenges in analysis and interpretation of microsatellite data for population genetic studies. Ecol. Evol. 2014;4(22):4399-4428. https://doi.org/10.1002/ece3.1305
15. Cockerham C. C. Variance of gene frequencies. Evolution. 1969;23(1):72‐84. https://doi.org/10.1111/j.1558-5646.1969.tb03496.x
16. Cockerham C. C. Analyses of gene frequencies. Genetics.1973;74(4):679-700. URL: https://www.genetics.org/content/74/4/679
17. Weir B. S., Сockerham C. C. Estimating F-statistics for the analysis of population structure. Evolution. 1984;38(6):1358-1310. https://doi.org/10.2307/2408641
18. Very B. Analiz geneticheskikh dannykh. [Genetic data analysis]. Moscow: Mir, 1995. 400 p.
19. Excoffier L., Smouse P. E., Quattro J. M. Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics. 1992;131(2):479-491. URL: https://www.genetics.org/content/131/2/479
20. Michdakis Y., Excoffied L. A Generic estimation of population subdivision using distances between alleles with special reference for microsatellite loci. Genetics. 1996;142(3):1061-1064. URL: https://www.genetics.org/content/genetics/142/3/1061.full.pdf
21. Slatkin M. A. Measure of population subdivision based on microsatellite allele frequencies. Genetics.1995;139(1):457-462. URL: https://www.genetics.org/content/139/1/457
22. Kuznetsov V. M. Sravnenie metodov otsenki geneticheskoy differentsiatsii popu-lyatsiy po mikrosatellitnym markeram. [Comparison of methods for evaluating genetic differentiation of populations by microsatellite markers]. Agrarnaya nauka Evro-Severo-Vostoka = Agricultural Science Euro-North-East. 2020;21(2):169-182. (In Russ.). https://doi.org/10.30766/2072-9081.2020.21.2.169-182
23. Nei M. Genetic distance between populations. Amer. Natur. 1972;106(949):283-292. https://doi.org/10.1086/282771
24. Nei M. Estimation of average heterozygosity and genetic distance from a small number of individuals. Genetics. 1978;89(3):583-590. URL: https://www.genetics.org/content/89/3/583
25. Peakall R., Smouse P. GenAlEx Tutorial 1: Introduction to population genetic analysis. Australian National University. 2012. 57 p. URL: https://mafiadoc.com/genalex-tutorial-1-introduction-to-population-genetic_597ef8441723dd6ae3e07272.html
26. Peakall R., Smouse P. E. GenAlEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research – an update. Bioinformatics. 2012;28(19):2537-2539. https://doi.org/10.1093/bioinformatics/bts460
27. Meirmans P. G. Using the AMOVA framework to estimate a standardized genetic differentiation measure. Evolution. 2006;60(11):2399-2402. https://doi.org/10.1111/j.0014-3820.2006.tb01874.x
28. Excoffier L., Laval G., Schneider S. Arlequin ver. 3.0: An integrated software package for population genetics data analysis. Evolutionary Bioinformatics Online. 2005;1:47-50. URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2658868/
29. Raymond M., Rousset F. GENEPOP (Version 1.2): Population genetics software for exact tests and ecumenicism. J. Hered. 1995;86(3):248-249. https://doi.org/10.1093/oxfordjournals.jhered.a111573
30. Rousset F. Genepop’007: a complete reimplementation of the Genepop software for Windows and Linux. Mol. Ecol. Res. 2008;8(1):103-106. https://doi.org/10.1111/j.1471-8286.2007.01931.x
31. Goodman S. J. RST CALC: A collection of computer programs for calculating unbiased estimates of genetic differentiation and determining their significance for microsatellite data. Mol. Ecol. 1997;6(9):881-885. https://doi.org/10.1046/j.1365-294X.1997.00260.x
32. Chao A., Ma K. H., Hsieh T. C., Chiu C. H. Online program SpadeR (Species-richness Prediction And Diversity Estimationin R). 2016. 88 p. https://doi.org/10.13140/RG.2.2.20744.62722
33. STATGRAPHICS® Centurion XVI User Manual. By StatPoint Technologies, Inc. 2010. 297 р.
34. Balloux F., Goudet J. Statistical properties of population differentiation estimators under stepwise mutation in a finite island model. Mol. Ecol. 2002;11(4):771-783. https://doi.org/10.1046/j.1365-294x.2002.01474.x
35. Excoffier L., Hamilton G. Comment on «Genetic Structure of Human Populations». Science. 2003;300(5627):1877. https://doi.org/10.1126/science.1083411
36. Nei M. Definition and estimation of fixation indices. Evolution. 1986;40(3):643-645. https://doi.org/10.1111/j.1558-5646.1986.tb00516.x
37. Hedrick P. W. Perspective: Highly variable loci and their interpretation in evolution and conservation. Evolution. 1999;53(2):313-318. https://doi.org/10.1111/j.1558-5646.1999.tb03767.x
38. Medugorac I., Veit-Kensch C. E., Ramljak J., Brka M., Marković B., Stojanović S., Bytyqi H., Kochoski L., Kume K., Grünenfelder H.-P., Bennewitz J., Förster M. Conservation priorities of genetic diversity in domesticated metapopulations: a study in taurine cattle breeds. Ecol. Evol. 2011;1(3):408-420. https://doi.org/10.1002/ece3.39
39. Abdelmanova A. S., Kharzinova V. R., Volkova V. V., Mishina A. I., Dotsev A. V., Sermyagin A. A., Boronetskaya O. I., Petrikeeva L. V., Chinarov R. Yu, Brem G., Zinovieva N. A. Genetic Diversity of Historical and Modern Populations of Russian Cattle Breeds Revealed by Microsatellite Analysis. Genes. 2020;11(940):1-15. https://doi.org/10.3390/genes11080940
40. Kharzinova V. R., Zinov'eva N. A. Pattern geneticheskogo raznoobraziya u lokal'nykh i kommercheskikh porod sviney na osnove analiza mikrosatellitov. [The pattern of genetic diversity of different breeds of pigs based on microsatellite analysis]. Vavilovskiy zhurnal genetiki i selektsii = Vavilov Journal of Genetics and Breeding. 2020;24(7):747-754. (In Russ.). https://doi.org/10.18699/VJ20.669
41. Huson H. J., Sonstegard T. S., Godfrey J., Hambrook D., Wolfe C., Wiggans G., Blackburn H., VanTassell C. P. A Genetic Investigation of Island Jersey Cattle, the Foundation of the Jersey Breed: Comparing Population Structure and Selection to Guernsey, Holstein, and United States Jersey Cattle. Front. Genet. 2020;11(366):1-17. https://doi.org/10.3389/fgene.2020.00366
42. Peres-Neto P. R., Jackson D. A. How well do multivariate data sets match? The advantages of a Procrustean superimposition approach over the Mantel test. Oecologia. 2001;129(2):169-178. https://doi.org/10.1007/s004420100720
43. Dray S., Chessel D., Thioulouse J. Procrustean co-inertia analysis for the linking of multivariate datasets. Écoscience. 2003;10(1):110-119. https://doi.org/10.1080/11956860.2003.11682757
44. Sermyagin A. A., Dotsev A. V., Gladyr E. A., Traspov A. A., Deniskova T. E., Kostyunina O. V., Reyer H., Wimmers K., Barbato M., Paronyan I. A., Plemyashov K. V., Sölkner J., Popov R. G., Brem G., Zinovieva N. A. Whole-genome SNP analysis elucidates the genetic structure of Russian cattle and its relationship with Eurasian taurine breeds. Genet. Sel. Evol. 2018;50(37):1-13. https://doi.org/10.1186/s12711-018-0408-8
45. Ma L., Sonstegard T. S., Cole J. B., Van Tassell C. P., Wiggans G. R., Crooker B. A., Tan C., Prakapenka D., Liu G., Da Y. Genome changes due to artificial selection in U.S. Holstein cattle. BMC Genomics. 2019;20(128):1-14. https://doi.org/10.1186/s12864-019-5459-x
46. Cooper T. A., Eaglen S. A. E., Wiggans G. R., Jenko J., Huson H. J., Morrice D. R., Bichard M., de L. Luff W. G., Woolliams J. A. Genomic evaluation, breed identification, and population structure of Guernsey cattle in North America, Great Britain, and the Isle of Guernsey. J. Dairy Sci. 2016;99(7):5508-5515. http://dx.doi.org/10.3168/jds.2015-10445
Review
For citations:
Kuznetsov V.M. Assessment of genetic differentiation of populations by analysis of molecular variance (analytical review). Agricultural Science Euro-North-East. 2021;22(2):167-187. (In Russ.) https://doi.org/10.30766/2072-9081.2021.22.2.167-187