Preview

Аграрная наука Евро-Северо-Востока

Расширенный поиск

Анализ показателей разнообразия STR-локусов в выборках производителей красной скандинавской и голштинской пород

https://doi.org/10.30766/2072-9081.2024.25.3.465-482

Аннотация

Standardized indicators of α- and β-diversity (𝒚′𝒌𝒎) calculated by DNA microsatellites (STR) in samples of bulls of Red Scandinavian (RED, n = 29) and Holstein (HOL, n = 45) breeds (each with three sub-samples) were analyzed using one-two- and multivariate statistics methods. The data represented two 11×7 matrices: objects – 11 STR loci (Eth3, Inra23, Tgla227, Tgla126, Tgla122, Sps115, Eth225, Tgla53, Bm2113, Bm1824, Eth10; No.1-11), variables – three indicators of α-diversity (number of alleles and effective alleles per locus, heterozygosity) and four – β-diversity (indices: fixation by Nei, differentiation by Meirmans-Hedrick, Jost and Shannon-Sherwin). ANOVA, using a fixed-type model, revealed a statistically significant (pvalue< 0.02) effect on the variability of 𝒚′𝒌𝒎 factors «breed» (2 %), «locus» (36.7 %) and their interaction (15.6 %). According to the mixed-type model (the «locus» factor as random), only the interaction effect was statistically significant (25.8 %, pvalue< 0.0001). The probability of a noncoincidence between the numbers of a randomly selected pair of loci from the RED and HOL samples was 31 %. The average Euclidean distance between the two samples, calculated by analogical loci, was 37.8±5.35 %. The Mantel correlation between the matrices of paired interlocus distances in RED and in HOL samples was 0.257±0.130 (pvalue = 0.056). The ordinations of loci and their grouping (structuring) in the space of the two main components of the REL sample and the HOL sample differed (Procrust test: m2 = 0.994, m12 = 0.747, pperm = 0.164, 𝒓𝟐𝑷𝒓𝒐𝒄 = 0.253). Estimates of the distance between samples based on the profiles of the α- and β-diversity of loci did not contradict, in general, the genetic distances calculated by allelic frequencies (29–37 %). To analyze the covariance (commonality) of multivariate RED and HOL sample data, a two-block partial least squares (2B-PLS) method was used. The integrated latent variables (LV) maximized the total square of covariance («squared covar» = 14.3 %), in which 83 % accounted for the first LV with max «weights» in terms of α-diversity (aLV). The second LV accounted for 16.7 % with max «weights» in terms of β-diversity (βLV). The linear relationship between RED and HOL samples for aLV was 0.717 (pvalue = 0.013), for βLV – 0.395 (pvalue = 0.229), averaged – 0.56 (pvalue = 0.025). The commonality (co-dispersion) of the two samples for aLV and βLV was estimated at 25,0–32.5 %. 2B-PLS analysis based on reduced data (only for α-diversity) showed a max «squared covar» of 0.393, in which 99.9 % accounted for the first LV (LV1). According to LV1, the linear relationship between RED and HOL samples was estimated at 0.659 (pvalue = 0.0253), the co-dispersion was 43.4 % (according to aLV it was 51.4 %). The ordinations of loci in the coordinate space of the RED and HOL samples for the complete (αLV) and reduced (LV1) datasets had a good match (Procrust test: m2 = 0.0742, m12 = 0.0728, pperm = 0.001, 𝒓𝟐𝑷𝒓𝒐𝒄 = 0.927). In the structure of the inter-sample covariance, «clumps» of loci with a bootstrap probability of [grouping] 50, 75 and 100 % were distinguished. It can be assumed that the RED and HOL samples had some consistency (congruence) in terms of the α-diversity of the loci of the same name. The extension of the «multivariate» approach to descriptive statistics of α-diversity of 7 breeds of dairy cattle and 11 breeds of pigs showed a fairly good correspondence of the results (differentiation index, PCA-ordination) with those obtained using «traditional» methods (pperm of matching ordinations 0.054 and 0.004). The approaches and methods considered expand the possibilities of population-genetic [and breeding-zootechnical] studies in which multidimensional data sets are the norm, not the exception.

Об авторе

В. М. Кузнецов
ФГБНУ «Федеральный аграрный научный центр Северо-Востока имени Н. В. Рудницкого»
Россия

Кузнецов Василий Михайлович, доктор с.-х. наук, профессор, зав. лабораторией популяционной  генетики в животноводстве

ул. Ленина, д. 166а, г. Киров, 610007



Список литературы

1. Кузнецов В. М. Анализ полокусных оценок аллельного разнообразия STR-маркеров в выборке быковпроизводителей. Аграрная наука Евро-Северо-Востока. 2023;24(5):888–906. DOI: https://doi.org/10.30766/2072-9081.2023.24.5.888-906 EDN: LCTSPP

2. Sheldon A. L. Equitability indices: Dependence on the species count. Ecology. 1969;50(3):466–467. DOI: https://doi.org/10.2307/1933900

3. Хедрик Ф. Генетика популяций. М.: Техносфера, 2003. 592 с.

4. Nei M. Analysis of gene diversity in subdivided populations. Proceedings of the National Academy of Sciences. 1973;70(12):3321–3323. DOI: https://doi.org/10.1073/pnas.70.12.3321

5. Weir B. S., Сockerham C. C. Estimating F-statistics for the analysis of population structure. Evolution. 1984;38(6):1358–1370. DOI: https://doi.org/10.2307/2408641

6. Meirmans P. G., Hedrick P. W. Assessing population structure: FST and related measures. Molecular Ecology Resources. 2011;11(1):5–18. DOI: https://doi.org/10.1111/j.1755-0998.2010.02927.x

7. Jost L. GST and its relatives do not measure differentiation. Molecular Ecology. 2008;17(18):4015–4026. DOI: https://doi.org/10.1111/j.1365-294X.2008.03887.x

8. Shannon C. E. A mathematical theory of communication. reprinted with corrections from. The Bell System Technical Journal. 1948;27(3):379–423, 623–656. URL: https://people.math.harvard.edu/~ctm/home/text/others/shannon/entropy/entropy.pdf

9. Sherwin W. B. Entropy and information approaches to genetic diversity and its expression: Genomic geography. Entropy. 2010;12(7):1765–1798. DOI: https://doi.org/10.3390/e12071765

10. Кузнецов В. М. F-статистики Райта: оценка и интерпретация. Проблемы биологии продуктивных животных. 2014;(4):80–104. Режим доступа: https://www.elibrary.ru/item.asp?id=22833217 EDN: TFRDMN

11. Кузнецов В. М. Методы Нея для анализа генетических различий между популяциями. Проблемы биологии продуктивных животных. 2020;(1):91–110. DOI: https://doi.org/10.25687/1996-6733.prodanimbiol.2020.1.91-110 EDN: DSEMYO

12. Кузнецов В. М. Сравнение методов оценки генетической дифференциации популяций по микросателлитным маркерам. Аграрная наука Евро-Северо-Востока. 2020;21(2):169–182. DOI: https://doi.org/10.30766/2072-9081.2020.21.2.169-182 EDN: FYQNTE

13. Кузнецов В. М. Оценка генетической дифференциации популяций молекулярным дисперсионным анализом. Аграрная наука Евро-Северо-Востока. 2021;22(2):167–187. DOI: https://doi.org/10.30766/2072-9081.2021.22.2.167-187 EDN: LGYMFT

14. Кузнецов В. М. Информационно-энтропийный подход к анализу генетического разнообразия популяций. Аграрная наука Евро-Северо-Востока. 2022;23(2):159–173. DOI: https://doi.org/10.30766/2072-9081.2022.23.2.159-173 EDN: LSSUYZ

15. Peakall R., Smouse P. E. GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Molecular Ecology Notes. 2006;6(1):288–295. DOI: https://doi.org/10.1111/j.1471-8286.2005.01155.x

16. 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. DOI: https://doi.org/10.1093/bioinformatics/bts460

17. Smouse P. E., Whitehead M., Peakall R. An informational diversity framework, illustrated with sexually deceptive orchids in early stages of speciation. Molecular Ecology Resources. 2015;15(6):1375–1384. DOI: https://doi.org/10.1111/1755-0998.12422

18. Hammer Ø., Harper D. A. T., Ryan P. D. PAST: Paleontological statistics software package for education and data analysis. Palaeontologia Electronica. 2001;4(1):1–9. URL: https://palaeo-electronica.org/2001_1/past/past.pdf

19. Camúñez L. E. M., Roca C. F., Tornero R. Guía de KyPlot: Programa de análisis de datosencontextocientífico. Facultat de Física- Universitat de València (UVEG). 2008. 33 p.

20. 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. DOI: https://doi.org/10.1007/s004420100720

21. Dray S., Chessel D., Thioulouse J. Procrustean co-inertia analysis for the linking of multivariate datasets. Écoscience. 2003;10(1):110–119. DOI: https://doi.org/10.1080/11956860.2003.11682757

22. McGraw K. O, Wong S. P. Forming inferences about some intraclass correlation coefficients. Psychological Methods. 1996;1(1):30–46. DOI: https://doi.org/10.1037/1082-989X.1.1.30

23. Koo T. K., Li M. Y. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. Journal of Chiropractic Medicine. 2016;15(2):155–163. DOI: https://doi.org/10.1016/j.jcm.2016.02.012

24. Шеффе Г. Дисперсионный анализ. М.: Наука. Главная редакция физико-математической литературы, 1980. 512 с. Режим доступа: https://studizba.com/files/show/djvu/3369-1-sheffe-g--dispersionnyy-analiz.html

25. Гласс Дж., Стэнли Дж. Статистические методы в педагогике. Пер. с англ. Л. И. Хайрусовой, общ. ред. Ю. П. Адлера. Послесл. Ю. П. Адлера и А. Н. Ковалева. М.: изд-во «Прогресс», 1976. 495 с.

26. VanRoon P., Zakizadeh J., Chartier S. Partial least squares tutorial for analyzing neuroimaging data. The Quantitative Methods for Psychology. 2014;10(2):200–215. DOI: https://doi.org/10.20982/tqmp.10.2.p200

27. Sampson P. D., Streissguth A. P., Barr H. M., Bookstein F. L. Neurobehavioral effects ofprenatal alcohol: Part II. Partial least squares analysis. Neurotoxicology and Teratology. 1989;11(5):477–491. DOI: https://doi.org/10.1016/0892-0362(89)90025-1

28. Rohlf F. J., Сorti M. Use of two-block partial least-squares to study covariation in shape. Systematyc Biology. 2000;49(4):740–753. DOI: https://doi.org/10.1080/106351500750049806

29. Fisher R. A. Combining independent tests of significance. American Statistician. 1948;2(5):30. DOI: https://digital.library.adelaide.edu.au/dspace/bitstream/2440/15258/1/224A.pdf

30. Czerneková V., Kott T., Dudková G., Sztankóová Z., Soldát J. Genetic diversity between seven Central European cattle breeds as revealed by microsatellite analysis. Czech Journal of Animal Science. 2006;51(1):1–7. DOI: https://doi.org/10.17221/3902-CJAS

31. Харзинова В. Р., Зиновьева Н. А. Паттерн генетического разнообразия у локальных и коммерческих пород свиней на основе анализа микросателлитов. Вавиловский журнал генетики и селекции. 2020;24(7):747–754. DOI: https://doi.org/10.18699/VJ20.669 EDN: BJRYAW

32. Hedrick P. W. Perspective: Highly variable loci and their interpretation in evolution and conservation. Evolution. 1999;53(2):313–318. DOI: https://doi.org/10.1111/j.1558-5646.1999.tb03767.x

33. Hedrick P. W. A standardized genetic differentiation measure. Evolution. 2005;59(8):1633–1638. URL: https://www.jstor.org/stable/3449070


Рецензия

Для цитирования:


Кузнецов В.М. Анализ показателей разнообразия STR-локусов в выборках производителей красной скандинавской и голштинской пород. Аграрная наука Евро-Северо-Востока. 2024;25(3):465-482. https://doi.org/10.30766/2072-9081.2024.25.3.465-482

For citation:


Kuznetsov V.M. Analysis of the diversity of STR-loci in the samples of bulls of Red Scandinavian and Holstein breeds. Agricultural Science Euro-North-East. 2024;25(3):465-482. (In Russ.) https://doi.org/10.30766/2072-9081.2024.25.3.465-482

Просмотров: 232


Creative Commons License
Контент доступен под лицензией Creative Commons Attribution 4.0 License.


ISSN 2072-9081 (Print)
ISSN 2500-1396 (Online)