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Journal articles on the topic 'Genetics – Statistical methods'

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Published: 4 June 2021
Last updated: 28 July 2024

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1

Montana, G. "Statistical methods in genetics." Briefings in Bioinformatics 7, no. 3 (May 23, 2006): 297–308. http://dx.doi.org/10.1093/bib/bbl028.

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2

Fisher, R. "Statistical methods in genetics." International Journal of Epidemiology 39, no. 2 (February 22, 2010): 329–35. http://dx.doi.org/10.1093/ije/dyp379.

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3

Edwards, A. W. F. "Statistical Methods for Evolutionary Trees." Genetics 183, no. 1 (September 2009): 5–12. http://dx.doi.org/10.1534/genetics.109.107847.

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4

Sham, P. C. "Statistical methods in psychiatric genetics." Statistical Methods in Medical Research 7, no. 3 (March 1, 1998): 279–300. http://dx.doi.org/10.1191/096228098677382724.

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5

Sham, Pak C. "Statistical methods in psychiatric genetics." Statistical Methods in Medical Research 7, no. 3 (June 1998): 279–300. http://dx.doi.org/10.1177/096228029800700305.

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6

GUILLOT, GILLES, RAPHA��L LEBLOIS, AUR��LIE COULON, and ALAIN C. FRANTZ. "Statistical methods in spatial genetics." Molecular Ecology 18, no. 23 (December 2009): 4734–56. http://dx.doi.org/10.1111/j.1365-294x.2009.04410.x.

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7

Schaid, D. J., X. Tong, B. Larrabee, R. B. Kennedy, G. A. Poland, and J. P. Sinnwell. "Statistical Methods for Testing Genetic Pleiotropy." Genetics 204, no. 2 (August 15, 2016): 483–97. http://dx.doi.org/10.1534/genetics.116.189308.

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8

Zeegers, M. P. "Statistical Methods in Genetic Epidemiology." Journal of Medical Genetics 41, no. 12 (December 1, 2004): 958. http://dx.doi.org/10.1136/jmg.2004.021113.

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9

Wyszynski, Diego F. "Statistical Methods in Genetic Epidemiology." American Journal of Human Genetics 76, no. 1 (January 2005): 190. http://dx.doi.org/10.1086/427114.

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10

Ott, Jurg, and Helen Donis-Keller. "Statistical Methods in Genetic Mapping." Genomics 22, no. 2 (July 1994): 496–97. http://dx.doi.org/10.1006/geno.1994.1421.

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11

Matise, Tara Cox, Helen Onis-Keller, and Jurg Ott. "Statistical Methods in Genetic Mapping." Genomics 36, no. 1 (August 1996): 223–25. http://dx.doi.org/10.1006/geno.1996.0456.

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12

Fu, Yun-Xin. "Statistical Methods for Analyzing Drosophila Germline Mutation Rates." Genetics 194, no. 4 (May 1, 2013): 927–36. http://dx.doi.org/10.1534/genetics.113.151571.

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13

Sun, Jingyi. "Application and Challenges of Statistical Methods in Biological Genetics." Highlights in Science, Engineering and Technology 40 (March 29, 2023): 43–49. http://dx.doi.org/10.54097/hset.v40i.6519.

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Humans are curious about genes, from plants to animals, from breeding to diseases. For centuries, it has been considered a genetic disease. With the development of medicine, people have also realized that many diseases are heritable. With the birth of modern statistics, humans have created many models. This article focuses on the application of statistical methods in biological genetics. This paper introduces the principles and their applications of Least Absolute Shrinkage and Selection Operator Regression, the Chen-Stein Method, and Logical Regression model in different branches, such as gene set selection. These models can effectively tackle the problem of reproducibility in genetics to a certain extent when used correctly. In addition, they offer an effective means of data analysis in genetics field. Although the three models have their weaknesses, such as the use and selection of a priori, it is reasonable to believe that with the continuous improvement of the models by mathematicians, they can have better prospects.
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14

Pluta, Dustin, Zhaoxia Yu, Tong Shen, Chuansheng Chen, Gui Xue, and Hernando Ombao. "Statistical methods and challenges in connectome genetics." Statistics & Probability Letters 136 (May 2018): 83–86. http://dx.doi.org/10.1016/j.spl.2018.02.048.

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15

Zhu, Yicheng, Teresa Neeman, Von Bing Yap, and Gavin A. Huttley. "Statistical Methods for Identifying Sequence Motifs Affecting Point Mutations." Genetics 205, no. 2 (December 14, 2016): 843–56. http://dx.doi.org/10.1534/genetics.116.195677.

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16

Ye, Shuyun, Rhonda Bacher, Mark P. Keller, Alan D. Attie, and Christina Kendziorski. "Statistical Methods for Latent Class Quantitative Trait Loci Mapping." Genetics 206, no. 3 (May 26, 2017): 1309–17. http://dx.doi.org/10.1534/genetics.117.203885.

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17

Hackinger, Sophie, and Eleftheria Zeggini. "Statistical methods to detect pleiotropy in human complex traits." Open Biology 7, no. 11 (November 2017): 170125. http://dx.doi.org/10.1098/rsob.170125.

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In recent years pleiotropy, the phenomenon of one genetic locus influencing several traits, has become a widely researched field in human genetics. With the increasing availability of genome-wide association study summary statistics, as well as the establishment of deeply phenotyped sample collections, it is now possible to systematically assess the genetic overlap between multiple traits and diseases. In addition to increasing power to detect associated variants, multi-trait methods can also aid our understanding of how different disorders are aetiologically linked by highlighting relevant biological pathways. A plethora of available tools to perform such analyses exists, each with their own advantages and limitations. In this review, we outline some of the currently available methods to conduct multi-trait analyses. First, we briefly introduce the concept of pleiotropy and outline the current landscape of pleiotropy research in human genetics; second, we describe analytical considerations and analysis methods; finally, we discuss future directions for the field.
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18

Nathoo, Farouk S., Linglong Kong, and Hongtu Zhu. "A review of statistical methods in imaging genetics." Canadian Journal of Statistics 47, no. 1 (February 25, 2019): 108–31. http://dx.doi.org/10.1002/cjs.11487.

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19

Wangkumhang, Pongsakorn, and Garrett Hellenthal. "Statistical methods for detecting admixture." Current Opinion in Genetics & Development 53 (December 2018): 121–27. http://dx.doi.org/10.1016/j.gde.2018.08.002.

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20

Hoeschele, I., P. Uimari, F. E. Grignola, Q. Zhang, and K. M. Gage. "Advances in Statistical Methods to Map Quantitative Trait Loci in Outbred Populations." Genetics 147, no. 3 (November 1, 1997): 1445–57. http://dx.doi.org/10.1093/genetics/147.3.1445.

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Statistical methods to map quantitative trait loci (QTL) in outbred populations are reviewed, extensions and applications to human and plant genetic data are indicated, and areas for further research are identified. Simple and computationally inexpensive methods include (multiple) linear regression of phenotype on marker genotypes and regression of squared phenotypic differences among relative pairs on estimated proportions of identity-by-descent at a locus. These methods are less suited for genetic parameter estimation in outbred populations but allow the determination of test statistic distributions via simulation or data permutation; however, further inferences including confidence intervals of QTL location require the use of Monte Carlo or bootstrap sampling techniques. A method which is intermediate in computational requirements is residual maximum likelihood (REML) with a covariance matrix of random QTL effects conditional on information from multiple linked markers. Testing for the number of QTLs on a chromosome is difficult in a classical framework. The computationally most demanding methods are maximum likelihood and Bayesian analysis, which take account of the distribution of multilocus marker-QTL genotypes on a pedigree and permit investigators to fit different models of variation at the QTL. The Bayesian analysis includes the number of QTLS on a chromosome as an unknown.
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21

Johnston, A. W. "Methodology in Medical Genetics: an Introduction to Statistical Methods." Postgraduate Medical Journal 63, no. 736 (February 1, 1987): 157. http://dx.doi.org/10.1136/pgmj.63.736.157.

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22

Lange, K., M. Boehnke, D. R. Cox, and K. L. Lunetta. "Statistical methods for polyploid radiation hybrid mapping." Genome Research 5, no. 2 (September 1, 1995): 136–50. http://dx.doi.org/10.1101/gr.5.2.136.

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23

Wu, Rongling, Chang-Xing Ma, Maria Gallo-Meagher, Ramon C. Littell, and George Casella. "Statistical Methods for Dissecting Triploid Endosperm Traits Using Molecular Markers: An Autogamous Model." Genetics 162, no. 2 (October 1, 2002): 875–92. http://dx.doi.org/10.1093/genetics/162.2.875.

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AbstractThe endosperm, a result of double fertilization in flowering plants, is a triploid tissue whose genetic composition is more complex than diploid tissue. We present a new maximum-likelihood-based statistical method for mapping quantitative trait loci (QTL) underlying endosperm traits in an autogamous plant. Genetic mapping of quantitative endosperm traits is qualitatively different from traits for other plant organs because the endosperm displays complicated trisomic inheritance and represents a younger generation than its mother plant. Our endosperm mapping method is based on two different experimental designs: (1) a one-stage design in which marker information is derived from the maternal genome and (2) a two-stage hierarchical design in which marker information is derived from both the maternal and offspring genomes (embryos). Under the one-stage design, the position and additive effect of a putative QTL can be well estimated, but the estimates of the dominant and epistatic effects are upward biased and imprecise. The two-stage hierarchical design, which extracts more genetic information from the material, typically improves the accuracy and precision of the dominant and epistatic effects for an endosperm trait. We discuss the effects on the estimation of QTL parameters of different sampling strategies under the two-stage hierarchical design. Our method will be broadly useful in mapping endosperm traits for many agriculturally important crop plants and also make it possible to study the genetic significance of double fertilization in the evolution of higher plants.
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24

Dupuis, Josée, and David Siegmund. "Statistical Methods for Mapping Quantitative Trait Loci From a Dense Set of Markers." Genetics 151, no. 1 (January 1, 1999): 373–86. http://dx.doi.org/10.1093/genetics/151.1.373.

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Abstract Lander and Botstein introduced statistical methods for searching an entire genome for quantitative trait loci (QTL) in experimental organisms, with emphasis on a backcross design and QTL having only additive effects. We extend their results to intercross and other designs, and we compare the power of the resulting test as a function of the magnitude of the additive and dominance effects, the sample size and intermarker distances. We also compare three methods for constructing confidence regions for a QTL: likelihood regions, Bayesian credible sets, and support regions. We show that with an appropriate evaluation of the coverage probability a support region is approximately a confidence region, and we provide a theroretical explanation of the empirical observation that the size of the support region is proportional to the sample size, not the square root of the sample size, as one might expect from standard statistical theory.
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25

Zou, Wei, and Zhao-Bang Zeng. "Statistical Methods for Mapping Multiple QTL." International Journal of Plant Genomics 2008 (June 8, 2008): 1–8. http://dx.doi.org/10.1155/2008/286561.

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Since Lander and Botstein proposed the interval mapping method for QTL mapping data analysis in 1989, tremendous progress has been made in the last many years to advance new and powerful statistical methods for QTL analysis. Recent research progress has been focused on statistical methods and issues for mapping multiple QTL together. In this article, we review this progress. We focus the discussion on the statistical methods for mapping multiple QTL by maximum likelihood and Bayesian methods and also on determining appropriate thresholds for the analysis.
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26

Zou, G. Y. "Statistical Methods for the Analysis of Genetic Association Studies." Annals of Human Genetics 70, no. 2 (March 2006): 262–76. http://dx.doi.org/10.1111/j.1529-8817.2005.00213.x.

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27

Cannings, C. "Mathematical and Statistical Methods for Genetic Analysis (2nd ed)." Heredity 92, no. 1 (December 15, 2003): 51. http://dx.doi.org/10.1038/sj.hdy.6800368.

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28

Ellsworth, Darrell L., Teri A. Manolio, and Mhs. "The Emerging Importance of Genetics in Epidemiologic Research III. Bioinformatics and Statistical Genetic Methods." Annals of Epidemiology 9, no. 4 (May 1999): 207–24. http://dx.doi.org/10.1016/s1047-2797(99)00007-1.

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29

Whelan, Simon, and Rasmus Nielsen. "Statistical Methods in Molecular Evolution." Systematic Biology 55, no. 4 (August 1, 2006): 698–700. http://dx.doi.org/10.1080/10635150600899780.

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30

Finney, D. J. "Commentary: 'Statistical Methods in Genetics' by Sir Ronald A Fisher." International Journal of Epidemiology 39, no. 2 (February 22, 2010): 339–40. http://dx.doi.org/10.1093/ije/dyp377.

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31

Bishop, D. T. "Genetic Analysis Workshop 7: Recent progress in statistical methods." Cytogenetic and Genome Research 59, no. 2-3 (1992): 131–32. http://dx.doi.org/10.1159/000133224.

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32

Ewens, Warren J. "Book review: Mathematical and statistical methods for genetic analysis." Genetic Epidemiology 24, no. 2 (January 23, 2003): 158–59. http://dx.doi.org/10.1002/gepi.10214.

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33

Balkenhol, Niko, Lisette P. Waits, and Raymond J. Dezzani. "Statistical approaches in landscape genetics: an evaluation of methods for linking landscape and genetic data." Ecography 32, no. 5 (October 2009): 818–30. http://dx.doi.org/10.1111/j.1600-0587.2009.05807.x.

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34

Ott, Jurg, and Josephine Hoh. "Statistical multilocus methods for disequilibrium analysis in complex traits." Human Mutation 17, no. 4 (2001): 285–88. http://dx.doi.org/10.1002/humu.25.

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35

Piegorsch, Walter W., and Joseph K. Haseman. "Statistical methods for analyzing developmental toxicity data." Teratogenesis, Carcinogenesis, and Mutagenesis 11, no. 3 (1991): 115–33. http://dx.doi.org/10.1002/tcm.1770110302.

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36

Beerli, Peter. "STATISTICAL METHODS IN (MOLECULAR) EVOLUTION." Evolution 60, no. 2 (February 2006): 421–23. http://dx.doi.org/10.1111/j.0014-3820.2006.tb01122.x.

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37

Beerli, Peter. "STATISTICAL METHODS IN (MOLECULAR) EVOLUTION1." Evolution 60, no. 2 (2006): 421. http://dx.doi.org/10.1554/br06-6.1.

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38

Andreassen, Ole, Chi-Hua Chen, and Jordan Smoller. "Leveraging Novel Statistical Methods And Big Data To Improve Imaging Genetics." European Neuropsychopharmacology 29 (2019): S725. http://dx.doi.org/10.1016/j.euroneuro.2017.06.043.

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39

Fisher Box, J. "Commentary: On RA Fisher's Bateson lecture on statistical methods in genetics." International Journal of Epidemiology 39, no. 2 (February 22, 2010): 335–39. http://dx.doi.org/10.1093/ije/dyp376.

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40

Dupuis, J., P. O. Brown, and D. Siegmund. "Statistical methods for linkage analysis of complex traits from high-resolution maps of identity by descent." Genetics 140, no. 2 (June 1, 1995): 843–56. http://dx.doi.org/10.1093/genetics/140.2.843.

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Abstract A multilocus model for complex traits is described that generalizes the additive and multiplicative models and hence allows simultaneously for both heterogeneity and gene interaction (epistasis). Statistical methods of linkage analysis are discussed under the assumption that identity by descent data from a dense set of polymorphic markers are available. Three methods, single locus search, simultaneous search and conditional search, are described and compared.
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41

Causton, D. R., and J. S. Rustagi. "Introduction to Statistical Methods." Biometrics 42, no. 4 (December 1986): 1005. http://dx.doi.org/10.2307/2530726.

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42

McCulloch, C. E., and P. Sprent. "Applied Nonparametric Statistical Methods." Biometrics 46, no. 2 (June 1990): 541. http://dx.doi.org/10.2307/2531462.

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43

Kourouklis, S., and M. N. Das. "Statistical Methods and Concepts." Biometrics 47, no. 1 (March 1991): 345. http://dx.doi.org/10.2307/2532523.

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44

Bailey, N. T. J. "Statistical Methods in Biology." Biometrics 51, no. 1 (March 1995): 386. http://dx.doi.org/10.2307/2533361.

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45

Wood, S. N., and P. Sprent. "Data Driven Statistical Methods." Biometrics 54, no. 4 (December 1998): 1678. http://dx.doi.org/10.2307/2533696.

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46

Lan, Tongtong, Bo Yang, Xuefen Zhang, Tong Wang, and Qing Lu. "Statistical Methods and Software for Substance Use and Dependence Genetic Research." Current Genomics 20, no. 3 (July 22, 2019): 172–83. http://dx.doi.org/10.2174/1389202920666190617094930.

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Background: Substantial substance use disorders and related health conditions emerged during the mid-20th century and continue to represent a remarkable 21st century global burden of disease. This burden is largely driven by the substance-dependence process, which is a complex process and is influenced by both genetic and environmental factors. During the past few decades, a great deal of progress has been made in identifying genetic variants associated with Substance Use and Dependence (SUD) through linkage, candidate gene association, genome-wide association and sequencing studies. Methods: Various statistical methods and software have been employed in different types of SUD genetic studies, facilitating the identification of new SUD-related variants. Conclusion: In this article, we review statistical methods and software that are currently available for SUD genetic studies, and discuss their strengths and limitations.
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47

YI, NENGJUN. "Statistical analysis of genetic interactions." Genetics Research 92, no. 5-6 (December 2010): 443–59. http://dx.doi.org/10.1017/s0016672310000595.

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SummaryMany common human diseases and complex traits are highly heritable and influenced by multiple genetic and environmental factors. Although genome-wide association studies (GWAS) have successfully identified many disease-associated variants, these genetic variants explain only a small proportion of the heritability of most complex diseases. Genetic interactions (gene–gene and gene–environment) substantially contribute to complex traits and diseases and could be one of the main sources of the missing heritability. This paper provides an overview of the available statistical methods and related computer software for identifying genetic interactions in animal and plant experimental crosses and human genetic association studies. The main discussion falls under the three broad issues in statistical analysis of genetic interactions: the definition, detection and interpretation of genetic interactions. Recently developed methods based on modern techniques for high-dimensional data are reviewed, including penalized likelihood approaches and hierarchical models; the relationships between these methods are also discussed. I conclude this review by highlighting some areas of future research.
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48

Liò, Pietro. "Statistical bioinformatic methods in microbial genome analysis." BioEssays 25, no. 3 (February 20, 2003): 266–73. http://dx.doi.org/10.1002/bies.10231.

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49

Montesinos López, Osval A., Brandon Alejandro Mosqueda Gonzalez, Abelardo Montesinos-Lopez, and José Crossa. "Statistical Machine-Learning Methods for Genomic Prediction Using the SKM Library." Genes 14, no. 5 (April 28, 2023): 1003. http://dx.doi.org/10.3390/genes14051003.

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Genomic selection (GS) is revolutionizing plant breeding. However, because it is a predictive methodology, a basic understanding of statistical machine-learning methods is necessary for its successful implementation. This methodology uses a reference population that contains both the phenotypic and genotypic information of genotypes to train a statistical machine-learning method. After optimization, this method is used to make predictions of candidate lines for which only genotypic information is available. However, due to a lack of time and appropriate training, it is difficult for breeders and scientists of related fields to learn all the fundamentals of prediction algorithms. With smart or highly automated software, it is possible for these professionals to appropriately implement any state-of-the-art statistical machine-learning method for its collected data without the need for an exhaustive understanding of statistical machine-learning methods and programing. For this reason, we introduce state-of-the-art statistical machine-learning methods using the Sparse Kernel Methods (SKM) R library, with complete guidelines on how to implement seven statistical machine-learning methods that are available in this library for genomic prediction (random forest, Bayesian models, support vector machine, gradient boosted machine, generalized linear models, partial least squares, feed-forward artificial neural networks). This guide includes details of the functions required to implement each of the methods, as well as others for easily implementing different tuning strategies, cross-validation strategies, and metrics to evaluate the prediction performance and different summary functions that compute it. A toy dataset illustrates how to implement statistical machine-learning methods and facilitate their use by professionals who do not possess a strong background in machine learning and programing.
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50

Tajima, F. "Simple methods for testing the molecular evolutionary clock hypothesis." Genetics 135, no. 2 (October 1, 1993): 599–607. http://dx.doi.org/10.1093/genetics/135.2.599.

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Abstract Simple statistical methods for testing the molecular evolutionary clock hypothesis are developed which can be applied to both nucleotide and amino acid sequences. These methods are based on the chi-square test and are applicable even when the pattern of substitution rates is unknown and/or the substitution rate varies among different sites. Furthermore, some of the methods can be applied even when the outgroup is unknown. Using computer simulations, these methods were compared with the likelihood ratio test and the relative rate test. The results indicate that the powers of the present methods are similar to those of the likelihood ratio test and the relative rate test, in spite of the fact that the latter two tests assume that the pattern of substitution rates follows a certain model and that the substitution rate is the same among different sites, while such assumptions are not necessary to apply the present methods. Therefore, the present methods might be useful.
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