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Bibliografías temáticas / Statistical genetics / Artículos de revistas

Artículos de revistas sobre el tema "Statistical genetics"

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Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 31 de julio de 2024

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1

Dentine, M. R. y P. Narain. "Statistical Genetics." Biometrics 47, n.º 2 (junio de 1991): 789. http://dx.doi.org/10.2307/2532180.

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2

Graver, Christopher. "Statistical Genetics". Medicine & Science in Sports & Exercise 40, n.º 12 (diciembre de 2008): 2145. http://dx.doi.org/10.1249/01.mss.0000323663.04665.ec.

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3

Dries, David J. "STATISTICAL GENETICS". Shock 30, n.º 4 (octubre de 2008): 482. http://dx.doi.org/10.1097/01.shk.0000286294.71456.dc.

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4

Gomulkiewicz, Richard. "Statistical genetics". Mathematical Biosciences 110, n.º 1 (junio de 1992): 133–35. http://dx.doi.org/10.1016/0025-5564(92)90020-w.

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5

Majumder, Partha P. "Statistical genetics". Journal of Genetics 72, n.º 2-3 (diciembre de 1993): 103–4. http://dx.doi.org/10.1007/bf02927926.

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6

EGELAND, THORE y PETTER F. MOSTAD. "Statistical Genetics and Genetical Statistics: a Forensic Perspective*". Scandinavian Journal of Statistics 29, n.º 2 (junio de 2002): 297–307. http://dx.doi.org/10.1111/1467-9469.00284.

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7

Crusio, Wim E. "Handbook of Statistical Genetics". Genes, Brain and Behavior 7, n.º 7 (octubre de 2008): 832. http://dx.doi.org/10.1111/j.1601-183x.2008.00424_7.x.

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8

Shields, D. "Handbook of Statistical Genetics". Briefings in Bioinformatics 2, n.º 3 (1 de enero de 2001): 305–7. http://dx.doi.org/10.1093/bib/2.3.305.

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9

Montana, G. "Statistical methods in genetics". Briefings in Bioinformatics 7, n.º 3 (23 de mayo de 2006): 297–308. http://dx.doi.org/10.1093/bib/bbl028.

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10

Fisher, R. "Statistical methods in genetics". International Journal of Epidemiology 39, n.º 2 (22 de febrero de 2010): 329–35. http://dx.doi.org/10.1093/ije/dyp379.

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11

Allison, David B. "Statistical Genetics and Obesity". Nutrition Today 40, n.º 4 (julio de 2005): 170–72. http://dx.doi.org/10.1097/00017285-200507000-00008.

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12

Weir, B. S. "Challenges Facing Statistical Genetics". Journal of the American Statistical Association 95, n.º 449 (marzo de 2000): 319–22. http://dx.doi.org/10.1080/01621459.2000.10473933.

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13

Fischer, Christine. "Handbook of statistical genetics:". Human Genetics 110, n.º 3 (15 de febrero de 2002): 290. http://dx.doi.org/10.1007/s00439-001-0667-1.

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14

Weir, Bruce S. "The Summer Institute in Statistical Genetics". Genetics 212, n.º 4 (agosto de 2019): 955–57. http://dx.doi.org/10.1534/genetics.119.302506.

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15

Zhao, H., M. S. McPeek y T. P. Speed. "Statistical analysis of chromatid interference." Genetics 139, n.º 2 (1 de febrero de 1995): 1057–65. http://dx.doi.org/10.1093/genetics/139.2.1057.

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Abstract The nonrandom occurrence of crossovers along a single strand during meiosis can be caused by either chromatid interference, crossover interference or both. Although crossover interference has been consistently observed in almost all organisms since the time of the first linkage studies, chromatid interference has not been as thoroughly discussed in the literature, and the evidence provided for it is inconsistent. In this paper with virtually no restrictions on the nature of crossover interference, we describe the constraints that follow from the assumption of no chromatid interference for single spore data. These constraints are necessary consequences of the assumption of no chromatid interference, but their satisfaction is not sufficient to guarantee no chromatid interference. Models can be constructed in which chromatid interference clearly exists but is not detectable with single spore data. We then extend our analysis to cover tetrad data, which permits more powerful tests of no chromatid interference. We note that the traditional test of no chromatid interference based on tetrad data does not make full use of the information provided by the data, and we offer a statistical procedure for testing the no chromatid interference constraints that does make full use of the data. The procedure is then applied to data from several organisms. Although no strong evidence of chromatid interference is found, we do observe an excess of two-strand double recombinations, i.e., negative chromatid interference.

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16

Zhao, Hongyu y Terence P. Speed. "Statistical Analysis of Half-Tetrads". Genetics 150, n.º 1 (1 de septiembre de 1998): 473–85. http://dx.doi.org/10.1093/genetics/150.1.473.

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Abstract Half-tetrads, where two meiotic products from a single meiosis are recovered together, arise in different forms in a variety of organisms. Closely related to ordered tetrads, half-tetrads yield information on chromatid interference, chiasma interference, and centromere positions. In this article, for different half-tetrad types and different marker configurations, we derive the relations between multilocus half-tetrad probabilities and multilocus ordered tetrad probabilities. These relations are used to obtain equality and inequality constraints among multilocus half-tetrad probabilities that are imposed by the assumption of no chromatid interference. We illustrate how to apply these results to study chiasma interference and to map centromeres using multilocus half-tetrad data.

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17

Zhao, Hongyu y Terence P. Speed. "Statistical Analysis of Ordered Tetrads". Genetics 150, n.º 1 (1 de septiembre de 1998): 459–72. http://dx.doi.org/10.1093/genetics/150.1.459.

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Abstract Ordered tetrad data yield information on chromatid interference, chiasma interference, and centromere locations. In this article, we show that the assumption of no chromatid interference imposes certain constraints on multilocus ordered tetrad probabilities. Assuming no chromatid interference, these constraints can be used to order markers under general chiasma processes. We also derive multilocus tetrad probabilities under a class of chiasma interference models, the chi-square models. Finally, we compare centromere map functions under the chi-square models with map functions proposed in the literature. Results in this article can be applied to order genetic markers and map centromeres using multilocus ordered tetrad data.

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18

Edwards, A. W. F. "Statistical Methods for Evolutionary Trees". Genetics 183, n.º 1 (septiembre de 2009): 5–12. http://dx.doi.org/10.1534/genetics.109.107847.

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19

Sham, P. C. "Statistical methods in psychiatric genetics". Statistical Methods in Medical Research 7, n.º 3 (1 de marzo de 1998): 279–300. http://dx.doi.org/10.1191/096228098677382724.

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20

Sham, Pak C. "Statistical methods in psychiatric genetics". Statistical Methods in Medical Research 7, n.º 3 (junio de 1998): 279–300. http://dx.doi.org/10.1177/096228029800700305.

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21

GUILLOT, GILLES, RAPHA��L LEBLOIS, AUR��LIE COULON y ALAIN C. FRANTZ. "Statistical methods in spatial genetics". Molecular Ecology 18, n.º 23 (diciembre de 2009): 4734–56. http://dx.doi.org/10.1111/j.1365-294x.2009.04410.x.

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22

Fu, Y. X. y W. H. Li. "Statistical tests of neutrality of mutations." Genetics 133, n.º 3 (1 de marzo de 1993): 693–709. http://dx.doi.org/10.1093/genetics/133.3.693.

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Abstract Mutations in the genealogy of the sequences in a random sample from a population can be classified as external and internal. External mutations are mutations that occurred in the external branches and internal mutations are mutations that occurred in the internal branches of the genealogy. Under the assumption of selective neutrality, the expected number of external mutations is equal to theta = 4Ne mu, where Ne is the effective population size and mu is the rate of mutation per gene per generation. Interestingly, this expectation is independent of the sample size. The number of external mutations is likely to deviate from its neutral expectation when there is selection while the number of internal mutations is less affected by the presence of selection. Statistical properties of the numbers of external mutations and of internal mutations are studied and their relationships to two commonly used estimates of theta are derived. From these properties, several new statistical tests based on a random sample of DNA sequences from the population are developed for testing the hypothesis that all mutations at a locus are neutral.

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23

Schaid, D. J., X. Tong, B. Larrabee, R. B. Kennedy, G. A. Poland y J. P. Sinnwell. "Statistical Methods for Testing Genetic Pleiotropy". Genetics 204, n.º 2 (15 de agosto de 2016): 483–97. http://dx.doi.org/10.1534/genetics.116.189308.

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24

Guillot, Gilles, Arnaud Estoup, Frédéric Mortier y Jean François Cosson. "A Spatial Statistical Model for Landscape Genetics". Genetics 170, n.º 3 (1 de noviembre de 2004): 1261–80. http://dx.doi.org/10.1534/genetics.104.033803.

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25

Sen, Śaunak y Gary A. Churchill. "A Statistical Framework for Quantitative Trait Mapping". Genetics 159, n.º 1 (1 de septiembre de 2001): 371–87. http://dx.doi.org/10.1093/genetics/159.1.371.

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AbstractWe describe a general statistical framework for the genetic analysis of quantitative trait data in inbred line crosses. Our main result is based on the observation that, by conditioning on the unobserved QTL genotypes, the problem can be split into two statistically independent and manageable parts. The first part involves only the relationship between the QTL and the phenotype. The second part involves only the location of the QTL in the genome. We developed a simple Monte Carlo algorithm to implement Bayesian QTL analysis. This algorithm simulates multiple versions of complete genotype information on a genomewide grid of locations using information in the marker genotype data. Weights are assigned to the simulated genotypes to capture information in the phenotype data. The weighted complete genotypes are used to approximate quantities needed for statistical inference of QTL locations and effect sizes. One advantage of this approach is that only the weights are recomputed as the analyst considers different candidate models. This device allows the analyst to focus on modeling and model comparisons. The proposed framework can accommodate multiple interacting QTL, nonnormal and multivariate phenotypes, covariates, missing genotype data, and genotyping errors in any type of inbred line cross. A software tool implementing this procedure is available. We demonstrate our approach to QTL analysis using data from a mouse backcross population that is segregating multiple interacting QTL associated with salt-induced hypertension.

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26

Zeng, Yong, Hua Li, Nicole M. Schweppe, R. Scott Hawley y William D. Gilliland. "Statistical Analysis of Nondisjunction Assays in Drosophila". Genetics 186, n.º 2 (26 de julio de 2010): 505–13. http://dx.doi.org/10.1534/genetics.110.118778.

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27

Diao, Guoqing y Anand N. Vidyashankar. "Assessing Genome-Wide Statistical Significance for LargepSmallnProblems". Genetics 194, n.º 3 (11 de mayo de 2013): 781–83. http://dx.doi.org/10.1534/genetics.113.150896.

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28

Wang, Jiankang, Jose Crossa, Maarten van Ginkel y Suketoshi Taba. "Statistical Genetics and Simulation Models in Genetic Resource Conservation and Regeneration". Crop Science 44, n.º 6 (noviembre de 2004): 2246–53. http://dx.doi.org/10.2135/cropsci2004.2246.

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29

Palmgren, J. "Exponential family models and statistical genetics". Statistical Methods in Medical Research 9, n.º 1 (1 de febrero de 2000): 57–72. http://dx.doi.org/10.1191/096228000673446959.

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30

Palmgren, Juni. "Exponential family models and statistical genetics". Statistical Methods in Medical Research 9, n.º 1 (febrero de 2000): 57–72. http://dx.doi.org/10.1177/096228020000900107.

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31

Sisson, Scott. "Book Review: Handbook of statistical genetics". Statistical Methods in Medical Research 12, n.º 1 (febrero de 2003): 86–87. http://dx.doi.org/10.1177/096228020301200109.

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32

Artoisenet, Pierre y Laure-Anne Minsart. "Statistical genetics in traditionally cultivated crops". Journal of Theoretical Biology 360 (noviembre de 2014): 208–21. http://dx.doi.org/10.1016/j.jtbi.2014.06.028.

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33

Thompson, Wesley, Ole Andreassen y Yunpeng Wang. "Novel Statistical Approaches For Imaging Genetics". European Neuropsychopharmacology 29 (2019): S725—S726. http://dx.doi.org/10.1016/j.euroneuro.2017.06.044.

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34

Edwards, J. H. "Handbook of statistical genetics, 2nd edition". Journal of Genetics 85, n.º 1 (abril de 2006): 89–92. http://dx.doi.org/10.1007/bf02728977.

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35

Strug, Lisa J. "The evidential statistical paradigm in genetics". Genetic Epidemiology 42, n.º 7 (18 de agosto de 2018): 590–607. http://dx.doi.org/10.1002/gepi.22151.

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36

Auer, Paul L. y R. W. Doerge. "Statistical Design and Analysis of RNA Sequencing Data". Genetics 185, n.º 2 (3 de mayo de 2010): 405–16. http://dx.doi.org/10.1534/genetics.110.114983.

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37

Wolf, Jason y James M. Cheverud. "Detecting Maternal-Effect Loci by Statistical Cross-Fostering". Genetics 191, n.º 1 (29 de febrero de 2012): 261–77. http://dx.doi.org/10.1534/genetics.111.136440.

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38

Fu, Yun-Xin. "Statistical Methods for Analyzing Drosophila Germline Mutation Rates". Genetics 194, n.º 4 (1 de mayo de 2013): 927–36. http://dx.doi.org/10.1534/genetics.113.151571.

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39

Sun, Jingyi. "Application and Challenges of Statistical Methods in Biological Genetics". Highlights in Science, Engineering and Technology 40 (29 de marzo de 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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40

Greenman, Chris, Richard Wooster, P. Andrew Futreal, Michael R. Stratton y Douglas F. Easton. "Statistical Analysis of Pathogenicity of Somatic Mutations in Cancer". Genetics 173, n.º 4 (18 de junio de 2006): 2187–98. http://dx.doi.org/10.1534/genetics.105.044677.

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41

Zou, Fei, Brian S. Yandell y Jason P. Fine. "Rank-Based Statistical Methodologies for Quantitative Trait Locus Mapping". Genetics 165, n.º 3 (1 de noviembre de 2003): 1599–605. http://dx.doi.org/10.1093/genetics/165.3.1599.

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Abstract This article addresses the identification of genetic loci (QTL and elsewhere) that influence nonnormal quantitative traits with focus on experimental crosses. QTL mapping is typically based on the assumption that the traits follow normal distributions, which may not be true in practice. Model-free tests have been proposed. However, nonparametric estimation of genetic effects has not been studied. We propose an estimation procedure based on the linear rank test statistics. The properties of the new procedure are compared with those of traditional likelihood-based interval mapping and regression interval mapping via simulations and a real data example. The results indicate that the nonparametric method is a competitive alternative to the existing parametric methodologies.

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42

Massip, F., M. Sheinman, S. Schbath y P. F. Arndt. "Comparing the Statistical Fate of Paralogous and Orthologous Sequences". Genetics 204, n.º 2 (29 de julio de 2016): 475–82. http://dx.doi.org/10.1534/genetics.116.193912.

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43

Zhu, Yicheng, Teresa Neeman, Von Bing Yap y Gavin A. Huttley. "Statistical Methods for Identifying Sequence Motifs Affecting Point Mutations". Genetics 205, n.º 2 (14 de diciembre de 2016): 843–56. http://dx.doi.org/10.1534/genetics.116.195677.

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44

Ye, Shuyun, Rhonda Bacher, Mark P. Keller, Alan D. Attie y Christina Kendziorski. "Statistical Methods for Latent Class Quantitative Trait Loci Mapping". Genetics 206, n.º 3 (26 de mayo de 2017): 1309–17. http://dx.doi.org/10.1534/genetics.117.203885.

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45

Chen, Meng y Christina Kendziorski. "A Statistical Framework for Expression Quantitative Trait Loci Mapping". Genetics 177, n.º 2 (29 de julio de 2007): 761–71. http://dx.doi.org/10.1534/genetics.107.071407.

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46

Barton, N. H. y H. P. de Vladar. "Statistical Mechanics and the Evolution of Polygenic Quantitative Traits". Genetics 181, n.º 3 (15 de diciembre de 2008): 997–1011. http://dx.doi.org/10.1534/genetics.108.099309.

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47

Zeegers, M. P. "Statistical Methods in Genetic Epidemiology". Journal of Medical Genetics 41, n.º 12 (1 de diciembre de 2004): 958. http://dx.doi.org/10.1136/jmg.2004.021113.

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48

Wyszynski, Diego F. "Statistical Methods in Genetic Epidemiology." American Journal of Human Genetics 76, n.º 1 (enero de 2005): 190. http://dx.doi.org/10.1086/427114.

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49

Ott, Jurg y Helen Donis-Keller. "Statistical Methods in Genetic Mapping". Genomics 22, n.º 2 (julio de 1994): 496–97. http://dx.doi.org/10.1006/geno.1994.1421.

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50

Matise, Tara Cox, Helen Onis-Keller y Jurg Ott. "Statistical Methods in Genetic Mapping". Genomics 36, n.º 1 (agosto de 1996): 223–25. http://dx.doi.org/10.1006/geno.1996.0456.

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