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Books on the topic 'Population genetics analyses'

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Published: 10 December 2022
Last updated: 29 January 2023

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1

P, Altukhov I͡U. Population genetics--diversity and stability. Chur, Switzerland: Harwood Academic Publishers, 1990.

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2

A, Zhivotovskiĭ L., ed. Geneticheskie prot͡sessy v populi͡at͡sii͡akh. 2nd ed. Moskva: "Nauka", 1989.

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3

Pedigree analysis in human genetics. Baltimore: Johns Hopkins University Press, 1986.

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4

Genetic data analysis: Methods for discrete population genetic data. Sunderland, Mass: Sinauer Associates, 1990.

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5

Weir, B. S. Genetic data analysis II: Methods for discrete population genetic data. Sunderland, Mass: Sinauer Associates, 1996.

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6

R, Baverstock P., and Adams M. 1954-, eds. Allozyme electrophoresis: A handbook for animal systematicsand population studies. San Diego: Academic Press, 1990.

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7

Richardson, B. J. Allozyme electrophoresis: A handbook for animal systematics and population studies. San Diego: Academic Press, 1990.

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8

Käär, Pekka. Evolution of human life cycle: An analysis on historical human populations. Turku: Turun Yliopisto, 2001.

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9

Bherer, Claude. Caractérisation du pool génique de Lanaudière: Analyse démogénétique et étude épidemiogénétique de la névrite héréditaire NHSA2. Chicoutimi, Québec: GRIR, Université du Québec à Chicoutimi, 2008.

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10

Université du Québec à Chicoutimi. Groupe de recherche et d'intervention régionales., ed. Caractérisation du pool génique de Lanaudière: Analyse démogénétique et étude épidemiogénétique de la névrite héréditaire NHSA2. Chicoutimi, Québec: GRIR, Université du Québec à Chicoutimi, 2008.

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11

Bherer, Claude. Caractérisation du pool génique de Lanaudière: Analyse démogénétique et étude épidemiogénétique de la névrite héréditaire NHSA2. Chicoutimi, Québec: GRIR, Université du Québec à Chicoutimi, 2008.

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12

Bherer, Claude. Caractérisation du pool génique de Lanaudière: Analyse démogénétique et étude épidemiogénétique de la névrite héréditaire NHSA2. Chicoutimi, Québec: GRIR, Université du Québec à Chicoutimi, 2008.

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13

Grewe, Peter M. An assessment of bigeye (Thunnus obesus) population structure in the Pacific Ocean, based on mitochondrial DNA and DNA microsatellite analysis. [Honolulu, Hawaii: University of Hawaii, Joint Institute for Marine and Atmospheric Research, 1998.

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14

Fedorova, S. A. Geneticheskie portrety narodov Respubliki Sakha, I︠A︡kutii︠a︡: Analiz liniĭ mitokhondrialʹnoĭ DNK i Y-khromosomy. I︠A︡kutsk: I︠A︡NT︠S︡ SO RAN, 2008.

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15

Zhang, Qingling. Complexity, Analysis and Control of Singular Biological Systems. London: Springer London, 2012.

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16

Hickerson, Michael J. Post-glacial population history and genetic structure of the northern clingfish (Gobbiesox maeandricus), revealed from mtDNA analysis. [Berlin: Springer-Verlag, 2001.

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17

Workshop, on the Analysis of Genetic Data to Address Problems of Stock Identity as Related to Management of Marine Mammals (1994 Southwest Fisheries Science Center La Jolla Calif ). Molecular genetics of marine mammals: Incorporating the proceedings of a Workshop on the Analysis of Genetic Data to Address Problems of Stock Identity as Related to Management of Marine Mammals. Lawrence, KS: Society for Marine Mammalogy, 1997.

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18

Microevolutionary patterns in Aboriginal Australia: A gradient analysis of clines. New York: Oxford University Press, 1993.

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19

Berejikian, Barry A. Review of relative fitness of hatchery and natural salmon. Seattle, Wash: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Marine Fisheries Service, Northwest Fisheries Science Center, 2004.

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20

Shostak, Stanley. Evolution of sameness and difference: Perspectives on the Human Genome Project. Amsterdam: Harwood Academic Publishers, 1999.

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21

Antonovich, Anton. Application of Bayesian mixed stock analysis for detection of small contributions in fish mixtures: Report to the Gene Conservation Laboratory, Alaska Department of Fish and Game. Anchorage, Alaska: Alaska Dept. of Fish and Game, Division of Commercial Fisheries, 2003.

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22

The statistics of natural selection on animal populations. London: Chapman and Hall, 1985.

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23

Jamal, Najim, and SpringerLink (Online service), eds. Stochastic Analysis and Related Topics: In Honour of Ali Süleyman Üstünel, Paris, June 2010. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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24

Unnatural selection: Choosing boys over girls, and the consequences of a world full of men. New York: PublicAffairs, 2011.

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25

author, Sarich Marco 1985, ed. Metastability and Markov state models in molecular dynamics: Modeling, analysis, algorithmic approaches. Providence, Rhode Island: American Mathematical Society, 2013.

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26

Herrmann, Samuel. Stochastic resonance: A mathematical approach in the small noise limit. Providence, Rhode Island: American Mathematical Society, 2014.

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27

Beaumont, Mark. Practical Population Genetics: Genetic Data Analysis for the Evolutionary Biologist. Imperial College Press, 2018.

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28

Population Genetics and Belonging: A Cultural Analysis of Genetic Ancestry. Palgrave Macmillan, 2017.

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29

Oikkonen, Venla. Population Genetics and Belonging: A Cultural Analysis of Genetic Ancestry. Springer, 2018.

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30

Marko, Peter B., and Michael W. Hart, eds. Genetic Analysis of Larval Dispersal, Gene Flow, and Connectivity. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198786962.003.0012.

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Does the dispersal of planktonic larvae promote strong connections between marine populations? Here we describe some of the most commonly used population- and individual-based genetic methods that have enhanced our understanding of larval dispersal and marine connectivity. Both approaches have strengths and weaknesses. Choosing between them depends on whether researchers want to know about average effective rates of connectivity over long timescales (over hundreds to thousands of generations) or recent patterns of connectivity on shorter timescales (one to two generations). The use of both approaches has improved our understanding of larval dispersal distances, the relationship between realized dispersal (from genetics) and dispersal potential (from planktonic larval duration), and the crucial distinction between genetic and demographic connectivity. Although rarely used together, combining population- and individual-based inferences from genetic data will likely further enrich our understanding of the scope and scale of larval dispersal in marine systems.
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31

Láruson, Áki Jarl, and Floyd Allan Reed. Population Genetics with R. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198829539.001.0001.

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Population genetics is an inherently quantitative discipline. Because the focus of population genetics studies is usually on abstract concepts like the frequencies of genetic variants over time, it can at first glance be difficult to conceptualize and appropriately visualize. As more and more quantitative models and methods have become established in the discipline, it has become necessary for people just entering the field to quickly develop a good understanding of the many layers of complex approaches, so as to correctly interpret even basic results. An unfortunate side effect of the widespread implementation of ready-to-use quantitative software packages is that some facets of analysis can become rote, which at best might lead to implementation without the full understanding of the user and at worst, inappropriate application leading to misguided conclusions. In this book a “learning by doing” approach is employed to encourage readers to begin developing an intuitive understanding of population genetics concepts. The analytical software R, which has increasingly been the program of choice for early exposure to basic statistical programming, is freely available online, has cross-platform compatibility (Windows, Mac, and Linux all support distributions of R), and offers the potential for hands-on implementation by the students, in addition to using pre-packaged functions.
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32

O'Rourke, Dennis, Justin Tackney, Joan Coltrain, and Jennifer Raff. Ancient DNA and Stable Isotopes. Edited by Max Friesen and Owen Mason. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199766956.013.3.

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Genetic diversity in modern Arctic communities provides a baseline from which to assess population history. This is augmented by documenting patterns of genetic variation in prehistoric populations using ancient DNA methods, and inferring dietary resource information and adaptive strategies derived from stable isotope analyses. This chapter uses this multidisciplinary approach to examine population history and colonization events in the Aleutians of South Alaska, and the origin and population history of Paleoeskimo and Neoeskimo populations of the North American Arctic. The power to identify past demographic events relies on knowledge of both genetic and isotopic signatures of demographic events, and on acquisition of securely dated and well provenienced samples for analysis.
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33

Population structure of the Atlantic bottlenose dolphin as determined by restriction endonuclease analysis of mitochondrial dna. [Washington, D.C.?: Marine Mammal Commission, 1992.

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34

Cutter, Asher D. A Primer of Molecular Population Genetics. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198838944.001.0001.

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The study of molecular population genetics seeks to understand the micro-evolutionary principles underlying DNA sequence variation and change. It addresses such questions as: Why do individuals differ as much as they do in their DNA sequences? What are the genomic signatures of adaptations? How often does natural selection dictate changes to DNA and accumulate as differences between species? How does the ebb and flow in the abundance of individuals over time get marked onto chromosomes to record genetic history? The concepts used to answer such questions also apply to analysis of personal genomics, genome-wide association studies, phylogenetics, landscape and conservation genetics, forensics, molecular anthropology, and selection scans. This Primer of Molecular Population Genetics introduces the bare essentials of the theory and practice of evolutionary analysis through the lens of DNA sequence change in populations. Intended as an introductory text for upper-level undergraduates and junior graduate students, this Primer also provides an accessible entryway for scientists from other areas of biology to appreciate the ideas and practice of molecular population genetics. With the revolutionary advances in genomic data acquisition, understanding molecular population genetics is now a fundamental requirement for today’s life scientists.
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35

Genetic Data Analysis 2: Methods for Discrete Population Genetic Data. 2nd ed. Sinauer Associates, 1996.

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36

Rus, Hoelzel A., ed. Molecular genetic analysis of populations: A practical approach. Oxford: IRL Press at Oxford University Press, 1992.

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37

Rus, Hoelzel A., ed. Molecular genetic analysis of populations: A practical approach. 2nd ed. Oxford: IRL Pess at Oxford University Press, 1998.

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38

Hoelzel, A. R. Molecular Genetic Analysis of Populations: A Practical Approach. Oxford University Press, 1998.

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39

Hoelzel, A. R. Molecular Genetic Analysis of Populations: A Practical Approach (Practical Approach Series). Oxford University Press, USA, 1998.

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40

Hoelzel, A. R. Molecular Genetic Analysis of Populations: A Practical Approach (Practical Approach Series). Oxford University Press, USA, 1998.

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41

Xu, Jianping. Mating and population genetic analyses of the basidiomycete fungus, Agaricus bisporus. 1997.

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42

Stram, Daniel O. Design, Analysis, and Interpretation of Genome-Wide Association Scans. Springer, 2013.

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43

Data Production And Analysis In Population Genomics Methods And Protocols. Humana Press, 2012.

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44

Frankham, Richard, Jonathan D. Ballou, Katherine Ralls, Mark D. B. Eldridge, Michele R. Dudash, Charles B. Fenster, Robert C. Lacy, and Paul Sunnucks. Determining the number and location of genetically differentiated population fragments. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198783398.003.0010.

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The number and geographic location of genetically differentiated populations must be identified to determine if fragmented populations require genetic management. Clustering of related genotypes to geographic locations (landscape genetic analyses) is used to determine the number of populations and their boundaries, with the simplest analyses relying on random mating within, but not across populations. Evidence of genetic differentiation among populations indicates either that they have drifted apart (and are likely inbred) and/or that the populations are adaptively differentiated. The current response when populations are genetically differentiated is usually to recommend separate management, but this is often ill-advised. A paradigm shift is needed where evidence of genetic differentiation among populations is followed by an assessment of whether populations are suffering genetic erosion, whether there are other populations to which they could be crossed, and whether the crosses would be beneficial, or harmful.
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45

Stram, Daniel O. Design, Analysis, and Interpretation of Genome-Wide Association Scans. Springer, 2016.

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46

Stram, Daniel O. Design, Analysis, and Interpretation of Genome-Wide Association Scans. Springer, 2013.

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47

Stram, Daniel O. Design, Analysis, and Interpretation of Genome-Wide Association Scans. Springer London, Limited, 2013.

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48

MacGregor, Alex, Ana Valdes, and Frances M. K. Williams. Genetics of osteoarthritis. Oxford University Press, 2013. http://dx.doi.org/10.1093/med/9780199642489.003.0044.

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In this chapter we outline the approaches which have been adopted to identify genetic variants predisposing to osteoarthritis (OA), a condition long recognized as having a heritable component. Such routes to their identification include examining mendelian traits in which OA is a feature, candidate gene studies based on knowledge of OA pathobiology, linkage analysis in related individuals, and, more recently, genome-wide association studies in large samples of unrelated individuals. It is increasingly evident that the main symptom deriving from OA—notably joint pain—also has a genetic basis but this is differs from that underlying OA. Variants convincingly shown to predispose to OA lie in the GDF5 and MCF2L genes and in the chr7 cluster mapping to the COG5 gene, in addition to the ASPN gene in Asian populations. Those associated with pain in OA include TRPV1 and PACE4. Epigenetic influences are also being explored in both the pathogenesis of OA and the variation of pain processing.
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49

Hoelzel, A. Rus. Molecular Genetic Analysis of Populations: A Practical Approach (Practical Approach Series). Oxford University Press, USA, 1992.

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50

Hoelzel, A. Rus. Molecular Genetic Analysis of Populations: A Practical Approach (Practical Approach Series). Oxford University Press, USA, 1992.

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