Literature DB >> 18663587

Analysis and implications of mutational variation.

Peter D Keightley1, Daniel L Halligan.   

Abstract

Variation from new mutations is important for several questions in quantitative genetics. Key parameters are the genomic mutation rate and the distribution of effects of mutations (DEM), which determine the amount of new quantitative variation that arises per generation from mutation (V(M)). Here, we review methods and empirical results concerning mutation accumulation (MA) experiments that have shed light on properties of mutations affecting quantitative traits. Surprisingly, most data on fitness traits from laboratory assays of MA lines indicate that the DEM is platykurtic in form (i.e., substantially less leptokurtic than an exponential distribution), and imply that most variation is produced by mutations of moderate to large effect. This finding contrasts with results from MA or mutagenesis experiments in which mutational changes to the DNA can be assayed directly, which imply that the vast majority of mutations have very small phenotypic effects, and that the distribution has a leptokurtic form. We compare these findings with recent approaches that attempt to infer the DEM for fitness based on comparing the frequency spectra of segregating nucleotide polymorphisms at putatively neutral and selected sites in population samples. When applied to data for humans and Drosophila, these analyses also indicate that the DEM is strongly leptokurtic. However, by combining the resultant estimates of parameters of the DEM with estimates of the mutation rate per nucleotide, the predicted V(M) for fitness is only a tiny fraction of V(M) observed in MA experiments. This discrepancy can be explained if we postulate that a few deleterious mutations of large effect contribute most of the mutational variation observed in MA experiments and that such mutations segregate at very low frequencies in natural populations, and effectively are never seen in population samples.

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Year:  2008        PMID: 18663587     DOI: 10.1007/s10709-008-9304-4

Source DB:  PubMed          Journal:  Genetica        ISSN: 0016-6707            Impact factor:   1.082


  68 in total

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Authors:  L L Vassilieva; A M Hook; M Lynch
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2.  Estimating the distribution of fitness effects from DNA sequence data: implications for the molecular clock.

Authors:  Gwenaël Piganeau; Adam Eyre-Walker
Journal:  Proc Natl Acad Sci U S A       Date:  2003-08-18       Impact factor: 11.205

3.  Comparing analysis methods for mutation-accumulation data.

Authors:  Peter D Keightley
Journal:  Genetics       Date:  2004-05       Impact factor: 4.562

4.  Increase of the spontaneous mutation rate in a long-term experiment with Drosophila melanogaster.

Authors:  Victoria Avila; David Chavarrías; Enrique Sánchez; Antonio Manrique; Carlos López-Fanjul; Aurora García-Dorado
Journal:  Genetics       Date:  2006-03-17       Impact factor: 4.562

5.  Minimum distance estimation of mutational parameters for quantitative traits.

Authors:  A García-Dorado; J M Marín
Journal:  Biometrics       Date:  1998-09       Impact factor: 2.571

6.  Predictions of response to artificial selection from new mutations.

Authors:  W G Hill
Journal:  Genet Res       Date:  1982-12       Impact factor: 1.588

7.  Multigeneration maximum-likelihood analysis applied to mutation-accumulation experiments in Caenorhabditis elegans.

Authors:  P D Keightley; T M Bataillon
Journal:  Genetics       Date:  2000-03       Impact factor: 4.562

8.  Adaptive protein evolution in Drosophila.

Authors:  Nick G C Smith; Adam Eyre-Walker
Journal:  Nature       Date:  2002-02-28       Impact factor: 49.962

9.  The distribution of mutation effects on viability in Drosophila melanogaster.

Authors:  P D Keightley
Journal:  Genetics       Date:  1994-12       Impact factor: 4.562

10.  Joint inference of the distribution of fitness effects of deleterious mutations and population demography based on nucleotide polymorphism frequencies.

Authors:  Peter D Keightley; Adam Eyre-Walker
Journal:  Genetics       Date:  2007-12       Impact factor: 4.562

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  24 in total

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Authors:  William G Hill; Xu-Sheng Zhang
Journal:  Genetics       Date:  2012-01-03       Impact factor: 4.562

2.  Genetic linkage and natural selection.

Authors:  N H Barton
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2010-08-27       Impact factor: 6.237

Review 3.  Measurements of spontaneous rates of mutations in the recent past and the near future.

Authors:  Fyodor A Kondrashov; Alexey S Kondrashov
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2010-04-27       Impact factor: 6.237

4.  The population genetics of mutations: good, bad and indifferent.

Authors:  Laurence Loewe; William G Hill
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2010-04-27       Impact factor: 6.237

Review 5.  The genetics of inbreeding depression.

Authors:  Deborah Charlesworth; John H Willis
Journal:  Nat Rev Genet       Date:  2009-11       Impact factor: 53.242

Review 6.  Causes of natural variation in fitness: evidence from studies of Drosophila populations.

Authors:  Brian Charlesworth
Journal:  Proc Natl Acad Sci U S A       Date:  2015-01-08       Impact factor: 11.205

7.  Spatial Vulnerabilities of the Escherichia coli Genome to Spontaneous Mutations Revealed with Improved Duplex Sequencing.

Authors:  Xiaolong Zhang; Xuehong Zhang; Xia Zhang; Yuwei Liao; Luyao Song; Qingzheng Zhang; Peiying Li; Jichao Tian; Yanyan Shao; Aisha Mohammed Ai-Dherasi; Yulong Li; Ruimei Liu; Tao Chen; Xiaodi Deng; Yu Zhang; Dekang Lv; Jie Zhao; Jun Chen; Zhiguang Li
Journal:  Genetics       Date:  2018-08-03       Impact factor: 4.562

8.  The relation between the genetic architecture of quantitative traits and long-term genetic response.

Authors:  Rostam Abdollahi-Arpanahi; Abbas Pakdel; Ardeshir Nejati-Javaremi; Mohammad Moradi Shahrbabak; Farhad Ghafouri-Kesbi
Journal:  J Appl Genet       Date:  2014-03-27       Impact factor: 3.240

9.  The effects of demography and long-term selection on the accuracy of genomic prediction with sequence data.

Authors:  Iona M MacLeod; Ben J Hayes; Michael E Goddard
Journal:  Genetics       Date:  2014-09-18       Impact factor: 4.562

10.  Understanding and using quantitative genetic variation.

Authors:  William G Hill
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2010-01-12       Impact factor: 6.237

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