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Pleiotropy, constraint, and modularity in the evolution of life histories: insights from genomic analyses. | LitMetric

Pleiotropy, constraint, and modularity in the evolution of life histories: insights from genomic analyses.

Ann N Y Acad Sci

Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, Maryland.

Published: February 2017

AI Article Synopsis

  • Multicellular organisms exhibit a wide range of life history strategies, yet these traits show significant genetic variation despite strong natural selection.
  • Understanding how life history traits evolve and maintain variation requires exploring the effects of specific alleles and their genetic interactions.
  • Recent genomic studies reveal new research methods that could enhance insights into life history evolution, with promising approaches including multidimensional selection experiments and genome-wide association analyses.

Article Abstract

Multicellular organisms display an enormous range of life history (LH) strategies and present an evolutionary conundrum; despite strong natural selection, LH traits are characterized by high levels of genetic variation. To understand the evolution of life histories and maintenance of this variation, the specific phenotypic effects of segregating alleles and the genetic networks in which they act need to be elucidated. In particular, the extent to which LH evolution is constrained by the pleiotropy of alleles contributing to LH variation is generally unknown. Here, we review recent empirical results that shed light on this question, with an emphasis on studies employing genomic analyses. While genome-scale analyses are increasingly practical and affordable, they face limitations of genetic resolution and statistical power. We describe new research approaches that we believe can produce new insights and evaluate their promise and applicability to different kinds of organisms. Two approaches seem particularly promising: experiments that manipulate selection in multiple dimensions and measure phenotypic and genomic response and analytical approaches that take into account genome-wide associations between markers and phenotypes, rather than applying a traditional marker-by-marker approach.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5318229PMC
http://dx.doi.org/10.1111/nyas.13256DOI Listing

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