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      Genetic variation in adaptability and pleiotropy in budding yeast

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          Abstract

          Evolution can favor organisms that are more adaptable, provided that genetic variation in adaptability exists. Here, we quantify this variation among 230 offspring of a cross between diverged yeast strains. We measure the adaptability of each offspring genotype, defined as its average rate of adaptation in a specific environmental condition, and analyze the heritability, predictability, and genetic basis of this trait. We find that initial genotype strongly affects adaptability and can alter the genetic basis of future evolution. Initial genotype also affects the pleiotropic consequences of adaptation for fitness in a different environment. This genetic variation in adaptability and pleiotropy is largely determined by initial fitness, according to a rule of declining adaptability with increasing initial fitness, but several individual QTLs also have a significant idiosyncratic role. Our results demonstrate that both adaptability and pleiotropy are complex traits, with extensive heritable differences arising from naturally occurring variation.

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          Most cited references60

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          The genetic landscape of a cell.

          A genome-scale genetic interaction map was constructed by examining 5.4 million gene-gene pairs for synthetic genetic interactions, generating quantitative genetic interaction profiles for approximately 75% of all genes in the budding yeast, Saccharomyces cerevisiae. A network based on genetic interaction profiles reveals a functional map of the cell in which genes of similar biological processes cluster together in coherent subsets, and highly correlated profiles delineate specific pathways to define gene function. The global network identifies functional cross-connections between all bioprocesses, mapping a cellular wiring diagram of pleiotropy. Genetic interaction degree correlated with a number of different gene attributes, which may be informative about genetic network hubs in other organisms. We also demonstrate that extensive and unbiased mapping of the genetic landscape provides a key for interpretation of chemical-genetic interactions and drug target identification.
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            Identification of mutations in laboratory-evolved microbes from next-generation sequencing data using breseq.

            Next-generation DNA sequencing (NGS) can be used to reconstruct eco-evolutionary population dynamics and to identify the genetic basis of adaptation in laboratory evolution experiments. Here, we describe how to run the open-source breseq computational pipeline to identify and annotate genetic differences found in whole-genome and whole-population NGS data from haploid microbes where a high-quality reference genome is available. These methods can also be used to analyze mutants isolated in genetic screens and to detect unintended mutations that may occur during strain construction and genome editing.
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              The mystery of missing heritability: Genetic interactions create phantom heritability.

              Human genetics has been haunted by the mystery of "missing heritability" of common traits. Although studies have discovered >1,200 variants associated with common diseases and traits, these variants typically appear to explain only a minority of the heritability. The proportion of heritability explained by a set of variants is the ratio of (i) the heritability due to these variants (numerator), estimated directly from their observed effects, to (ii) the total heritability (denominator), inferred indirectly from population data. The prevailing view has been that the explanation for missing heritability lies in the numerator--that is, in as-yet undiscovered variants. While many variants surely remain to be found, we show here that a substantial portion of missing heritability could arise from overestimation of the denominator, creating "phantom heritability." Specifically, (i) estimates of total heritability implicitly assume the trait involves no genetic interactions (epistasis) among loci; (ii) this assumption is not justified, because models with interactions are also consistent with observable data; and (iii) under such models, the total heritability may be much smaller and thus the proportion of heritability explained much larger. For example, 80% of the currently missing heritability for Crohn's disease could be due to genetic interactions, if the disease involves interaction among three pathways. In short, missing heritability need not directly correspond to missing variants, because current estimates of total heritability may be significantly inflated by genetic interactions. Finally, we describe a method for estimating heritability from isolated populations that is not inflated by genetic interactions.
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                Author and article information

                Contributors
                Role: Reviewing Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                17 August 2017
                2017
                : 6
                : e27167
                Affiliations
                [1 ]deptDepartment of Organismic and Evolutionary Biology Harvard University CambridgeUnited States
                [2 ]deptDepartment of Physics Harvard University CambridgeUnited States
                [3 ]deptFAS Center for Systems Biology Harvard University CambridgeUnited States
                [4 ]deptSection of Ecology, Behavior and Evolution, Division of Biological Sciences University of California, San Diego San DiegoUnited States
                [5 ]deptDepartment of Human Genetics University of California, Los Angeles Los AngelesUnited States
                University of Michigan United States
                University of Michigan United States
                Author notes
                [†]

                These authors contributed equally to this work.

                Author information
                http://orcid.org/0000-0003-3793-8839
                http://orcid.org/0000-0001-9128-8705
                http://orcid.org/0000-0002-8065-3057
                http://orcid.org/0000-0002-9581-1150
                Article
                27167
                10.7554/eLife.27167
                5580887
                28826486
                17fd478b-15ba-4fbd-b9bc-ef0173dd8312
                © 2017, Jerison et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 27 March 2017
                : 14 August 2017
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R01GM102308
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000893, Simons Foundation;
                Award ID: 376196
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: PHY 1313638
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000011, Howard Hughes Medical Institute;
                Award ID: Investigator
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R01GM104239
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: Graduate Research Fellowship
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000861, Burroughs Wellcome Fund;
                Award ID: Career Award at the Scientific Interface
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Genomics and Evolutionary Biology
                Custom metadata
                Substantial heritable genetic variation in adaptability and the pleiotropic consequences of adaptation exists in budding yeast, and can be explained by a combination of fitness and specific segregating alleles.

                Life sciences
                adaptability,pleiotropy,experimental evolution,s. cerevisiae
                Life sciences
                adaptability, pleiotropy, experimental evolution, s. cerevisiae

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