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      Recent natural selection causes adaptive evolution of an avian polygenic trait

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          Computational Many-Particle Physics

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            The genetics of human adaptation: hard sweeps, soft sweeps, and polygenic adaptation.

            There has long been interest in understanding the genetic basis of human adaptation. To what extent are phenotypic differences among human populations driven by natural selection? With the recent arrival of large genome-wide data sets on human variation, there is now unprecedented opportunity for progress on this type of question. Several lines of evidence argue for an important role of positive selection in shaping human variation and differences among populations. These include studies of comparative morphology and physiology, as well as population genetic studies of candidate loci and genome-wide data. However, the data also suggest that it is unusual for strong selection to drive new mutations rapidly to fixation in particular populations (the 'hard sweep' model). We argue, instead, for alternatives to the hard sweep model: in particular, polygenic adaptation could allow rapid adaptation while not producing classical signatures of selective sweeps. We close by discussing some of the likely opportunities for progress in the field. Copyright 2010 Elsevier Ltd. All rights reserved.
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              Improving accuracy of genomic predictions within and between dairy cattle breeds with imputed high-density single nucleotide polymorphism panels.

              Achieving accurate genomic estimated breeding values for dairy cattle requires a very large reference population of genotyped and phenotyped individuals. Assembling such reference populations has been achieved for breeds such as Holstein, but is challenging for breeds with fewer individuals. An alternative is to use a multi-breed reference population, such that smaller breeds gain some advantage in accuracy of genomic estimated breeding values (GEBV) from information from larger breeds. However, this requires that marker-quantitative trait loci associations persist across breeds. Here, we assessed the gain in accuracy of GEBV in Jersey cattle as a result of using a combined Holstein and Jersey reference population, with either 39,745 or 624,213 single nucleotide polymorphism (SNP) markers. The surrogate used for accuracy was the correlation of GEBV with daughter trait deviations in a validation population. Two methods were used to predict breeding values, either a genomic BLUP (GBLUP_mod), or a new method, BayesR, which used a mixture of normal distributions as the prior for SNP effects, including one distribution that set SNP effects to zero. The GBLUP_mod method scaled both the genomic relationship matrix and the additive relationship matrix to a base at the time the breeds diverged, and regressed the genomic relationship matrix to account for sampling errors in estimating relationship coefficients due to a finite number of markers, before combining the 2 matrices. Although these modifications did result in less biased breeding values for Jerseys compared with an unmodified genomic relationship matrix, BayesR gave the highest accuracies of GEBV for the 3 traits investigated (milk yield, fat yield, and protein yield), with an average increase in accuracy compared with GBLUP_mod across the 3 traits of 0.05 for both Jerseys and Holsteins. The advantage was limited for either Jerseys or Holsteins in using 624,213 SNP rather than 39,745 SNP (0.01 for Holsteins and 0.03 for Jerseys, averaged across traits). Even this limited and nonsignificant advantage was only observed when BayesR was used. An alternative panel, which extracted the SNP in the transcribed part of the bovine genome from the 624,213 SNP panel (to give 58,532 SNP), performed better, with an increase in accuracy of 0.03 for Jerseys across traits. This panel captures much of the increased genomic content of the 624,213 SNP panel, with the advantage of a greatly reduced number of SNP effects to estimate. Taken together, using this panel, a combined breed reference and using BayesR rather than GBLUP_mod increased the accuracy of GEBV in Jerseys from 0.43 to 0.52, averaged across the 3 traits. Copyright © 2012 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.
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                Author and article information

                Journal
                Science
                Science
                American Association for the Advancement of Science (AAAS)
                0036-8075
                1095-9203
                October 19 2017
                October 19 2017
                : 358
                : 6361
                : 365-368
                Article
                10.1126/science.aal3298
                29051380
                © 2017

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