<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mostafavi, Hakhamanesh</style></author><author><style face="normal" font="default" size="100%">Berisa, Tomaz</style></author><author><style face="normal" font="default" size="100%">Day, Felix R.</style></author><author><style face="normal" font="default" size="100%">Perry, John R. B.</style></author><author><style face="normal" font="default" size="100%">Przeworski, Molly</style></author><author><style face="normal" font="default" size="100%">Pickrell, Joseph K.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Identifying genetic variants that affect viability in large cohorts</style></title><secondary-title><style face="normal" font="default" size="100%">PLOS Biology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2017/09/05</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.2002458</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">15</style></volume><pages><style face="normal" font="default" size="100%">e2002458 - </style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;div class=&quot;page&quot; title=&quot;Page 1&quot;&gt;
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&lt;p&gt;&lt;span style=&quot;font-size: 10.000000pt; font-family: &#039;Helvetica&#039;&quot;&gt;A number of open questions in human evolutionary genetics would become tractable if we were able to directly measure evolutionary fitness. As a step towards this goal, we devel- oped a method to examine whether individual genetic variants, or sets of genetic variants, currently influence viability. The approach consists in testing whether the frequency of an allele varies across ages, accounting for variation in ancestry. We applied it to the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort and to the parents of par- ticipants in the UK Biobank. Across the genome, we found only a few common variants with large effects on age-specific mortality: tagging the &lt;/span&gt;&lt;span style=&quot;font-size: 10.000000pt; font-family: &#039;Helvetica&#039;; font-style: oblique&quot;&gt;APOE &lt;/span&gt;&lt;span style=&quot;font-size: 10.000000pt; font-family: &#039;ArialMT-Identity-H&#039;&quot;&gt;ε&lt;/span&gt;&lt;span style=&quot;font-size: 10.000000pt; font-family: &#039;Helvetica&#039;&quot;&gt;4 allele and near &lt;/span&gt;&lt;span style=&quot;font-size: 10.000000pt; font-family: &#039;Helvetica&#039;; font-style: oblique&quot;&gt;CHRNA3&lt;/span&gt;&lt;span style=&quot;font-size: 10.000000pt; font-family: &#039;Helvetica&#039;&quot;&gt;. These results suggest that when large, even late-onset effects are kept at low frequency by purifying selection. Testing viability effects of sets of genetic variants that jointly influence 1 of 42 traits, we detected a number of strong signals. In participants of the UK Biobank of Brit- ish ancestry, we found that variants that delay puberty timing are associated with a longer parental life span (&lt;/span&gt;&lt;span style=&quot;font-size: 10.000000pt; font-family: &#039;Helvetica&#039;; font-style: oblique&quot;&gt;P&lt;/span&gt;&lt;span style=&quot;font-size: 10.000000pt; font-family: &#039;Helvetica&#039;&quot;&gt;~6.2 &lt;/span&gt;&lt;span style=&quot;font-size: 10.000000pt; font-family: &#039;ArialMT-Identity-H&#039;&quot;&gt;× &lt;/span&gt;&lt;span style=&quot;font-size: 10.000000pt; font-family: &#039;Helvetica&#039;&quot;&gt;10&lt;/span&gt;&lt;span style=&quot;font-size: 7.000000pt; font-family: &#039;ArialMT-Identity-H&#039;; vertical-align: 4.000000pt&quot;&gt;−&lt;/span&gt;&lt;span style=&quot;font-size: 7.000000pt; font-family: &#039;Helvetica&#039;; vertical-align: 4.000000pt&quot;&gt;6 &lt;/span&gt;&lt;span style=&quot;font-size: 10.000000pt; font-family: &#039;Helvetica&#039;&quot;&gt;for fathers and &lt;/span&gt;&lt;span style=&quot;font-size: 10.000000pt; font-family: &#039;Helvetica&#039;; font-style: oblique&quot;&gt;P&lt;/span&gt;&lt;span style=&quot;font-size: 10.000000pt; font-family: &#039;Helvetica&#039;&quot;&gt;~2.0 &lt;/span&gt;&lt;span style=&quot;font-size: 10.000000pt; font-family: &#039;ArialMT-Identity-H&#039;&quot;&gt;× &lt;/span&gt;&lt;span style=&quot;font-size: 10.000000pt; font-family: &#039;Helvetica&#039;&quot;&gt;10&lt;/span&gt;&lt;span style=&quot;font-size: 7.000000pt; font-family: &#039;ArialMT-Identity-H&#039;; vertical-align: 4.000000pt&quot;&gt;−&lt;/span&gt;&lt;span style=&quot;font-size: 7.000000pt; font-family: &#039;Helvetica&#039;; vertical-align: 4.000000pt&quot;&gt;3 &lt;/span&gt;&lt;span style=&quot;font-size: 10.000000pt; font-family: &#039;Helvetica&#039;&quot;&gt;for mothers), consistent with epidemiological studies. Similarly, variants associated with later age at first birth are associ- ated with a longer maternal life span (&lt;/span&gt;&lt;span style=&quot;font-size: 10.000000pt; font-family: &#039;Helvetica&#039;; font-style: oblique&quot;&gt;P&lt;/span&gt;&lt;span style=&quot;font-size: 10.000000pt; font-family: &#039;Helvetica&#039;&quot;&gt;~1.4 &lt;/span&gt;&lt;span style=&quot;font-size: 10.000000pt; font-family: &#039;ArialMT-Identity-H&#039;&quot;&gt;× &lt;/span&gt;&lt;span style=&quot;font-size: 10.000000pt; font-family: &#039;Helvetica&#039;&quot;&gt;10&lt;/span&gt;&lt;span style=&quot;font-size: 7.000000pt; font-family: &#039;ArialMT-Identity-H&#039;; vertical-align: 4.000000pt&quot;&gt;−&lt;/span&gt;&lt;span style=&quot;font-size: 7.000000pt; font-family: &#039;Helvetica&#039;; vertical-align: 4.000000pt&quot;&gt;3&lt;/span&gt;&lt;span style=&quot;font-size: 10.000000pt; font-family: &#039;Helvetica&#039;&quot;&gt;). Signals are also observed for variants influencing cholesterol levels, risk of coronary artery disease (CAD), body mass index, as well as risk of asthma. These signals exhibit consistent effects in the GERA cohort and among participants of the UK Biobank of non-British ancestry. We also found marked differ- ences between males and females, most notably at the &lt;/span&gt;&lt;span style=&quot;font-size: 10.000000pt; font-family: &#039;Helvetica&#039;; font-style: oblique&quot;&gt;CHRNA3 &lt;/span&gt;&lt;span style=&quot;font-size: 10.000000pt; font-family: &#039;Helvetica&#039;&quot;&gt;locus, and variants associ- ated with risk of CAD and cholesterol levels. Beyond our findings, the analysis serves as a proof of principle for how upcoming biomedical data sets can be used to learn about selec- tion effects in contemporary humans.&amp;nbsp;&lt;/span&gt;&lt;/p&gt;
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