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      Paternal age in rhesus macaques is positively associated with germline mutation accumulation but not with measures of offspring sociability

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          Abstract

          Mutation is the ultimate source of all genetic novelty and the cause of heritable genetic disorders. Mutational burden has been linked to complex disease, including neurodevelopmental disorders such as schizophrenia and autism. The rate of mutation is a fundamental genomic parameter and direct estimates of this parameter have been enabled by accurate comparisons of whole-genome sequences between parents and offspring. Studies in humans have revealed that the paternal age at conception explains most of the variation in mutation rate: Each additional year of paternal age in humans leads to approximately 1.5 additional inherited mutations. Here, we present an estimate of the de novo mutation rate in the rhesus macaque ( Macaca mulatta) using whole-genome sequence data from 32 individuals in four large pedigrees. We estimated an average mutation rate of 0.58 × 10 −8 per base pair per generation (at an average parental age of 7.5 yr), much lower than found in direct estimates from great apes. As in humans, older macaque fathers transmit more mutations to their offspring, increasing the per generation mutation rate by 4.27 × 10 −10 per base pair per year. We found that the rate of mutation accumulation after puberty is similar between macaques and humans, but that a smaller number of mutations accumulate before puberty in macaques. We additionally investigated the role of paternal age on offspring sociability, a proxy for normal neurodevelopment, by studying 203 male macaques in large social groups.

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          From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline.

          This unit describes how to use BWA and the Genome Analysis Toolkit (GATK) to map genome sequencing data to a reference and produce high-quality variant calls that can be used in downstream analyses. The complete workflow includes the core NGS data processing steps that are necessary to make the raw data suitable for analysis by the GATK, as well as the key methods involved in variant discovery using the GATK.
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            Observational Study of Behavior: Sampling Methods

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              High-throughput genotyping by whole-genome resequencing.

              The next-generation sequencing technology coupled with the growing number of genome sequences opens the opportunity to redesign genotyping strategies for more effective genetic mapping and genome analysis. We have developed a high-throughput method for genotyping recombinant populations utilizing whole-genome resequencing data generated by the Illumina Genome Analyzer. A sliding window approach is designed to collectively examine genome-wide single nucleotide polymorphisms for genotype calling and recombination breakpoint determination. Using this method, we constructed a genetic map for 150 rice recombinant inbred lines with an expected genotype calling accuracy of 99.94% and a resolution of recombination breakpoints within an average of 40 kb. In comparison to the genetic map constructed with 287 PCR-based markers for the rice population, the sequencing-based method was approximately 20x faster in data collection and 35x more precise in recombination breakpoint determination. Using the sequencing-based genetic map, we located a quantitative trait locus of large effect on plant height in a 100-kb region containing the rice "green revolution" gene. Through computer simulation, we demonstrate that the method is robust for different types of mapping populations derived from organisms with variable quality of genome sequences and is feasible for organisms with large genome sizes and low polymorphisms. With continuous advances in sequencing technologies, this genome-based method may replace the conventional marker-based genotyping approach to provide a powerful tool for large-scale gene discovery and for addressing a wide range of biological questions.
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                Author and article information

                Journal
                Genome Res
                Genome Res
                genome
                genome
                GENOME
                Genome Research
                Cold Spring Harbor Laboratory Press
                1088-9051
                1549-5469
                June 2020
                : 30
                : 6
                : 826-834
                Affiliations
                [1 ]Department of Biology, Indiana University, Bloomington, Indiana 47405, USA;
                [2 ]Department of Computer Science, Indiana University, Bloomington, Indiana 47405, USA;
                [3 ]Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas 77030, USA;
                [4 ]Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA;
                [5 ]California National Primate Research Center, University of California–Davis, Davis, California 95616, USA;
                [6 ]Khoury College of Computer Sciences, Northeastern University, Boston, Massachusetts 02115, USA
                Author notes
                Corresponding author: rjwang@ 123456indiana.edu
                Author information
                http://orcid.org/0000-0002-8889-4269
                http://orcid.org/0000-0002-5731-8808
                Article
                9509184
                10.1101/gr.255174.119
                7370888
                32461224
                f1fd0f15-5610-4053-8939-cb84c3645475
                © 2020 Wang et al.; Published by Cold Spring Harbor Laboratory Press

                This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the first six months after the full-issue publication date (see http://genome.cshlp.org/site/misc/terms.xhtml). After six months, it is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 29 July 2019
                : 21 May 2020
                Page count
                Pages: 9
                Funding
                Funded by: Precision Health Initiative of Indiana University
                Funded by: California National Primate Research Center , open-funder-registry 10.13039/100007862;
                Funded by: CNPRC , open-funder-registry 10.13039/100007862;
                Award ID: P51 OD011107
                Funded by: National Institutes of Health , open-funder-registry 10.13039/100000002;
                Funded by: NIH , open-funder-registry 10.13039/100000002;
                Award ID: R37 AG033590
                Award ID: R24 OD010962
                Categories
                Research

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