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      Autozygosity mapping and time-to-spontaneous delivery in Norwegian parent-offspring trios

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          Abstract

          Parental genetic relatedness may lead to adverse health and fitness outcomes in the offspring. However, the degree to which it affects human delivery timing is unknown. We use genotype data from ≃25 000 parent-offspring trios from the Norwegian Mother, Father and Child Cohort Study to optimize runs of homozygosity (ROH) calling by maximizing the correlation between parental genetic relatedness and offspring ROHs. We then estimate the effect of maternal, paternal and fetal autozygosity and that of autozygosity mapping (common segments and gene burden test) on the timing of spontaneous onset of delivery. The correlation between offspring ROH using a variety of parameters and parental genetic relatedness ranged between −0.2 and 0.6, revealing the importance of the minimum number of genetic variants included in an ROH and the use of genetic distance. The optimized compared to predefined parameters showed a ≃45% higher correlation between parental genetic relatedness and offspring ROH. We found no evidence of an effect of maternal, paternal nor fetal overall autozygosity on spontaneous delivery timing. Yet, through autozygosity mapping, we identified three maternal loci TBC1D1, SIGLECs and EDN1 gene regions reducing the median time-to-spontaneous onset of delivery by ≃2–5% ( P-value < 2.3 × 10 −6). We also found suggestive evidence of a fetal locus at 3q22.2, near the RYK gene region ( P-value = 2.0 × 10 −6). Autozygosity mapping may provide new insights on the genetic determinants of delivery timing beyond traditional genome-wide association studies, but particular and rigorous attention should be given to ROH calling parameter selection.

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          Most cited references70

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          The mutational constraint spectrum quantified from variation in 141,456 humans

          Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation. However, predicted loss-of-function variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes 1 . Here we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence predicted loss-of-function variants in this cohort after filtering for artefacts caused by sequencing and annotation errors. Using an improved model of human mutation rates, we classify human protein-coding genes along a spectrum that represents tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve the power of gene discovery for both common and rare diseases.
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            A reference panel of 64,976 haplotypes for genotype imputation.

            We describe a reference panel of 64,976 human haplotypes at 39,235,157 SNPs constructed using whole-genome sequence data from 20 studies of predominantly European ancestry. Using this resource leads to accurate genotype imputation at minor allele frequencies as low as 0.1% and a large increase in the number of SNPs tested in association studies, and it can help to discover and refine causal loci. We describe remote server resources that allow researchers to carry out imputation and phasing consistently and efficiently.
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              Robust relationship inference in genome-wide association studies.

              Genome-wide association studies (GWASs) have been widely used to map loci contributing to variation in complex traits and risk of diseases in humans. Accurate specification of familial relationships is crucial for family-based GWAS, as well as in population-based GWAS with unknown (or unrecognized) family structure. The family structure in a GWAS should be routinely investigated using the SNP data prior to the analysis of population structure or phenotype. Existing algorithms for relationship inference have a major weakness of estimating allele frequencies at each SNP from the entire sample, under a strong assumption of homogeneous population structure. This assumption is often untenable. Here, we present a rapid algorithm for relationship inference using high-throughput genotype data typical of GWAS that allows the presence of unknown population substructure. The relationship of any pair of individuals can be precisely inferred by robust estimation of their kinship coefficient, independent of sample composition or population structure (sample invariance). We present simulation experiments to demonstrate that the algorithm has sufficient power to provide reliable inference on millions of unrelated pairs and thousands of relative pairs (up to 3rd-degree relationships). Application of our robust algorithm to HapMap and GWAS datasets demonstrates that it performs properly even under extreme population stratification, while algorithms assuming a homogeneous population give systematically biased results. Our extremely efficient implementation performs relationship inference on millions of pairs of individuals in a matter of minutes, dozens of times faster than the most efficient existing algorithm known to us. Our robust relationship inference algorithm is implemented in a freely available software package, KING, available for download at http://people.virginia.edu/∼wc9c/KING.
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                Author and article information

                Contributors
                Journal
                Hum Mol Genet
                Hum Mol Genet
                hmg
                Human Molecular Genetics
                Oxford University Press
                0964-6906
                1460-2083
                01 December 2020
                08 December 2020
                08 December 2020
                : 29
                : 23
                : 3845-3858
                Affiliations
                Department of Obstetrics and Gynecology , Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg , Gothenburg 41685, Sweden
                Department of Obstetrics and Gynecology , Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg , Gothenburg 41685, Sweden
                Center for Diabetes Research , Department of Clinical Science, University of Bergen , 5020 Bergen, Norway
                Division of Health Data and Digitalization , Department of Genetics and Bioinformatics, Norwegian Institute of Public Health , Oslo 0213, Norway
                Department of Obstetrics and Gynecology , Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg , Gothenburg 41685, Sweden
                Center for Diabetes Research , Department of Clinical Science, University of Bergen , 5020 Bergen, Norway
                Department of Pediatrics and Adolescents , Haukeland University Hospital , Bergen 5021, Norway
                Division of Health Data and Digitalization , Department of Genetics and Bioinformatics, Norwegian Institute of Public Health , Oslo 0213, Norway
                NORMENT , University of Oslo , Oslo 0450, Norway
                Division of Mental Health and Addiction , Oslo University Hospital , Oslo 0450, Norway
                Department of Psychiatry , University of California San Diego , San Diego, CA 92093, USA
                Center for Diabetes Research , Department of Clinical Science, University of Bergen , 5020 Bergen, Norway
                Department of Pediatrics and Adolescents , Haukeland University Hospital , Bergen 5021, Norway
                Department of Pediatrics , University of Cincinnati College of Medicine , Cincinnati, OH 45267, USA
                Division of Human Genetics , The Center for Prevention of Preterm Birth, Perinatal Institute , March of Dimes Prematurity Research Center Ohio Collaborative, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45267, USA
                Center for Diabetes Research , Department of Clinical Science, University of Bergen , 5020 Bergen, Norway
                Center for Medical Genetics , Haukeland University Hospital , Bergen 5021, Norway
                Department of Pediatrics , University of Cincinnati College of Medicine , Cincinnati, OH 45267, USA
                Division of Human Genetics , The Center for Prevention of Preterm Birth, Perinatal Institute , March of Dimes Prematurity Research Center Ohio Collaborative, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45267, USA
                Department of Obstetrics and Gynecology , Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg , Gothenburg 41685, Sweden
                Division of Health Data and Digitalization , Department of Genetics and Bioinformatics, Norwegian Institute of Public Health , Oslo 0213, Norway
                Department of Obstetrics and Gynecology , Sahlgrenska University Hospital , Gothenburg 41685, Sweden
                Author notes
                To whom correspondence should be addressed at: Tel: (+46)31-3436769; Fax: (+46)031-786 3573; Email: pol.sole.navais@ 123456gu.se
                Author information
                http://orcid.org/0000-0002-3326-266X
                http://orcid.org/0000-0002-2298-7008
                Article
                ddaa255
                10.1093/hmg/ddaa255
                7861013
                33291140
                ba368b99-e5c7-41fb-adb4-e2bc0b0a187a
                © The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                : 3 July 2020
                : 21 November 2020
                : 24 November 2020
                Page count
                Pages: 14
                Funding
                Funded by: Norwegian Ministry of Health and Care Services;
                Funded by: Ministry of Education and Research, DOI 10.13039/501100010774;
                Award ID: N01-ES-75558
                Funded by: NIH, DOI 10.13039/501100012264;
                Award ID: UO1 NS 047537-01
                Funded by: NINDS, DOI 10.13039/100000065;
                Award ID: UO1 NS 047537-06A1
                Funded by: Burroughs Wellcome Fund, DOI 10.13039/100000861;
                Award ID: 10172896
                Funded by: Norwegian Institute of Public Health;
                Categories
                Association Studies Article
                AcademicSubjects/SCI01140

                Genetics
                Genetics

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