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      Time-varying effects are common in genetic control of gestational duration

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          Abstract

          Preterm birth is a major burden to neonatal health worldwide, determined in part by genetics. Recently, studies discovered several genes associated with this trait or its continuous equivalent—gestational duration. However, their effect timing, and thus clinical importance, is still unclear. Here, we use genotyping data of 31 000 births from the Norwegian Mother, Father and Child cohort (MoBa) to investigate different models of the genetic pregnancy ‘clock’. We conduct genome-wide association studies using gestational duration or preterm birth, replicating known maternal associations and finding one new fetal variant. We illustrate how the interpretation of these results is complicated by the loss of power when dichotomizing. Using flexible survival models, we resolve this complexity and find that many of the known loci have time-varying effects, often stronger early in pregnancy. The overall polygenic control of birth timing appears to be shared in the term and preterm, but not very preterm, periods and exploratory results suggest involvement of the major histocompatibility complex genes in the latter. These findings show that the known gestational duration loci are clinically relevant and should help design further experimental studies.

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          Most cited references30

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          Second-generation PLINK: rising to the challenge of larger and richer datasets

          PLINK 1 is a widely used open-source C/C++ toolset for genome-wide association studies (GWAS) and research in population genetics. However, the steady accumulation of data from imputation and whole-genome sequencing studies has exposed a strong need for even faster and more scalable implementations of key functions. In addition, GWAS and population-genetic data now frequently contain probabilistic calls, phase information, and/or multiallelic variants, none of which can be represented by PLINK 1's primary data format. To address these issues, we are developing a second-generation codebase for PLINK. The first major release from this codebase, PLINK 1.9, introduces extensive use of bit-level parallelism, O(sqrt(n))-time/constant-space Hardy-Weinberg equilibrium and Fisher's exact tests, and many other algorithmic improvements. In combination, these changes accelerate most operations by 1-4 orders of magnitude, and allow the program to handle datasets too large to fit in RAM. This will be followed by PLINK 2.0, which will introduce (a) a new data format capable of efficiently representing probabilities, phase, and multiallelic variants, and (b) extensions of many functions to account for the new types of information. The second-generation versions of PLINK will offer dramatic improvements in performance and compatibility. For the first time, users without access to high-end computing resources can perform several essential analyses of the feature-rich and very large genetic datasets coming into use.
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            g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update)

            Abstract Biological data analysis often deals with lists of genes arising from various studies. The g:Profiler toolset is widely used for finding biological categories enriched in gene lists, conversions between gene identifiers and mappings to their orthologs. The mission of g:Profiler is to provide a reliable service based on up-to-date high quality data in a convenient manner across many evidence types, identifier spaces and organisms. g:Profiler relies on Ensembl as a primary data source and follows their quarterly release cycle while updating the other data sources simultaneously. The current update provides a better user experience due to a modern responsive web interface, standardised API and libraries. The results are delivered through an interactive and configurable web design. Results can be downloaded as publication ready visualisations or delimited text files. In the current update we have extended the support to 467 species and strains, including vertebrates, plants, fungi, insects and parasites. By supporting user uploaded custom GMT files, g:Profiler is now capable of analysing data from any organism. All past releases are maintained for reproducibility and transparency. The 2019 update introduces an extensive technical rewrite making the services faster and more flexible. g:Profiler is freely available at https://biit.cs.ut.ee/gprofiler.
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              LD Score regression distinguishes confounding from polygenicity in genome-wide association studies.

              Both polygenicity (many small genetic effects) and confounding biases, such as cryptic relatedness and population stratification, can yield an inflated distribution of test statistics in genome-wide association studies (GWAS). However, current methods cannot distinguish between inflation from a true polygenic signal and bias. We have developed an approach, LD Score regression, that quantifies the contribution of each by examining the relationship between test statistics and linkage disequilibrium (LD). The LD Score regression intercept can be used to estimate a more powerful and accurate correction factor than genomic control. We find strong evidence that polygenicity accounts for the majority of the inflation in test statistics in many GWAS of large sample size.
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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
                15 July 2023
                17 May 2023
                17 May 2023
                : 32
                : 14
                : 2399-2407
                Affiliations
                Department of Obstetrics and Gynecology, Institute of Clinical Sciences, University of Gothenburg , Gothenburg 416 50, Sweden
                Department of Obstetrics and Gynecology, Institute of Clinical Sciences, University of Gothenburg , Gothenburg 416 50, Sweden
                Department of Obstetrics and Gynecology, Institute of Clinical Sciences, University of Gothenburg , Gothenburg 416 50, Sweden
                Department of Obstetrics and Gynecology, Institute of Clinical Sciences, University of Gothenburg , Gothenburg 416 50, Sweden
                Department of Obstetrics and Gynecology, Institute of Clinical Sciences, University of Gothenburg , Gothenburg 416 50, Sweden
                Department of Genetics and Bioinformatics, Division of Health Data and Digitalisation, Norwegian Institute of Public Health , Oslo 0456, Norway
                Author notes
                To whom correspondence should be addressed at: Department of Obstetrics and Gynecology, Institute of Clinical Sciences, University of Gothenburg, Diagnosvägen 14, Gothenburg 416 50, Sweden. Tel: +46 31 3436769; Email: xjouju@ 123456gu.se
                Author information
                https://orcid.org/0000-0001-5600-974X
                https://orcid.org/0000-0002-4314-1546
                https://orcid.org/0000-0002-3326-266X
                Article
                ddad086
                10.1093/hmg/ddad086
                10321382
                37195282
                83f85878-f344-49d1-ab0b-d68959b3657c
                © The Author(s) 2023. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 21 February 2023
                : 10 May 2023
                : 15 May 2023
                : 01 June 2023
                Page count
                Pages: 9
                Funding
                Funded by: Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health;
                Award ID: R01HD101669
                Funded by: Agreement concerning research and education of doctors;
                Award ID: ALFGBG-965353
                Funded by: March of Dimes, DOI 10.13039/100000912;
                Award ID: 21-FY16-121
                Funded by: The Research Council of Norway, Oslo, Norway;
                Award ID: 547711
                Award ID: 2019-01004
                Funded by: Research Council of Norway, DOI 10.13039/501100005416;
                Award ID: 223273
                Funded by: Research Council of Norway, DOI 10.13039/501100005416;
                Award ID: 229624
                Categories
                AcademicSubjects/SCI01140
                Association Studies Article

                Genetics
                Genetics

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