The detection of family relationships in genetic databases is of interest in various scientific disciplines such as genetic epidemiology, population and conservation genetics, forensic science, and genealogical research. Nowadays, screening genetic databases for related individuals forms an important aspect of standard quality control procedures. Relatedness research is usually based on an allele sharing analysis of identity by state (IBS) or identity by descent (IBD) alleles. Existing IBS/IBD methods mainly aim to identify first-degree relationships (parent–offspring or full siblings) and second degree (half-siblings, avuncular, or grandparent–grandchild) pairs. Little attention has been paid to the detection of in-between first and second-degree relationships such as three-quarter siblings (3/4S) who share fewer alleles than first-degree relationships but more alleles than second-degree relationships. With the progressively increasing sample sizes used in genetic research, it becomes more likely that such relationships are present in the database under study. In this paper, we extend existing likelihood ratio (LR) methodology to accurately infer the existence of 3/4S, distinguishing them from full siblings and second-degree relatives. We use bootstrap confidence intervals to express uncertainty in the LRs. Our proposal accounts for linkage disequilibrium (LD) by using marker pruning, and we validate our methodology with a pedigree-based simulation study accounting for both LD and recombination. An empirical genome-wide array data set from the GCAT Genomes for Life cohort project is used to illustrate the method. ; This work was partially supported by grants RTI2018-095518-B-C22 (JG), RTI2018-095518-B-C21 (IGF and CBV), and ADE 10/00026 (RdC) (MCIU/AEI/FEDER) of the Spanish Ministry of Science, Innovation and Universities and the European Regional Development Fund, by grants SGR1269 and 2017 SGR529 (RdC) of the Generalitat de Catalunya, by grant R01 GM075091 (JG) from the United States National Institutes of Health, by the Ramon y Cajal action RYC-2011-07822 (RdC), by Agency for Management of University and Research Grants (AGAUR) of the Catalan Government grant 2017SGR723 (VM), and by the Spanish Association Against Cancer (AECC) Scientific Foundation, grant GCTRA18022MORE (VM). ; Peer Reviewed ; Postprint (published version)
The combined analysis of haplotype panels with phenotype clinical cohorts is a common approach to explore the genetic architecture of human diseases. However, genetic studies are mainly based on single nucleotide variants (SNVs) and small insertions and deletions (indels). Here, we contribute to fill this gap by generating a dense haplotype map focused on the identification, characterization, and phasing of structural variants (SVs). By integrating multiple variant identification methods and Logistic Regression Models (LRMs), we present a catalogue of 35 431 441 variants, including 89 178 SVs (≥50 bp), 30 325 064 SNVs and 5 017 199 indels, across 785 Illumina high coverage (30x) whole-genomes from the Iberian GCAT Cohort, containing a median of 3.52M SNVs, 606 336 indels and 6393 SVs per individual. The haplotype panel is able to impute up to 14 360 728 SNVs/indels and 23 179 SVs, showing a 2.7-fold increase for SVs compared with available genetic variation panels. The value of this panel for SVs analysis is shown through an imputed rare Alu element located in a new locus associated with Mononeuritis of lower limb, a rare neuromuscular disease. This study represents the first deep characterization of genetic variation within the Iberian population and the first operational haplotype panel to systematically include the SVs into genome-wide genetic studies. ; GCAT|Genomes for Life, a cohort study of the Genomes of Catalonia, Fundació Institut Germans Trias i Pujol (IGTP); IGTP is part of the CERCA Program/Generalitat de Catalunya; GCAT is supported by Acción de Dinamización del ISCIII-MINECO; Ministry of Health of the Generalitat of Catalunya [ADE 10/00026]; Agència de Gestió d'Ajuts Universitaris i de Recerca (AGAUR) [2017-SGR 529]; B.C. is supported by national grants [PI18/01512]; X.F. is supported by VEIS project [001-P-001647] (co-funded by European Regional Development Fund (ERDF), 'A way to build Europe'); a full list of the investigators who contributed to the generation of the GCAT data is available from www.genomesforlife.com/; Severo Ochoa Program, awarded by the Spanish Government [SEV-2011-00067 and SEV2015-0493]; Spanish Ministry of Science [TIN2015-65316-P]; Innovation and by the Generalitat de Catalunya [2014-SGR-1051 to D.T.]; Agencia Estatal de Investigación (AEI, Spain) [BFU2016-77244-R and PID2019-107836RB-I00]; European Regional Development Fund (FEDER, EU) (to M.C.); Spanish Ministry of Science and Innovation [FPI BES-2016-0077344 to J.V.M.]; C.S. received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement [H2020-MSCA-COFUND-2016-754433]; this study made use of data generated by the UK10K Consortium from UK10K COHORT IMPUTATION [EGAS00001000713]; formal agreement with the Barcelona Supercomputing Center (BSC); this study made use of data generated by the Genome of the Netherlands' project, which is funded by the Netherlands Organization for Scientific Research [184021007], allowing us to use the GoNL reference panel containing SVs, upon request (GoNL Data Access request 2019203); this study also used data generated by the Haplotype Reference Consortium (HRC) accessed through the European Genome-phenome Archive with the accession numbers EGAD00001002729; formal agreement of the Barcelona Supercomputing Center (BSC) with WTSI; this study made use of data generated by the 1000 Genomes (1000G), accessed through the FTP portal (http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/); this study used the GeneHancer-for-AnnotSV dump for GeneCards Suite Version 4.14, through a formal agreement between the BSC and the Weizmann Institute of Science. ; Peer Reviewed ; "Article signat per 21 autors/es: Jordi Valls-Margarit, Iván Galván-Femenía, Daniel Matías-Sánchez, Natalia Blay, Montserrat Puiggròs, Anna Carreras, Cecilia Salvoro, Beatriz Cortés, Ramon Amela, Xavier Farre, Jon Lerga-Jaso, Marta Puig, Jose Francisco Sánchez-Herrero, Victor Moreno, Manuel Perucho, Lauro Sumoy, Lluís Armengol, Olivier Delaneau, Mario Cáceres, Rafael de Cid, David Torrents" ; Postprint (published version)
The combined analysis of haplotype panels with phenotype clinical cohorts is a common approach to explore the genetic architecture of human diseases. However, genetic studies are mainly based on single nucleotide variants (SNVs) and small insertions and deletions (indels). Here, we contribute to fill this gap by generating a dense haplotype map focused on the identification, characterization, and phasing of structural variants (SVs). By integrating multiple variant identification methods and Logistic Regression Models (LRMs), we present a catalogue of 35 431 441 variants, including 89 178 SVs (≥50 bp), 30 325 064 SNVs and 5 017 199 indels, across 785 Illumina high coverage (30x) whole-genomes from the Iberian GCAT Cohort, containing a median of 3.52M SNVs, 606 336 indels and 6393 SVs per individual. The haplotype panel is able to impute up to 14 360 728 SNVs/indels and 23 179 SVs, showing a 2.7-fold increase for SVs compared with available genetic variation panels. The value of this panel for SVs analysis is shown through an imputed rare Alu element located in a new locus associated with Mononeuritis of lower limb, a rare neuromuscular disease. This study represents the first deep characterization of genetic variation within the Iberian population and the first operational haplotype panel to systematically include the SVs into genome-wide genetic studies. ; GCAT|Genomes for Life, a cohort study of the Genomes of Catalonia, Fundació Institut Germans Trias i Pujol (IGTP); IGTP is part of the CERCA Program/Generalitat de Catalunya; GCAT is supported by Acción de Dinamización del ISCIII-MINECO; Ministry of Health of the Generalitat of Catalunya [ADE 10/00026]; Agència de Gestió d'Ajuts Universitaris i de Recerca (AGAUR) [2017-SGR 529]; B.C. is supported by national grants [PI18/01512]; X.F. is supported by VEIS project [001-P-001647] (co-funded by European Regional Development Fund (ERDF), 'A way to build Europe'); a full list of the investigators who contributed to the generation of the GCAT data is available from www.genomesforlife.com/; Severo Ochoa Program, awarded by the Spanish Government [SEV-2011-00067 and SEV2015-0493]; Spanish Ministry of Science [TIN2015-65316-P]; Innovation and by the Generalitat de Catalunya [2014-SGR-1051 to D.T.]; Agencia Estatal de Investigación (AEI, Spain) [BFU2016-77244-R and PID2019-107836RB-I00]; European Regional Development Fund (FEDER, EU) (to M.C.); Spanish Ministry of Science and Innovation [FPI BES-2016-0077344 to J.V.M.]; C.S. received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement [H2020-MSCA-COFUND-2016-754433]; this study made use of data generated by the UK10K Consortium from UK10K COHORT IMPUTATION [EGAS00001000713]; formal agreement with the Barcelona Supercomputing Center (BSC); this study made use of data generated by the Genome of the Netherlands' project, which is funded by the Netherlands Organization for Scientific Research [184021007], allowing us to use the GoNL reference panel containing SVs, upon request (GoNL Data Access request 2019203); this study also used data generated by the Haplotype Reference Consortium (HRC) accessed through the European Genome-phenome Archive with the accession numbers EGAD00001002729; formal agreement of the Barcelona Supercomputing Center (BSC) with WTSI; this study made use of data generated by the 1000 Genomes (1000G), accessed through the FTP portal (http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/); this study used the GeneHancer-for-AnnotSV dump for GeneCards Suite Version 4.14, through a formal agreement between the BSC and the Weizmann Institute of Science. Funding for open access charge: GCAT|Genomes for Life, a cohort study of the Genomes of Catalonia, Fundació Institut Germans Trias i Pujol (IGTP); IGTP is part of the CERCA Program/Generalitat de Catalunya; GCAT is supported by Acción de Dinamización del ISCIII-MINECO; Ministry of Health of the Generalitat of Catalunya [ADE 10/00026]; Agència de Gestió d'Ajuts Universitaris i de Recerca (AGAUR) [2017-SGR 529]; B.C. is supported by national grants [PI18/01512]; X.F. is supported by VEIS project [001-P-001647] (co-funded by European Regional Development Fund (ERDF), 'A way to build Europe'); a full list of the investigators who contributed to the generation of the GCAT data is available from www.genomesforlife.com/; Severo Ochoa Program, awarded by the Spanish Government [SEV-2011-00067 and SEV2015-0493]; Spanish Ministry of Science [TIN2015-65316-P]; Innovation and by the Generalitat de Catalunya [2014-SGR-1051 to D.T.]; [Agencia Estatal de Investigación (AEI, Spain) [BFU2016-77244-R and PID2019-107836RB-I00]; European Regional Development Fund (FEDER, EU) (to M.C.); Spanish Ministry of Science and Innovation [FPI BES-2016-0077344 to J.V.M.]; C.S. received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement [H2020-MSCA-COFUND-2016-754433]; this study made use of data generated by the UK10K Consortium from UK10K COHORT IMPUTATION [EGAS00001000713]; formal agreement with the Barcelona Supercomputing Center (BSC); this study made use of data generated by the Genome of the Netherlands' project, which is funded by the Netherlands Organization for Scientific Research [184021007], allowing us to use the GoNL reference panel containing SVs, upon request (GoNL Data Access request 2019203); this study also used data generated by the Haplotype Reference Consortium (HRC) accessed through the European Genome-phenome Archive with the accession numbers EGAD00001002729; formal agreement of the Barcelona Supercomputing Center (BSC) with WTSI; this study made use of data generated by the 1000 Genomes (1000G), accessed through the FTP portal (http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/); this study used the GeneHancer-for-AnnotSV dump for GeneCards Suite Version 4.14, through a formal agreement between the BSC and The Weizmann Institute of Science. ; "Article signat per 21 autors/es: Jordi Valls-Margarit, Iván Galván-Femenía, Daniel Matías-Sánchez, Natalia Blay, Montserrat Puiggròs, Anna Carreras, Cecilia Salvoro, Beatriz Cortés, Ramon Amela, Xavier Farre, Jon Lerga-Jaso, Marta Puig, Jose Francisco Sánchez-Herrero, Victor Moreno, Manuel Perucho, Lauro Sumoy, Lluís Armengol, Olivier Delaneau, Mario Cáceres, Rafael de Cid, David Torrents" ; Postprint (published version)
