Comprehensive identification and characterisation of germline structural variation within the Iberian population

[eng] One of the central aims of biology and biomedicine has been the characterisation and understanding of genetic variation across humans, to answer important evolutionary questions and to explain phenotypic variability concerning the diseases. Understanding genetic variability, is key to study this relationship (through imputation and GWASs) and to translate the results into improved clinical protocols. Different initiatives have emerged around the world to systematically characterise the genetic variability of specific human populations from whole-genome sequences, usually by selecting geographical regions. Examples such as 1000 Genomes (1000G)1, GoNL2, HRC, UK10K3 or Estonian population4, have already identified and characterised millions of genetic variants across different populations. In combination with imputation analysis, these sequenced-based projects allow increasing the statistical power and resolution of Genome-Wide Association Studies (GWAS), identifying and discovering new disease-associated variants5. Additionally, genetic variability among population groups is associated with geographic ancestry and can affect the disease risk or treatment efficacy differently6,7. For this reason, population- specific reference panels are necessary to characterise their genetic diversity and to assess its effect on human phenotypes, improving GWAS studies, as one of the cornerstones of precision medicine7. Existing genetic variability panels include Single Nucleotide Variants (SNVs) and indels (<50bp) but are limited in large Structural Variants (SV) (≥50bp). Technical and methodological limitations hindered the discovery of SVs using Next-generation Sequencing (NGS) technologies, as it produced False-Discovery Rates between 9-89% and recall 10-70%, depending on the SV type and size8. On average, the genomic variation between two human genomes is around 0.1%, but this difference increases to 1.5% with SVs8. The SVs also affect 3-10 times more nucleotides than SNVs9 (4M SNVs per genome10), showing their potential effect on human phenotypes. For this reason, including a complete catalogue of SVs in reference panels will increase the power in GWAS studies and provide opportunities to find new disease-associated variants. To overcome these limitations, in this thesis, we have generated the first genome-wide Iberian haplotype reference panel, mainly focused on Structural Variants, using 785 samples whole-genome sequenced (WGS) at high coverage (30X) from the GCAT-Genomics for life project. We designed a complete strategy, including an extensive benchmarking of multiple variant calling programs and by building specific Logistic Regression Models (LRM) for SV types, as well as phasing strategies to come up with a high quality and comprehensive genetic variability panel. This strategy was benchmarked using different controlled sets of variants, showing high precision and recall values across all variant types and sizes. The application of this strategy to our GCAT whole-genome samples resulted in the identification of 35,431,441 genetic variants, classified as 30,325,064 SNPs, 5,017,19 small indels (< 50bp), and 89,178 larger SV (≥ 50bp). The latter group was further subclassified into 33,244 deletions, 6,269 duplications, 12,782 insertions, 10,115 inversions, 18,779 transposons and 7,989 translocations, covering all ranges of frequencies and sizes. Besides, 60% of the discovered SVs were not catalogued in any repository, thus increasing the insights of SV in humans. Additionally, 52.44% of common and 71.63% of low-frequency SVs were not included in any haplotype reference panel. Thus, new SVs could be used in GWAS, adding more value to the Iberian-GCAT catalogue. The prediction of the functional impact of the SVs shows that these variants might have a central role in several diseases. Of all SVs included in the Iberian-GCAT catalogue, 46% overlapped in genes (both protein-coding genes and non-protein-coding genes), highlighting their potential impact on human traits. Besides, 92.7% of protein-coding genes were located outside low-complexity (repeated) genomic regions, expecting short-reads from NGS to capture the most interpretable SVs in humans11. Moreover, 32.93% of SVs affected protein-coding genes with a predicted loss of function intolerance (pLI) effect, further supporting the potential implication of these variants on complex diseases and therefore enabling a better explanation of missing heritability. Importantly, taking advantage of high coverage (30X), we accurately determine the genotypes of SVs, enabling to phase together with SNVs and indels, and increasing the SV phasing accuracy, in contrast to 1000G and GoNL. Besides, high coverage allowed to use Phasing Informative Reads (PIRs), increasing the phasing performance. The overall strategy enables the community to expand and improve the imputation possibilities within GWAS. The Iberian-GCAT haplotype reference panel created in this thesis, imputes accurately common SVs, with near ~100% of agreement with sequencing results. Although the Iberian- GCAT haplotype reference panel can be used in all populations from different continental groups, due to closer ancestries, the imputation performance is high in European and Latin American populations, reflected in the amount of low-frequency (1% ≤ MAF MAF) variants imputed at high info scores. These results demonstrated the versatility of our resource, increasing their performance in closer ancestries. In general, we observed that when the allele frequency decreases, the imputation accuracy drops too, highlighting the necessity to include more samples in reference panels, to impute low-frequency and rare variants efficiently, which normally are expected to have more functional impact on diseases. Finally, we compared the imputation possibilities of the 1000G and GoNL reference panels, with our Iberian-GCAT reference panel. We observed that the Iberian-GCAT reference panel outperformed the imputation of high-quality SVs by 2.7 and 1.6-fold compared to 1000G and GoNL, respectively. Also, the overall imputation quality is higher, showing the value of this new resource in GWAS as it includes more SVs than previous reference panels. The combination of different reference panels will improve the resolution and statistical power of GWAS, thus increasing the chances to find more risk variants in complex diseases, and ultimately, translated this insight to precision medicine

GCAT|Panel, a comprehensive structural variant haplotype map of the Iberian population from high-coverage whole-genome sequencing

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

Engineered Macroscale Cardiac Constructs Elicit Human Myocardial Tissue-like Functionality

In vitro surrogate models of human cardiac tissue hold great promise in disease modeling, cardiotoxicity testing, and future applications in regenerative medicine. However, the generation of engineered human cardiac constructs with tissue-like functionality is currently thwarted by difficulties in achieving efficient maturation at the cellular and/or tissular level. Here, we report on the design and implementation of a platform for the production of engineered cardiac macrotissues from human pluripotent stem cells (PSCs), which we term "CardioSlice." PSC-derived cardiomyocytes, together with human fibroblasts, are seeded into large 3D porous scaffolds and cultured using a parallelized perfusion bioreactor with custom-made culture chambers. Continuous electrical stimulation for 2 weeks promotes cardiomyocyte alignment and synchronization, and the emergence of cardiac tissue-like properties. These include electrocardiogram-like signals that can be readily measured on the surface of CardioSlice constructs, and a response to proarrhythmic drugs that is predictive of their effect in human patients

GCAT|Panel, a comprehensive structural variant haplotype map of the Iberian population from high-coverage whole-genome sequencing

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

Genomic and proteomic biomarker landscape in clinical trials

The use of molecular biomarkers to support disease diagnosis, monitor its progression, and guide drug treatment has gained traction in the last decades. While only a dozen biomarkers have been approved for their exploitation in the clinic by the FDA, many more are evaluated in the context of translational research and clinical trials. Furthermore, the information on which biomarkers are measured, for which purpose, and in relation to which conditions are not readily accessible: biomarkers used in clinical studies available through resources such as ClinicalTrials.gov are described as free text, posing significant challenges in finding, analyzing, and processing them by both humans and machines. We present a text mining strategy to identify proteomic and genomic biomarkers used in clinical trials and classify them according to the methodologies by which they are measured. We find more than 3000 biomarkers used in the context of 2600 diseases. By analyzing this dataset, we uncover patterns of use of biomarkers across therapeutic areas over time, including the biomarker type and their specificity. These data are made available at the Clinical Biomarker App at https://www.disgenet.org/biomarkers/, a new portal that enables the exploration of biomarkers extracted from the clinical studies available at ClinicalTrials.gov and enriched with information from the scientific literature. The App features several metrics that assess the specificity of the biomarkers, facilitating their selection and prioritization. Overall, the Clinical Biomarker App is a valuable and timely resource about clinical biomarkers, to accelerate biomarker discovery, development, and application

Genomic and proteomic biomarker landscape in clinical trials

The use of molecular biomarkers to support disease diagnosis, monitor its progression, and guide drug treatment has gained traction in the last decades. While only a dozen biomarkers have been approved for their exploitation in the clinic by the FDA, many more are evaluated in the context of translational research and clinical trials. Furthermore, the information on which biomarkers are measured, for which purpose, and in relation to which conditions are not readily accessible: biomarkers used in clinical studies available through resources such as ClinicalTrials.gov are described as free text, posing significant challenges in finding, analyzing, and processing them by both humans and machines. We present a text mining strategy to identify proteomic and genomic biomarkers used in clinical trials and classify them according to the methodologies by which they are measured. We find more than 3000 biomarkers used in the context of 2600 diseases. By analyzing this dataset, we uncover patterns of use of biomarkers across therapeutic areas over time, including the biomarker type and their specificity. These data are made available at the Clinical Biomarker App at https://www.disgenet.org/biomarkers/, a new portal that enables the exploration of biomarkers extracted from the clinical studies available at ClinicalTrials.gov and enriched with information from the scientific literature. The App features several metrics that assess the specificity of the biomarkers, facilitating their selection and prioritization. Overall, the Clinical Biomarker App is a valuable and timely resource about clinical biomarkers, to accelerate biomarker discovery, development, and application.This work was partially funded by IMI2-JU grants, resources which are composed of financial contributions from the European Union’s Horizon 2020 Research and Innovation Programme and EFPIA [GA: 116030 TransQST and GA: 777365 eTRANSAFE], and the EU H2020 Programme [GA: 871075 Elixir-CONVERGE and GA:964537 RISKHUNT3R, which is part of the ASPIS cluster]; Project 001-P-001647—Valorisation of EGA for Industry and Society funded by the European Regional Development Fund (ERDF) and Generalitat de Catalunya; Agència de Gestió d’Ajuts Universitaris i de Recerca Generalitat de Catalunya [2017SGR00519], and the Institute of Health Carlos III (project IMPaCT-Data, exp. IMP/00019), co-funded by the European Union, European Regional Development Fund (ERDF, “A way to make Europe”). The Research Programme on Biomedical Informatics (GRIB) is a member of the Spanish National Bioinformatics Institute (INB), funded by ISCIII and ERDF (PRB2-ISCIII [PT13/0001/0023, of the PE I + D + i 2013-2016]). The MELIS is a ‘Unidad de Excelencia María de Maeztu’, funded by the MINECO [MDM-2014-0370]. This work reflects only the author’s view and that the IMI2-JU is not responsible for any use that may be made of the information it contains. The European Commission is not responsible for any use that may be made of the information it contains