A Log-Ratio Biplot Approach for Exploring Genetic Relatedness Based on Identity by State

The detection of cryptic relatedness in large population-based cohorts is of great importance in genome research. The usual approach for detecting closely related individuals is to plot allele sharing statistics, based on identity-by-state or identity-by-descent, in a two-dimensional scatterplot. This approach ignores that allele sharing data across individuals has in reality a higher dimensionality, and neither regards the compositional nature of the underlying counts of shared genotypes. In this paper we develop biplot methodology based on log-ratio principal component analysis that overcomes these restrictions. This leads to entirely new graphics that are essentially useful for exploring relatedness in genetic databases from homogeneous populations. The proposed method can be applied in an iterative manner, acting as a looking glass for more remote relationships that are harder to classify. Datasets from the 1,000 Genomes Project and the Genomes For Life-GCAT Project are used to illustrate the proposed method. The discriminatory power of the log-ratio biplot approach is compared with the classical plots in a simulation study. In a non-inbred homogeneous population the classification rate of the log-ratio principal component approach outperforms the classical graphics across the whole allele frequency spectrum, using only identity by state. In these circumstances, simulations show that with 35,000 independent bi-allelic variants, log-ratio principal component analysis, combined with discriminant analysis, can correctly classify relationships up to and including the fourth degree

A log-ratio biplot approach for exploring genetic relatedness based on identity by state

The detection of cryptic relatedness in large population-based cohorts is of great importance in genome research. The usual approach for detecting closely related individuals is to plot allele sharing statistics, based on identity-by-state or identity-by-descent, in a two-dimensional scatterplot. This approach ignores that allele sharing data across individuals has in reality a higher dimensionality, and neither regards the compositional nature of the underlying counts of shared genotypes. In this paper we develop biplot methodology based on log-ratio principal component analysis that overcomes these restrictions. This leads to entirely new graphics that are essentially useful for exploring relatedness in genetic databases from homogeneous populations. The proposed method can be applied in an iterative manner, acting as a looking glass for more remote relationships that are harder to classify. Datasets from the 1,000 Genomes Project and the Genomes For Life-GCAT Project are used to illustrate the proposed method. The discriminatory power of the log-ratio biplot approach is compared with the classical plots in a simulation study. In a non-inbred homogeneous population the classification rate of the log-ratio principal component approach outperforms the classical graphics across the whole allele frequency spectrum, using only identity by state. In these circumstances, simulations show that with 35,000 independent bi-allelic variants, log-ratio principal component analysis, combined with discriminant analysis, can correctly classify relationships up to and including the fourth degreePostprint (published version

The chromatin network helps prevent cancer-associated mutagenesis at transcription-replication conflicts

Genome instability is a feature of cancer cells, transcription being an important source of DNA damage. This is in large part associated with R-loops, which hamper replication, especially at head-on transcription-replication conflicts (TRCs). Here we show that TRCs trigger a DNA Damage Response (DDR) involving the chromatin network to prevent genome instability. Depletion of the key chromatin factors INO80, SMARCA5 and MTA2 results in TRCs, fork stalling and R-loop-mediated DNA damage which mostly accumulates at S/G2, while histone H3 Ser10 phosphorylation, a mark of chromatin compaction, is enriched at TRCs. Strikingly, TRC regions show increased mutagenesis in cancer cells with signatures of homologous recombination deficiency, transcription-coupled nucleotide excision repair (TC-NER) and of the AID/ APOBEC cytidine deaminases, being predominant at head-on collisions. Thus, our results support that the chromatin network prevents R-loops and TRCs from genomic instability and mutagenic signatures frequently associated with cancer.MCIN/AEI/10.13039/501100011033 - I + D + i PID2019-104270GB-I00/BMCConsejo Europeo de Investigación - ERC2014 AdG669898 TARLOO

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

Pipeline design to identify key features and classify the chemotherapy response on lung cancer patients using large-scale genetic data

Background: During the last decade, the interest to apply machine learning algorithms to genomic data has increased in many bioinformatics applications. Analyzing this type of data entails difficulties for managing high-dimensional data, class imbalance for knowledge extraction, identifying important features and classifying individuals. In this study, we propose a general framework to tackle these challenges with different machine learning algorithms and techniques. We apply the configuration of this framework on lung cancer patients, identifying genetic signatures for classifying response to drug treatment response. We intersect these relevant SNPs with the GWAS Catalog of the National Human Genome Research Institute and explore the Regulomedb, GTEx databases for functional analysis purposes. Results: The machine learning based solution proposed in this study is a scalable and flexible alternative to the classical uni-variate regression approach to analyze large-scale data. From 36 experiments executed using the machine learning framework design, we obtain good classification performance from the top 5 models with the highest cross-validation score and the smallest standard deviation. One thousand two hundred twenty four SNPs corresponding to the key features from the top 20 models (cross validation F1 mean >= 0.65) were compared with the GWAS Catalog finding no intersection with genome-wide significant reported hits. From these, new genetic signatures in MAE, CEP104, PRKCZ and ADRB2 show relevant biological regulatory functionality related to lung physiology. Conclusions: We have defined a machine learning framework using data with an unbalanced large data-set of SNP-arrays and imputed genotyping data from a pharmacogenomics study in lung cancer patients subjected to first-line platinum-based treatment. This approach found genome signals with no genome-wide significance in the uni-variate regression approach (GWAS Catalog) that are valuable for classifying patients, only few of them with related biological function. The effect results of these variants can be explained by the recently proposed omnigenic model hypothesis, which states that complex traits can be influenced mostly by genes outside not only by the “core genes”, mainly found by the genome-wide significant SNPs, but also by the rest of genes outside of the “core pathways” with apparent unrelated biological functionality.Peer ReviewedPostprint (published version

GCAT|Genomes for life: a prospective cohort study of the genomes of Catalonia

PURPOSE: The prevalence of chronic non-communicable diseases (NCDs) is increasing worldwide. NCDs are the leading cause of both morbidity and mortality, and it is estimated that by 2030, they will be responsible for 80% of deaths across the world. The Genomes for Life (GCAT) project is a long-term prospective cohort study that was designed to integrate and assess the role of epidemiological, genomic and epigenomic factors in the development of major chronic diseases in Catalonia, a north-east region of Spain. PARTICIPANTS: At the end of 2017, the GCAT Study will have recruited 20 000 participants aged 40-65 years. Participants who agreed to take part in the study completed a self-administered computer-driven questionnaire, and underwent blood pressure, cardiac frequency and anthropometry measurements. For each participant, blood plasma, blood serum and white blood cells are collected at baseline. The GCAT Study has access to the electronic health records of the Catalan Public Healthcare System. Participants will be followed biannually at least 20 years after recruitment. FINDINGS TO DATE: Among all GCAT participants, 59.2% are women and 83.3% of the cohort identified themselves as Caucasian/white. More than half of the participants have higher education levels, 72.2% are current workers and 42.1% are classified as overweight (body mass index ≥25 and <30 kg/m2). We have genotyped 5459 participants, of which 5000 have metabolome data. Further, the whole genome of 808 participants will be sequenced by the end of 2017. FUTURE PLANS: The first follow-up study started in December 2017 and will end by March 2018. Residences of all subjects will be geocoded during the following year. Several genomic analyses are ongoing, and metabolomic and genomic integrations will be performed to identify underlying genetic variants, as well as environmental factors that influence metabolites

Multitrait genome association analysis identifies new susceptibility genes for human anthropometric variation in the GCAT cohort

Background Heritability estimates have revealed an important contribution of SNP variants for most common traits; however, SNP analysis by single-trait genome-wide association studies (GWAS) has failed to uncover their impact. In this study, we applied a multitrait GWAS approach to discover additional factor of the missing heritability of human anthropometric variation. Methods We analysed 205 traits, including diseases identified at baseline in the GCAT cohort (Genomes For Life- Cohort study of the Genomes of Catalonia) (n=4988), a Mediterranean adult population-based cohort study from the south of Europe. We estimated SNP heritability contribution and single-trait GWAS for all traits from 15 million SNP variants. Then, we applied a multitrait-related approach to study genome-wide association to anthropometric measures in a two-stage meta-analysis with the UK Biobank cohort (n=336 107). Results Heritability estimates (eg, skin colour, alcohol consumption, smoking habit, body mass index, educational level or height) revealed an important contribution of SNP variants, ranging from 18% to 77%. Single-trait analysis identified 1785 SNPs with genome-wide significance threshold. From these, several previously reported single-trait hits were confirmed in our sample with LINC01432 (p=1.9×10−9) variants associated with male baldness, LDLR variants with hyperlipidaemia (ICD-9:272) (p=9.4×10−10) and variants in IRF4 (p=2.8×10−57), SLC45A2 (p=2.2×10−130), HERC2 (p=2.8×10−176), OCA2 (p=2.4×10−121) and MC1R (p=7.7×10−22) associated with hair, eye and skin colour, freckling, tanning capacity and sun burning sensitivity and the Fitzpatrick phototype score, all highly correlated cross-phenotypes. Multitrait meta-analysis of anthropometric variation validated 27 loci in a two-stage meta-analysis with a large British ancestry cohort, six of which are newly reported here (p value threshold <5×10−9) at ZRANB2-AS2, PIK3R1, EPHA7, MAD1L1, CACUL1 and MAP3K9. Conclusion Considering multiple-related genetic phenotypes improve associated genome signal detection. These results indicate the potential value of data-driven multivariate phenotyping for genetic studies in large population-based cohorts to contribute to knowledge of complex traits.This work was supported in part by the Spanish Ministerio de Economía y Competitividad (MINECO) project ADE 10/00026, by the Catalan Departament de Salut and by the Departament d’Empresa i Coneixement de la Generalitat de Catalunya, the Agència de Gestió d’Estudis Universitaris i de Recerca (AGA UR) (SGR 1269, SGR 1589 and SGR 647). RdC is the recipient of a Ramon y Cajal grant (RYC-2011-07822). The Project GCAT is coordinated by the Germans Trias i Pujol Research Institute (IGTP), in collaboration with the Catalan Institute of Oncology (ICO), and in partnership with the Blood and Tissue Bank of Catalonia (BST). IGTP is part of the CERCA Programme/Generalitat de Catalunya.Peer ReviewedPostprint (published version

Multitrait genome association analysis identifies new susceptibility genes for human anthropometric variation in the GCAT cohort

BACKGROUND: Heritability estimates have revealed an important contribution of SNP variants for most common traits; however, SNP analysis by single-trait genome-wide association studies (GWAS) has failed to uncover their impact. In this study, we applied a multitrait GWAS approach to discover additional factor of the missing heritability of human anthropometric variation. METHODS: We analysed 205 traits, including diseases identified at baseline in the GCAT cohort (Genomes For Life- Cohort study of the Genomes of Catalonia) (n=4988), a Mediterranean adult population-based cohort study from the south of Europe. We estimated SNP heritability contribution and single-trait GWAS for all traits from 15 million SNP variants. Then, we applied a multitrait-related approach to study genome-wide association to anthropometric measures in a two-stage meta-analysis with the UK Biobank cohort (n=336 107). RESULTS: Heritability estimates (eg, skin colour, alcohol consumption, smoking habit, body mass index, educational level or height) revealed an important contribution of SNP variants, ranging from 18% to 77%. Single-trait analysis identified 1785 SNPs with genome-wide significance threshold. From these, several previously reported single-trait hits were confirmed in our sample with LINC01432 (p=1.9×10-9) variants associated with male baldness, LDLR variants with hyperlipidaemia (ICD-9:272) (p=9.4×10-10) and variants in IRF4 (p=2.8×10-57), SLC45A2 (p=2.2×10-130), HERC2 (p=2.8×10-176), OCA2 (p=2.4×10-121) and MC1R (p=7.7×10-22) associated with hair, eye and skin colour, freckling, tanning capacity and sun burning sensitivity and the Fitzpatrick phototype score, all highly correlated cross-phenotypes. Multitrait meta-analysis of anthropometric variation validated 27 loci in a two-stage meta-analysis with a large British ancestry cohort, six of which are newly reported here (p value threshold <5×10-9) at ZRANB2-AS2, PIK3R1, EPHA7, MAD1L1, CACUL1 and MAP3K9. CONCLUSION: Considering multiple-related genetic phenotypes improve associated genome signal detection. These results indicate the potential value of data-driven multivariate phenotyping for genetic studies in large population-based cohorts to contribute to knowledge of complex traits

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

Immune Cell Associations with Cancer Risk.

Proper immune system function hinders cancer development, but little is known about whether genetic variants linked to cancer risk alter immune cells. Here, we report 57 cancer risk loci associated with differences in immune and/or stromal cell contents in the corresponding tissue. Predicted target genes show expression and regulatory associations with immune features. Polygenic risk scores also reveal associations with immune and/or stromal cell contents, and breast cancer scores show consistent results in normal and tumor tissue. SH2B3 links peripheral alterations of several immune cell types to the risk of this malignancy. Pleiotropic SH2B3 variants are associated with breast cancer risk in BRCA1/2 mutation carriers. A retrospective case-cohort study indicates a positive association between blood counts of basophils, leukocytes, and monocytes and age at breast cancer diagnosis. These findings broaden our knowledge of the role of the immune system in cancer and highlight promising prevention strategies for individuals at high risk