A Quadratically Regularized Functional Canonical Correlation Analysis for Identifying the Global Structure of Pleiotropy with NGS Data

Investigating the pleiotropic effects of genetic variants can increase statistical power, provide important information to achieve deep understanding of the complex genetic structures of disease, and offer powerful tools for designing effective treatments with fewer side effects. However, the current multiple phenotype association analysis paradigm lacks breadth (number of phenotypes and genetic variants jointly analyzed at the same time) and depth (hierarchical structure of phenotype and genotypes). A key issue for high dimensional pleiotropic analysis is to effectively extract informative internal representation and features from high dimensional genotype and phenotype data. To explore multiple levels of representations of genetic variants, learn their internal patterns involved in the disease development, and overcome critical barriers in advancing the development of novel statistical methods and computational algorithms for genetic pleiotropic analysis, we proposed a new framework referred to as a quadratically regularized functional CCA (QRFCCA) for association analysis which combines three approaches: (1) quadratically regularized matrix factorization, (2) functional data analysis and (3) canonical correlation analysis (CCA). Large-scale simulations show that the QRFCCA has a much higher power than that of the nine competing statistics while retaining the appropriate type 1 errors. To further evaluate performance, the QRFCCA and nine other statistics are applied to the whole genome sequencing dataset from the TwinsUK study. We identify a total of 79 genes with rare variants and 67 genes with common variants significantly associated with the 46 traits using QRFCCA. The results show that the QRFCCA substantially outperforms the nine other statistics.Comment: 64 pages including 12 figure

Multiple Sporadic Colorectal Cancers Display a Unique Methylation Phenotype

The members of the Gastrointestinal Oncology Group of the Spanish Gastroenterological Association are: Hospital 12 de Octubre, Madrid: Juan Diego Morillas (local coordinator), Raquel Muñoz, Marisa Manzano, Francisco Colina, Jose Díaz, Carolina Ibarrola, Guadalupe López, Alberto Ibáñez; Hospital Clínic, Barcelona: Antoni Castells (local coordinator), Virgínia Piñol, Sergi Castellví-Bel, Francesc Balaguer, Victoria Gonzalo, Teresa Ocaña, María Dolores Giráldez, Maria Pellisé, Anna Serradesanferm, Leticia Moreira, Miriam Cuatrecasas, Josep M. Piqué; Hospital Clínico Universitario, Zaragoza: Ángel Lanas (local coordinator), Javier Alcedo, Javier Ortego; Hospital Cristal-Piñor, Complexo Hospitalario de Ourense: Joaquin Cubiella (local coordinator), Ma Soledad Díez, Mercedes Salgado, Eloy Sánchez, Mariano Vega; Hospital del Mar, Barcelona: Montserrat Andreu (local coordinator), Anna Abuli, Xavier Bessa, Mar Iglesias, Agustín Seoane, Felipe Bory, Gemma Navarro, Beatriz Bellosillo, Josep Ma Dedeu, Cristina Álvarez, Begoña Gonzalez; Hospital San Eloy, Baracaldo and Hospital Donostia, CIBERehd, University of Country Basque, San Sebastián: Luis Bujanda (local coordinator) Ángel Cosme, Inés Gil, Mikel Larzabal, Carlos Placer, María del Mar Ramírez, Elisabeth Hijona, Jose M. Enríquez-Navascués, Jose L. Elosegui; Hospital General Universitario de Alicante: Artemio Payá (EPICOLON I local coordinator), Rodrigo Jover (EPICOLON II local coordinator), Cristina Alenda, Laura Sempere, Nuria Acame, Estefanía Rojas, Lucía Pérez-Carbonell; Hospital General de Granollers: Joaquim Rigau (local coordinator), Ángel Serrano, Anna Giménez; Hospital General de Vic: Joan Saló (local coordinator), Eduard Batiste-Alentorn, Josefina Autonell, Ramon Barniol; Hospital General Universitario de Guadalajara and Fundación para la Formación e Investigación Sanitarias Murcia: Ana María García (local coordinator), Fernando Carballo, Antonio Bienvenido, Eduardo Sanz, Fernando González, Jaime Sánchez, Akiko Ono; Hospital General Universitario de Valencia: Mercedes Latorre (local coordinator), Enrique Medina, Jaime Cuquerella, Pilar Canelles, Miguel Martorell, José Ángel García, Francisco Quiles, Elisa Orti; CHUVI-Hospital Meixoeiro, Vigo: EPICOLON I: Juan Clofent (local coordinator), Jaime Seoane, Antoni Tardío, Eugenia Sanchez; EPICOLON II: Ma Luisa de Castro (local coordinator), Antoni Tardío, Juan Clofent, Vicent Hernández; Hospital Universitari Germans Trias i Pujol, Badalona and Section of Digestive Diseases and Nutrition, University of Illinois at Chicago, Chicago, IL: Xavier Llor (local coordinator), Rosa M. Xicola, Marta Piñol, Mercè Rosinach, Anna Roca, Elisenda Pons, José M. Hernández, Miquel A. Gassull; Hospital Universitari Mútua de Terrassa: Fernando Fernández-Bañares (local coordinator), Josep M. Viver, Antonio Salas, Jorge Espinós, Montserrat Forné, Maria Esteve; Hospital Universitari Arnau de Vilanova, Lleida: Josep M. Reñé (local coordinator), Carmen Piñol, Juan Buenestado, Joan Viñas; Hospital Universitario de Canarias: Enrique Quintero (local coordinator), David Nicolás, Adolfo Parra, Antonio Martín; Hospital Universitario La Fe, Valencia: Lidia Argüello (local coordinator), Vicente Pons, Virginia Pertejo, Teresa Sala; Hospital Sant Pau, Barcelona: Dolors Gonzalez (local coordinator), Eva Roman, Teresa Ramon, Maria Poca, Ma Mar Concepción, Marta Martin, Lourdes Pétriz; Hospital Xeral Cies, Vigo: Daniel Martinez (local coordinator); Fundacion Publica Galega de Medicina Xenomica (FPGMX), CIBERER, Genomic Medicine Group-University of Santiago de Compostela, Santiago de Compostela, Galicia, Spain: Ángel Carracedo (local coordinator), Clara Ruiz-Ponte, Ceres Fernández-Rozadilla, Ma Magdalena Castro; Hospital Universitario Central de Asturias: Sabino Riestra (local coordinator), Luis Rodrigo; Hospital de Galdácano, Vizcaya: Javier Fernández (local coordinator), Jose Luis Cabriada; Fundación Hospital de Calahorra (La Rioja) La Rioja: Luis Carreño (local coordinator), Susana Oquiñena, Federico Bolado; Hospital Royo Villanova, Zaragoza: Elena Peña (local coordinator), José Manuel Blas, Gloria Ceña, Juan José Sebastián; Hospital Universitario Reina Sofía, Córdoba: Antonio Naranjo (local coordinator).Epigenetics are thought to play a major role in the carcinogenesis of multiple sporadic colorectal cancers (CRC). Previous studies have suggested concordant DNA hypermethylation between tumor pairs. However, only a few methylation markers have been analyzed. This study was aimed at describing the epigenetic signature of multiple CRC using a genome-scale DNA methylation profiling. We analyzed 12 patients with synchronous CRC and 29 age-, sex-, and tumor location-paired patients with solitary tumors from the EPICOLON II cohort. DNA methylation profiling was performed using the Illumina Infinium HM27 DNA methylation assay. The most significant results were validated by Methylight. Tumors samples were also analyzed for the CpG Island Methylator Phenotype (CIMP); KRAS and BRAF mutations and mismatch repair deficiency status. Functional annotation clustering was performed. We identified 102 CpG sites that showed significant DNA hypermethylation in multiple tumors with respect to the solitary counterparts (difference in β value ≥0.1). Methylight assays validated the results for 4 selected genes (p = 0.0002). Eight out of 12(66.6%) multiple tumors were classified as CIMP-high, as compared to 5 out of 29(17.2%) solitary tumors (p = 0.004). Interestingly, 76 out of the 102 (74.5%) hypermethylated CpG sites found in multiple tumors were also seen in CIMP-high tumors. Functional analysis of hypermethylated genes found in multiple tumors showed enrichment of genes involved in different tumorigenic functions. In conclusion, multiple CRC are associated with a distinct methylation phenotype, with a close association between tumor multiplicity and CIMP-high. Our results may be important to unravel the underlying mechanism of tumor multiplicity.This work was supported by grants from the Hospital Clínic of Barcelona (Josep Font grant), Ministerio de Economí­a y Competitividad (SAF 2007-64873 and SAF2010-19273), Fundación Científica de la Asociación Española contra el Cáncer, and Instituto de Salud Carlos III (PI10/00384). “Cofinanciado por el Fondo Europeo de Desarrollo Regional (FEDER). Unión Europea. Una manera de hacer Europa”. CIBEREHD is funded by the Instituto de Salud Carlos III

The adiponectin paradox for all-cause and cardiovascular mortality

Basic science studies have shown beneficial effects of adiponectin on glucose homeostasis, chronic low-grade inflammation, apoptosis, oxidative stress, and atherosclerotic processes, so this molecule usually has been considered a salutary adipokine. It was therefore quite unexpected that large prospective human studies suggested that adiponectin is simply a marker of glucose homeostasis,with no direct favorable effect on the risk of type 2 diabetes and cardiovascular disease. But even more unforeseen were data addressing the role of adiponectin on the risk of death. In fact, a positive, rather than the expected negative, relationship was reported between adiponectin and mortality rate across many clinical conditions, comprising diabetes. The biology underlying this paradox is unknown. Several explanations have been proposed, including adiponectin resistance and the confounding role of natriuretic peptides. In addition, preliminary genetic evidence speaks in favor of a direct role of adiponectin in increasing the risk of death. However, none of these hypotheses are based on robust data, so further efforts are needed to unravel the elusive role of adiponectin on cardiometabolic health and, most important, its paradoxical association with mortality rate

Resilience effects of SGK1 and TAP1 DNA markers during PRRSV outbreaks in reproductive sows

The porcine reproductive and respiratory syndrome virus (PRRSV) is a major infectious stressor that causes serious health problems and productivity drops. Based on previous genome-wide analyses, we selected SGK1 and TAP1 as candidate genes for resilience, and genotyped three mutations, including a 3′UTR variant SGK1_rs338508371 and two synonymous variants TAP1_rs1109026889 and TAP1_rs80928141 in 305 Landrace × Large White sows. All polymorphisms affected the reproductive performance in the outbreak, but not during the endemic phase, thereby indicating a potential use of these markers for resilience. Moreover, some genotypes were associated with a stable performance across PRRSV phases. Thus, in the outbreak, the SGK1_rs338508371 AA sows had less piglets born alive (p < 0.0001) and more stillborns (p < 0.05) while other sows were able to keep their productivity. During the outbreak, TAP1_rs80928141 GG sows had less piglets born alive (p < 0.05) and both TAP1 polymorphisms influenced the number of mummies in an additive manner (p < 0.05). Remarkably, TAP1_rs80928141 AA sows had around one mummy more than GG sows (p < 0.01). Resilience to PRRSV could be improved by including the SGK1 and TAP1 markers in crossbreeding and/or selection schemes, as they contribute to maintaining a stable number of piglets born alive and lost, particularly mummies, despite the outbreak.This research and the APC were partially funded by FEDER projects COMRDI16-1-0035-03 and RTI2018-097700-B-I00 from the Spanish Ministry of Science, Innovation, and Universities. M.L. received a postdoctoral grant from UdL-Impuls programme

A rewiring model of intratumoral interaction networks.

Intratumoral heterogeneity (ITH) has been regarded as a key cause of the failure and resistance of cancer therapy, but how it behaves and functions remains unclear. Advances in single-cell analysis have facilitated the collection of a massive amount of data about genetic and molecular states of individual cancer cells, providing a fuel to dissect the mechanistic organization of ITH at the molecular, metabolic and positional level. Taking advantage of these data, we propose a computational model to rewire up a topological network of cell-cell interdependences and interactions that operate within a tumor mass. The model is grounded on the premise of game theory that each interactive cell (player) strives to maximize its fitness by pursuing a rational self-interest strategy, war or peace, in a way that senses and alters other cells to respond properly. By integrating this idea with genome-wide association studies for intratumoral cells, the model is equipped with a capacity to visualize, annotate and quantify how somatic mutations mediate ITH and the network of intratumoral interactions. Taken together, the model provides a topological flow by which cancer cells within a tumor cooperate or compete with each other to downstream pathogenesis. This topological flow can be potentially used as a blueprint for genetically intervening the pattern and strength of cell-cell interactions towards cancer control

Into the Wild: GWAS Exploration of Non-coding RNAs

Genome-wide association studies (GWAS) have proven a fundamental tool to identify common variants associated to complex traits, thus contributing to unveil the genetic components of human disease. Besides, the advent of GWAS contributed to expose unexpected findings that urged to redefine the framework of population genetics. First, loci identified by GWAS had small effect sizes and could only explain a fraction of the predicted heritability of the traits under study. Second, the majority of GWAS hits mapped within non-coding regions (such as intergenic or intronic regions) where new functional RNA species (such as lncRNAs or circRNAs) have started to emerge. Bigger cohorts, meta-analysis and technical improvements in genotyping allowed identification of an increased number of genetic variants associated to coronary artery disease (CAD) and cardiometabolic traits. The challenge remains to infer causal mechanisms by which these variants influence cardiovascular disease development. A tendency to assign potential causal variants preferentially to coding genes close to lead variants contributed to disregard the role of non-coding elements. In recent years, in parallel to an increased knowledge of the non-coding genome, new studies started to characterize disease-associated variants located within non-coding RNA regions. The upcoming of databases integrating single-nucleotide polymorphisms (SNPs) and non-coding RNAs together with novel technologies will hopefully facilitate the discovery of causal non-coding variants associated to disease. This review attempts to summarize the current knowledge of genetic variation within non-coding regions with a focus on long non-coding RNAs that have widespread impact in cardiometabolic diseases

Classification of life by the mechanism of genome size evolution

The classification of life should be based upon the fundamental mechanism in the evolution of life. We found that the global relationships among species should be circular phylogeny, which is quite different from the common sense based upon phylogenetic trees. The genealogical circles can be observed clearly according to the analysis of protein length distributions of contemporary species. Thus, we suggest that domains can be defined by distinguished phylogenetic circles, which are global and stable characteristics of living systems. The mechanism in genome size evolution has been clarified; hence main component questions on C-value enigma can be explained. According to the correlations and quasi-periodicity of protein length distributions, we can also classify life into three domains.Comment: 53 pages, 9 figures, 2 table