Biology subjects

Vazquez, A. I.

Publications and source records attributed to Vazquez, A. I..

4 recordsLinked to original sources

bioRxiv2020

Analysis of variance when both input and output sets are high-dimensional

MotivationModern genomic data sets often involve multiple data-layers (e.g., DNA-sequence, gene expression), each of which itself can be high-dimensional. The biological processes underlying these data-layers can lead to intricate multivariate association patterns. ResultsWe propose and evaluate two methods for analysis variance when both input and output sets are high-dimensional. Our approach uses random effects models to estimate the proportion of variance of vectors in the linear span of the output set that can be explained by regression on the input set. We consider a method based on orthogonal basis (Eigen-ANOVA) and one that uses random vectors (Monte Carlo ANOVA, MC-ANOVA) in the linear span of the output set. We used simulations to assess the bias and variance of each of the methods, and to compare it with that of the Partial Least Squares (PLS)-an approach commonly used in multivariate-high-dimensional regressions. The MC-ANOVA method gave nearly unbiased estimates in all the simulation scenarios considered. Estimates produced by Eigen-ANOVA and PLS had noticeable biases. Finally, we demonstrate insight that can be obtained with the of MC-ANOVA and Eigen-ANOVA by applying these two methods to the study of multi-locus linkage disequilibrium in chicken genomes and to the assessment of inter-dependencies between gene expression, methylation and copy-number-variants in data from breast cancer tumors. AvailabilityThe Supplementary data includes an R-implementation of each of the proposed methods as well as the scripts used in simulations and in the real-data analyses. Contactgustavoc@msu.edu Supplementary informationSupplementary data are available at Bioinformatics online.

genomics↗

bioRxiv2019

Multi-omic signatures identify pan-cancer classes of tumors beyond tissue of origin.

Despite recent advances in treatment, cancer continues to be one of the most lethal human maladies. One of the challenges of cancer treatment is the extreme diversity among seemingly identical tumors: while some tumors may have good prognosis and are treatable, others are quite aggressive, and may lack of effective therapies. Most of this variability comes from wide-spread mutations and epigenetic alterations. Using a novel omic-integration method, we have exploited this molecular information to re-classify tumors beyond the constraints of cell type. Eight novel tumor groups (C1-8) emerged, characterized by unique cancer signatures. C3 had better prognosis, genome stability, and immune infiltration. C2 and C5 had higher genome instability and poorer clinical outcomes. Remaining clusters were characterized by worse outcomes, along with higher genome instability. C1, C7, and C8 were upregulated for cellular and mitochondrial translation, and relatively low proliferation. C6 and C4 were also downregulated for cellular and mitochondrial translation, and had high proliferation rates. C4 was represented by copy losses on chromosome 6, and had the highest number of metastatic samples. C8 was characterized by copy losses on chromosome 11, having also the lowest lymphocytic infiltration rate. C6 had the lowest natural killer infiltration rate and was represented by copy gains of genes in chromosome 11. C7 was represented by copy gains on chromosome 6, and had the highest upregulation in mitochondrial translation. We believe that, since molecularly alike tumors could respond similarly to treatment, our results could inform therapeutic action.\n\nSignificanceCancer has been traditionally studied as a family of different diseases from different anatomical sites. Nevertheless, regardless of the tissue of origin, cancer can be characterized by molecular alterations on mechanisms controlling cell fate and progression. In this study, we integrate 33 cancer types and show the existence of eight clusters with unique genomic signatures and clinical characteristics, beyond the site of origin of the tumor. The study and treatment of cancer, based on predominant molecular features, rather than site of origin, can potentially aid in the discovery of novel therapeutic alternatives.

genomics↗

bioRxiv2019

Deciphering sex-specific genetic architectures using local Bayesian regressions

Many complex human traits exhibit differences between sexes. While numerous factors likely contribute to this phenomenon, growing evidence from genome-wide studies suggest a partial explanation: that males and females from the same population possess differing genetic architectures. Despite this, mapping gene-by-sex (GxS) interactions remains a challenge likely because the magnitude of such an interaction is typically and exceedingly small; traditional genome-wide association techniques may be underpowered to detect such events partly due to the burden of multiple test correction. Here, we developed a local Bayesian regression (LBR) method to estimate sex-specific SNP marker effects after fully accounting for local linkage-disequilibrium (LD) patterns. This enabled us to infer sex-specific effects and GxS interactions either at the single SNP level, or by aggregating the effects of multiple SNPs to make inferences at the level of small LD-based regions. Using simulations in which there was imperfect LD between SNPs and causal variants, we showed that aggregating sex-specific marker effects with LBR provides improved power and resolution to detect GxS interactions over traditional single-SNP-based tests. When using LBR to analyze traits from the UK Biobank, we detected a relatively large GxS interaction impacting bone-mineral density within ABO and replicated many previously detected large-magnitude GxS interactions impacting waist-to-hip ratio. We also discovered many new GxS interactions impacting such traits as height and BMI within regions of the genome where both male- and female-specific effects explain a small proportion of phenotypic variance (R2 < 1x10-4), but are enriched in known expression quantitative trait loci. By combining biobank-level data and techniques to estimate sex-specific SNP effects after accounting for local-LD patterns, we are providing evidence that numerous small-magnitude GxS interactions exist to influence sex differences in a variety of complex traits.\n\nAuthor SummaryMany complex human traits are known to be influenced by an impressive number of causal variants each with very small effects, posing great challenges for genome-wide association studies (GWAS). To add to this challenge, many causal variants may possess context-dependent effects such as effects that are dependent on biological sex. While GWAS are commonly performed using specific methods in which one single nucleotide polymorphism (SNP) at a time is tested for association with a trait, alternatively we utilize methods more commonly observed in the genomic prediction literature. Such methods are advantageous in that they are not burdened by multiple test correction in the same way as traditional GWAS techniques are, and can fully account for linkage-disequilibrium patterns to accurately estimate the true effects of SNP markers. Here we adapt such methods to estimate genetic effects within sexes and provide a powerful means to compare sex-specific genetic effects.

genetics↗

bioRxiv2018

Quantifying Heterogeneity in the Genetic Architecture of Complex Traits Between Ethnically Diverse Groups using Random Effect Interaction Models

In humans, most genome-wide association studies have been conducted using data from Caucasians and many of the reported findings have not replicated in other populations. This lack of replication may be due to statistical issues (small sample size, confounding) or perhaps more fundamentally to differences in the genetic architecture of traits between ethnically diverse subpopulations. What aspects of the genetic architecture of traits vary between subpopulations and how can this be quantified? We consider studying effect heterogeneity using random-effect Bayesian interaction models. The proposed methodology can be applied using shrinkage and variable selection methods and produces useful information about effect heterogeneity in the form of whole-genome summaries (e.g., SNP-heritability and the average correlation of effects) as well as SNP-specific attributes. Using simulations, we show that the proposed methodology yields (nearly) unbiased estimates of genomic heritability and of the average correlation of effects between groups when the sample size is not too small relative to the number of SNPs used. Subsequently, we used the proposed methodology for the analyses of four complex human traits (standing height, high-density lipoprotein, low-density lipoprotein, and serum urate levels) in European-Americans (EAs) and African-Americans (AAs). The estimated correlations of effects between the two subpopulations was well below unity for all the traits, ranging from 0.73 to 0.50. The extent of effect heterogeneity varied between traits and SNP-sets. Height showed less differences in SNP effects between AAs and EAs whereas HDL, a trait highly influenced by life-style, exhibited greater extent of effect heterogeneity. For all the traits we observed substantial variability in effect heterogeneity across SNPs, suggesting it varies between regions of the genome.

genetics↗