Genetic and epigenetic contribution to complex traits

Much of the recent advances in functional genomics owe to developments in next-generation sequencing technology, which has contributed to the exponential increase of genomic data available for different human disease and population samples. With functional sequencing assays available to query both the transcriptome and the epigenome, annotation of the non-coding, regulatory genome is steadily improving and providing means to interpret the functional consequences of genetic variants associated with human complex traits. This has highlighted the need to better understand the normal variation in various cellular phenotypes, such as epigenetic modifications, and their transgenerational inheritance. In this review, we discuss different aspects of epigenetic variation in the context of DNA sequence variation and its contribution to complex phenotype

Evolutionary history of regulatory variation in human populations

Genetic variation in the regulation of gene expression is likely to be a major contributor to phenotypic variation in humans, and it also constitutes an important target of recent natural selection in human populations and plays a major role in morphological evolution. The increasing amount of data of genome and transcriptome variation is now leading to a better annotation of regulatory elements and a growing understanding of how the evolution of gene regulation has shaped human diversity. In this review, we discuss the evolutionary history of the variation in the expression of protein-coding genes in humans. We outline the current methodology for mapping regulatory variants and their distribution in human populations. General mechanisms of regulatory evolution are discussed with a special emphasis on different selective processes targeting gene regulation in human

The resolution of the genetics of gene expression

Understanding the influence of genetics on the molecular mechanisms underpinning human phenotypic diversity is fundamental to being able to predict health outcomes and treat disease. To interrogate the role of genetics on cellular state and function, gene expression has been extensively used. Past and present studies have highlighted important patterns of heritability, population differentiation and tissue-specificity in gene expression. Current and future studies are taking advantage of systems biology-based approaches and advances in sequencing technology: new methodology aims to translate regulatory networks to enrich pathways responsible for disease etiology and 2nd generation sequencing now offers single-molecular resolution of the transcriptome providing unprecedented information on the structural and genetic characteristics of gene expression. Such advances are leading to a future where rich cellular phenotypes will facilitate understanding of the transmission of genetic effect from the gene to organis

On Quality Control Measures in Genome-wide Association Studies: A Test to Assess the Genotyping Quality of Individual Probands in Family-based Association Studies and an Application to the HapMap Data

Allele transmissions in pedigrees provide a natural way of evaluating the genotyping quality of a particular proband in a family-based, genome-wide association study. We propose a transmission test that is based on this feature and that can be used for quality control filtering of genome-wide genotype data for individual probands. The test has one degree of freedom and assesses the average genotyping error rate of the genotyped SNPs for a particular proband. As we show in simulation studies, the test is sufficiently powerful to identify probands with an unreliable genotyping quality that cannot be detected with standard quality control filters. This feature of the test is further exemplified by an application to the third release of the HapMap data. The test is ideally suited as the final layer of quality control filters in the cleaning process of genome-wide association studies. It identifies probands with insufficient genotyping quality that were not removed by standard quality control filtering

Needed for completion of the human genome: hypothesis driven experiments and biologically realistic mathematical models

With the sponsorship of ``Fundacio La Caixa'' we met in Barcelona, November 21st and 22nd, to analyze the reasons why, after the completion of the human genome sequence, the identification all protein coding genes and their variants remains a distant goal. Here we report on our discussions and summarize some of the major challenges that need to be overcome in order to complete the human gene catalog.Comment: Report and discussion resulting from the `Fundacio La Caixa' gene finding meeting held November 21 and 22 2003 in Barcelon

Rates of SARS-COV-2 transmission and vaccination impact the fate of vaccine-resistant strains

Se considera que las vacunas son la mejor solución para controlar la actual pandemia por SARS-CoV-2. Sin embargo, la proliferación de cepas resistentes a las vacunas puede ser demasiado rápida para que su aplicación alivie la propagación de la pandemia, así como sus consecuencias económicas y sociales. Para cuantificar y caracterizar el riesgo de este escenario, utilizamos un modelo SIR con una dinámica estocástica para estudiar la probabilidad de aparición y transmisión de cepas resistentes a la vacuna. Usando parámetros que repliquen de manera realista la transmisión del SARS-CoV-2, modelizamos el patrón en forma de olas de la pandemia y consideramos el impacto que el ritmo de vacunación y la intensidad de las medidas de contención adoptadas tienen sobre la probabilidad de aparición de cepas resistentes a la vacuna. Como era de esperar, un ritmo rápido de vacunación disminuye la probabilidad de aparición de una cepa resistente a la vacuna. Sin embargo, aunque en principio pueda parecer contraintuitivo, cuando se produce una relajación de las restricciones en el momento en el que la mayoría de la población ya ha sido vacunada, la probabilidad de aparición de una cepa resistente a la vacuna aumenta considerablemente. En consecuencia, un período de contención estricta de la transmisión cerca del final de la campaña de vacunación puede reducir sustancialmente la probabilidad del establecimiento de cepas resistentes a la vacuna. Estos resultados, por tanto, sugieren la conveniencia de mantener las medidas y los protocolos de prevención durante toda la duración de la campaña de vacunación.Vaccines are thought to be the best available solution for controlling the ongoing SARS-CoV-2 pandemic. However, the emergence of vaccine-resistant strains may come too rapidly for current vaccine developments to alleviate the health, economic and social consequences of the pandemic. To quantify and characterize the risk of such a scenario, we created a SIR-derived model with initial stochastic dynamics of the vaccine-resistant strain to study the probability of its emergence and establishment. Using parameters realistically resembling SARS-CoV-2 transmission, we model a wave-like pattern of the pandemic and consider the impact of the rate of vaccination and the strength of non-pharmaceutical intervention measures on the probability of emergence of a resistant strain. As expected, we found that a fast rate of vaccination decreases the probability of emergence of a resistant strain. Counterintuitively, when a relaxation of non-pharmaceutical interventions happened at a time when most individuals of the population have already been vaccinated the probability of emergence of a resistant strain was greatly increased. Consequently, we show that a period of transmission reduction close to the end of the vaccination campaign can substantially reduce the probability of resistant strain establishment. These results, therefore, suggest the convenience of maintaining non-pharmaceutical interventions and prevention protocols throughout the entire vaccination period

Rare and Common Regulatory Variation in Population-Scale Sequenced Human Genomes

Population-scale genome sequencing allows the characterization of functional effects of a broad spectrum of genetic variants underlying human phenotypic variation. Here, we investigate the influence of rare and common genetic variants on gene expression patterns, using variants identified from sequencing data from the 1000 genomes project in an African and European population sample and gene expression data from lymphoblastoid cell lines. We detect comparable numbers of expression quantitative trait loci (eQTLs) when compared to genotypes obtained from HapMap 3, but as many as 80% of the top expression quantitative trait variants (eQTVs) discovered from 1000 genomes data are novel. The properties of the newly discovered variants suggest that mapping common causal regulatory variants is challenging even with full resequencing data; however, we observe significant enrichment of regulatory effects in splice-site and nonsense variants. Using RNA sequencing data, we show that 46.2% of nonsynonymous variants are differentially expressed in at least one individual in our sample, creating widespread potential for interactions between functional protein-coding and regulatory variants. We also use allele-specific expression to identify putative rare causal regulatory variants. Furthermore, we demonstrate that outlier expression values can be due to rare variant effects, and we approximate the number of such effects harboured in an individual by effect size. Our results demonstrate that integration of genomic and RNA sequencing analyses allows for the joint assessment of genome sequence and genome function

Genome-Wide Expression of Azoospermia Testes Demonstrates a Specific Profile and Implicates ART3 in Genetic Susceptibility

Infertility affects about one in six couples attempting pregnancy, with the man responsible in approximately half of the cases. Because the pathophysiology underlying azoospermia is not elucidated, most male infertility is diagnosed as idiopathic. Genome-wide gene expression analyses with microarray on testis specimens from 47 non-obstructive azoospermia (NOA) and 11 obstructive azoospermia (OA) patients were performed, and 2,611 transcripts that preferentially included genes relevant to gametogenesis and reproduction according to Gene Ontology classification were found to be differentially expressed. Using a set of 945 of the 2,611 transcripts without missing data, NOA was further categorized into three classes using the non-negative matrix factorization method. Two of the three subclasses were different from the OA group in Johnsen's score, FSH level, and/or LH level, while there were no significant differences between the other subclass and the OA group. In addition, the 52 genes showing high statistical difference between NOA subclasses (p < 0.01 with Tukey's post hoc test) were subjected to allelic association analyses to identify genetic susceptibilities. After two rounds of screening, SNPs of the ADP-ribosyltransferase 3 gene (ART3) were associated with NOA with highest significance with ART3-SNP25 (rs6836703; p = 0.0025) in 442 NOA patients and 475 fertile men. Haplotypes with five SNPs were constructed, and the most common haplotype was found to be under-represented in patients (NOA 26.6% versus control 35.3%, p = 0.000073). Individuals having the most common haplotype showed an elevated level of testosterone, suggesting a protective effect of the haplotype on spermatogenesis. Thus, genome-wide gene expression analyses were used to identify genes involved in the pathogenesis of NOA, and ART3 was subsequently identified as a susceptibility gene for NOA. These findings clarify the molecular pathophysiology of NOA and suggest a novel therapeutic target in the treatment of NOA

Genotype-Based Test in Mapping Cis-Regulatory Variants from Allele-Specific Expression Data

Identifying and understanding the impact of gene regulatory variation is of considerable importance in evolutionary and medical genetics; such variants are thought to be responsible for human-specific adaptation [1] and to have an important role in genetic disease. Regulatory variation in cis is readily detected in individuals showing uneven expression of a transcript from its two allelic copies, an observation referred to as allelic imbalance (AI). Identifying individuals exhibiting AI allows mapping of regulatory DNA regions and the potential to identify the underlying causal genetic variant(s). However, existing mapping methods require knowledge of the haplotypes, which make them sensitive to phasing errors. In this study, we introduce a genotype-based mapping test that does not require haplotype-phase inference to locate regulatory regions. The test relies on partitioning genotypes of individuals exhibiting AI and those not expressing AI in a 2×3 contingency table. The performance of this test to detect linkage disequilibrium (LD) between a potential regulatory site and a SNP located in this region was examined by analyzing the simulated and the empirical AI datasets. In simulation experiments, the genotype-based test outperforms the haplotype-based tests with the increasing distance separating the regulatory region from its regulated transcript. The genotype-based test performed equally well with the experimental AI datasets, either from genome–wide cDNA hybridization arrays or from RNA sequencing. By avoiding the need of haplotype inference, the genotype-based test will suit AI analyses in population samples of unknown haplotype structure and will additionally facilitate the identification of cis-regulatory variants that are located far away from the regulated transcript

Assessing allele-specific expression across multiple tissues from RNA-seq read data

Motivation: RNA sequencing enables allele-specific expression (ASE) studies that complement standard genotype expression studies for common variants and, importantly, also allow measuring the regulatory impact of rare variants. The Genotype-Tissue Expression (GTEx) project is collecting RNA-seq data on multiple tissues of a same set of individuals and novel methods are required for the analysis of these data. Results: We present a statistical method to compare different patterns of ASE across tissues and to classify genetic variants according to their impact on the tissue-wide expression profile. We focus on strong ASE effects that we are expecting to see for protein-truncating variants, but our method can also be adjusted for other types of ASE effects. We illustrate the method with a real data example on a tissue-wide expression profile of a variant causal for lipoid proteinosis, and with a simulation study to assess our method more generally. Availability and implementation: http://www.well.ox.ac.uk/~rivas/mamba/. R-sources and data examples http://www.iki.fi/mpirinen/ Contact: [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin