Timothy Hefferon

Bio: Timothy Hefferon is an academic researcher from National Institutes of Health. The author has contributed to research in topics: Genomics & Liver transplantation. The author has an hindex of 7, co-authored 11 publications receiving 11045 citations. Previous affiliations of Timothy Hefferon include European Bioinformatics Institute.

A global reference for human genetic variation.

Abstract: The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.

A global reference for human genetic variation

Abstract: The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.

dbVar and DGVa: public archives for genomic structural variation

Abstract: Much has changed in the last two years at DGVa (http://www.ebi.ac.uk/dgva) and dbVar (http://www.ncbi.nlm.nih.gov/dbvar). We are now processing direct submissions rather than only curating data from the literature and our joint study catalog includes data from over 100 studies in 11 organisms. Studies from human dominate with data from control and case populations, tumor samples as well as three large curated studies derived from multiple sources. During the processing of these data, we have made improvements to our data model, submission process and data representation. Additionally, we have made significant improvements in providing access to these data via web and FTP interfaces.

A synonymous mutation in TCOF1 causes Treacher Collins syndrome due to mis-splicing of a constitutive exon†

Abstract: Interpretation of the pathogenicity of sequence alterations in disease-associated genes is challenging. This is especially true for novel alterations that lack obvious functional consequences. We report here on a patient with Treacher Collins syndrome (TCS) found to carry a previously reported mutation, c.122C > T, which predicts p.A41V, and a novel synonymous mutation, c.3612A > C. Pedigree analysis showed that the c.122C > T mutation segregated with normal phenotypes in multiple family members while the c.3612A > C was de novo in the patient. Analysis of TCOF1 RNA in lymphocytes showed a transcript missing exon 22. These results show that TCS in the patient is due to haploinsufficiency of TCOF1 caused by the synonymous de novo c.3612A > C mutation. This study highlights the importance of clinical and pedigree evaluation in the interpretation of known and novel sequence alterations.

Acquired pseudoxanthoma elasticum presenting after liver transplantation

Abstract: Background Pseudoxanthoma elasticum (PXE) is thought to be a metabolic disorder resulting from mutations in the gene encoding the cellular transporter, ABCC6, which is primarily expressed in liver and kidney. We encountered 3 patients who developed clinical and histopathological evidence of PXE after liver transplantation, suggesting that PXE could have been acquired from the transplanted organ. Objective We sought to delineate the clinical features and screen each patient and samples of donor liver for mutations in the ABCC6 gene. Methods Each patient underwent full clinical examination, skin biopsy, and ophthalmologic examination, and whole genome sequencing using standard techniques. Fixed samples of donor liver tissue were available for mutation analysis in two patients and of donor kidney tissue in one. Results All 3 patients had unequivocal clinical and histopathologic evidence of PXE. No patient (or family member available for screening) had evidence of mutations in ABCC6. Neither liver specimen nor the single available kidney specimen showed evidence of mutations in ABCC6. Limitations Liver tissue was not available from one patient and DNA was of poor quality in another, resulting in limited screening. Genetic testing does not detect ABCC6 mutations in 10% of patients with confirmed PXE. Conclusion Although we were unable to demonstrate ABCC6 mutations in limited screening of fixed donor livers, the absence of any PXE mutations in the affected patients, the timing of onset of PXE, and the known acquisition of other metabolic disorders and coagulopathies from donor livers suggest that PXE was likely acquired via liver transplantation.

Analysis of protein-coding genetic variation in 60,706 humans

Abstract: Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. Here we describe the aggregation and analysis of high-quality exome (protein-coding region) DNA sequence data for 60,706 individuals of diverse ancestries generated as part of the Exome Aggregation Consortium (ExAC). This catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We have used this catalogue to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; identifying 3,230 genes with near-complete depletion of predicted protein-truncating variants, with 72% of these genes having no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human 'knockout' variants in protein-coding genes.

Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype

Abstract: The human reference genome represents only a small number of individuals, which limits its usefulness for genotyping. We present a method named HISAT2 (hierarchical indexing for spliced alignment of transcripts 2) that can align both DNA and RNA sequences using a graph Ferragina Manzini index. We use HISAT2 to represent and search an expanded model of the human reference genome in which over 14.5 million genomic variants in combination with haplotypes are incorporated into the data structure used for searching and alignment. We benchmark HISAT2 using simulated and real datasets to demonstrate that our strategy of representing a population of genomes, together with a fast, memory-efficient search algorithm, provides more detailed and accurate variant analyses than other methods. We apply HISAT2 for HLA typing and DNA fingerprinting; both applications form part of the HISAT-genotype software that enables analysis of haplotype-resolved genes or genomic regions. HISAT-genotype outperforms other computational methods and matches or exceeds the performance of laboratory-based assays. A graph-based genome indexing scheme enables variant-aware alignment of sequences with very low memory requirements.

The Ensembl Variant Effect Predictor.

Abstract: The Ensembl Variant Effect Predictor is a powerful toolset for the analysis, annotation, and prioritization of genomic variants in coding and non-coding regions. It provides access to an extensive collection of genomic annotation, with a variety of interfaces to suit different requirements, and simple options for configuring and extending analysis. It is open source, free to use, and supports full reproducibility of results. The Ensembl Variant Effect Predictor can simplify and accelerate variant interpretation in a wide range of study designs.

The UK Biobank resource with deep phenotyping and genomic data

Abstract: The UK Biobank project is a prospective cohort study with deep genetic and phenotypic data collected on approximately 500,000 individuals from across the United Kingdom, aged between 40 and 69 at recruitment. The open resource is unique in its size and scope. A rich variety of phenotypic and health-related information is available on each participant, including biological measurements, lifestyle indicators, biomarkers in blood and urine, and imaging of the body and brain. Follow-up information is provided by linking health and medical records. Genome-wide genotype data have been collected on all participants, providing many opportunities for the discovery of new genetic associations and the genetic bases of complex traits. Here we describe the centralized analysis of the genetic data, including genotype quality, properties of population structure and relatedness of the genetic data, and efficient phasing and genotype imputation that increases the number of testable variants to around 96 million. Classical allelic variation at 11 human leukocyte antigen genes was imputed, resulting in the recovery of signals with known associations between human leukocyte antigen alleles and many diseases.

Genetic effects on gene expression across human tissues.

Abstract: Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease.