Dataset Information

The Medical Genome Reference Bank contains whole genome and phenotype data of 2570 healthy elderly.

ABSTRACT: Population health research is increasingly focused on the genetic determinants of healthy ageing, but there is no public resource of whole genome sequences and phenotype data from healthy elderly individuals. Here we describe the first release of the Medical Genome Reference Bank (MGRB), comprising whole genome sequence and phenotype of 2570 elderly Australians depleted for cancer, cardiovascular disease, and dementia. We analyse the MGRB for single-nucleotide, indel and structural variation in the nuclear and mitochondrial genomes. MGRB individuals have fewer disease-associated common and rare germline variants, relative to both cancer cases and the gnomAD and UK Biobank cohorts, consistent with risk depletion. Age-related somatic changes are correlated with grip strength in men, suggesting blood-derived whole genomes may also provide a biologic measure of age-related functional deterioration. The MGRB provides a broadly applicable reference cohort for clinical genetics and genomic association studies, and for understanding the genetics of healthy ageing.

SUBMITTER: Pinese M

PROVIDER: S-EPMC6978518 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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The Medical Genome Reference Bank contains whole genome and phenotype data of 2570 healthy elderly.

Pinese Mark M Lacaze Paul P Rath Emma M EM Stone Andrew A Brion Marie-Jo MJ Ameur Adam A Nagpal Sini S Puttick Clare C Husson Shane S Degrave Dmitry D Cristina Tina Navin TN Kahl Vivian F S VFS Statham Aaron L AL Woods Robyn L RL McNeil John J JJ Riaz Moeen M Barr Margo M Nelson Mark R MR Reid Christopher M CM Murray Anne M AM Shah Raj C RC Wolfe Rory R Atkins Joshua R JR Fitzsimmons Chantel C Cairns Heath M HM Green Melissa J MJ Carr Vaughan J VJ Cowley Mark J MJ Pickett Hilda A HA James Paul A PA Powell Joseph E JE Kaplan Warren W Gibson Greg G Gyllensten Ulf U Cairns Murray J MJ McNamara Martin M Dinger Marcel E ME Thomas David M DM

Nature communications 20200123 1

Population health research is increasingly focused on the genetic determinants of healthy ageing, but there is no public resource of whole genome sequences and phenotype data from healthy elderly individuals. Here we describe the first release of the Medical Genome Reference Bank (MGRB), comprising whole genome sequence and phenotype of 2570 elderly Australians depleted for cancer, cardiovascular disease, and dementia. We analyse the MGRB for single-nucleotide, indel and structural variation in ...[more]

PMID: 31974348

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Project description:Genetic variation at the Human Leucocyte Antigen (HLA) genes is associated with many autoimmune and infectious disease phenotypes, is an important element of the immunological distinction between self and non-self, and shapes immune epitope repertoires. Determining the allelic state of the HLA genes (HLA typing) as a by-product of standard whole-genome sequencing data would therefore be highly desirable and enable the immunogenetic characterization of samples in currently ongoing population sequencing projects. Extensive hyperpolymorphism and sequence similarity between the HLA genes, however, pose problems for accurate read mapping and make HLA type inference from whole-genome sequencing data a challenging problem. We describe how to address these challenges in a Population Reference Graph (PRG) framework. First, we construct a PRG for 46 (mostly HLA) genes and pseudogenes, their genomic context and their characterized sequence variants, integrating a database of over 10,000 known allele sequences. Second, we present a sequence-to-PRG paired-end read mapping algorithm that enables accurate read mapping for the HLA genes. Third, we infer the most likely pair of underlying alleles at G group resolution from the IMGT/HLA database at each locus, employing a simple likelihood framework. We show that HLA*PRG, our algorithm, outperforms existing methods by a wide margin. We evaluate HLA*PRG on six classical class I and class II HLA genes (HLA-A, -B, -C, -DQA1, -DQB1, -DRB1) and on a set of 14 samples (3 samples with 2 x 100bp, 11 samples with 2 x 250bp Illumina HiSeq data). Of 158 alleles tested, we correctly infer 157 alleles (99.4%). We also identify and re-type two erroneous alleles in the original validation data. We conclude that HLA*PRG for the first time achieves accuracies comparable to gold-standard reference methods from standard whole-genome sequencing data, though high computational demands (currently ~30-250 CPU hours per sample) remain a significant challenge to practical application.

Dataset Information

The Medical Genome Reference Bank contains whole genome and phenotype data of 2570 healthy elderly.

Publications

The Medical Genome Reference Bank contains whole genome and phenotype data of 2570 healthy elderly.

Similar Datasets

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