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.

DNA sequence information underpins genetic research, enabling discoveries of important biological or medical benefit. Sequencing projects have traditionally employed long (400–800 bp) reads, but the existence of reference sequences for the human and many other genomes makes it possible to develop new, fast approaches to re-sequencing, whereby shorter reads are compared to a reference to identify intra-species genetic variation. We report an approach that generates several billion bases of accurate nucleotide sequence per experiment at low cost. Single molecules of DNA are attached to a flat surface, amplified in situ and used as templates for synthetic sequencing with fluorescent reversible terminator deoxyribonucleotides. Images of the surface are analysed to generate high quality sequence. We demonstrate application of this approach to human genome sequencing on flow-sorted X chromosomes and then scale the approach to determine the genome sequence of a male Yoruba from Ibadan, Nigeria. We build an accurate consensus sequence from >30x average depth of paired 35-base reads. We characterise four million SNPs and four hundred thousand structural variants, many of which are previously unknown. Our approach is effective for accurate, rapid and economical whole genome re-sequencing and many other biomedical applications.

csaunders@illumina.com

SummaryThe Tasmanian devil (Sarcophilus harrisii), the largest marsupial carnivore, is endangered due to a transmissible facial cancer spread by direct transfer of living cancer cells through biting. Here we describe the sequencing, assembly, and annotation of the Tasmanian devil genome and whole-genome sequences for two geographically distant subclones of the cancer. Genomic analysis suggests that the cancer first arose from a female Tasmanian devil and that the clone has subsequently genetically diverged during its spread across Tasmania. The devil cancer genome contains more than 17,000 somatic base substitution mutations and bears the imprint of a distinct mutational process. Genotyping of somatic mutations in 104 geographically and temporally distributed Tasmanian devil tumors reveals the pattern of evolution and spread of this parasitic clonal lineage, with evidence of a selective sweep in one geographical area and persistence of parallel lineages in other populations.PaperClip

Chronic lymphocytic leukemia is characterized by relapse after treatment and chemotherapy resistance. Similarly, in other malignancies leukemia cells accumulate mutations during growth, forming heterogeneous cell populations that are subject to Darwinian selection and may respond differentially to treatment. There is therefore a clinical need to monitor changes in the subclonal composition of cancers during disease progression. Here, we use whole-genome sequencing to track subclonal heterogeneity in 3 chronic lymphocytic leukemia patients subjected to repeated cycles of therapy. We reveal different somatic mutation profiles in each patient and use these to establish probable hierarchical patterns of subclonal evolution, to identify subclones that decline or expand over time, and to detect founder mutations. We show that clonal evolution patterns are heterogeneous in individual patients. We conclude that genome sequencing is a powerful and sensitive approach to monitor disease progression repeatedly at the molecular level. IntroductionDespite significant progress in the management of lymphomas and leukemias, relapse remains the major cause of death. Increased use of expensive targeted therapies and toxic chemotherapies (especially in the elderly) confronts us with an urgent need to improve response prediction for all cancer patients to reduce side effects and costs from ineffective treatment. Current diagnostic approaches to treatment selection, response monitoring, and relapse prediction are limited to single genes and apply only to a minority of hematologic cancers. This is at odds with modern concepts of tumor propagation and maintenance, which propose that every cell in an individual cancer is characterized by a combination of mutation events that comprise tumorigenic (driver) mutations, passive (passenger) mutations, and possibly predisposing germline risk variants. Cancer cells propagate and diversify during tumor growth, resulting in a heterogeneous population of genotypically and phenotypically distinct subclones that are related in a hierarchical lineage. As the composition of the local environment changes, for example as a consequence of drug treatment, tumor cell populations adapt and evolve by Darwinian selection. [1][2][3] Whole-genome sequencing (WGS) of a single tumor sample can be used to generate a comprehensive catalog of variants that provides a snapshot of the cell population en masse at a particular time point. 2,4-6 However, over time and with continued evolution of the cancer, this snapshot becomes progressively less representative of the disease. Recent reports have described whole-tumor genomes from single patients or cohorts of individuals mostly at single time points and irrespective of treatment. [7][8][9][10] This approach has enabled identification of mutations representative and in some cases highly predictive of histologic cancer type, outcome, and/or treatment response. [11][12][13][14][15] Comparison of sequence data from primary and metastatic tumor samples, or from multiple lo...