Gabor T. Marth

The Sequence Alignment/Map format and SAMtools

Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk

The variant call format and VCFtools

Summary: The variant call format (VCF) is a generic format for storing DNA polymorphism data such as SNPs, insertions, deletions and structural variants, together with rich annotations. VCF is usually stored in a compressed manner and can be indexed for fast data retrieval of variants from a range of positions on the reference genome. The format was developed for the 1000 Genomes Project, and has also been adopted by other projects such as UK10K, dbSNP and the NHLBI Exome Project. VCFtools is a software suite that implements various utilities for processing VCF files, including validation, merging, comparing and also provides a general Perl API. Availability: http://vcftools.sourceforge.net Contact: rd@sanger.ac.uk

Mapping copy number variation by population-scale genome sequencing

Genomic structural variants (SVs) are abundant in humans, differing from other forms of variation in extent, origin and functional impact. Despite progress in SV characterization, the nucleotide resolution architecture of most SVs remains unknown. We constructed a map of unbalanced SVs (that is, copy number variants) based on whole genome DNA sequencing data from 185 human genomes, integrating evidence from complementary SV discovery approaches with extensive experimental validations. Our map encompassed 22,025 deletions and 6,000 additional SVs, including insertions and tandem duplications. Most SVs (53%) were mapped to nucleotide resolution, which facilitated analysing their origin and functional impact. We examined numerous whole and partial gene deletions with a genotyping approach and observed a depletion of gene disruptions amongst high frequency deletions. Furthermore, we observed differences in the size spectra of SVs originating from distinct formation mechanisms, and constructed a map of SV hotspots formed by common mechanisms. Our analytical framework and SV map serves as a resource for sequencing-based association studies.

An integrated map of structural variation in 2,504 human genomes

Structural variants are implicated in numerous diseases and make up the majority of varying nucleotides among human genomes. Here we describe an integrated set of eight structural variant classes comprising both balanced and unbalanced variants, which we constructed using short-read DNA sequencing data and statistically phased onto haplotype blocks in 26 human populations. Analysing this set, we identify numerous gene-intersecting structural variants exhibiting population stratification and describe naturally occurring homozygous gene knockouts that suggest the dispensability of a variety of human genes. We demonstrate that structural variants are enriched on haplotypes identified by genome-wide association studies and exhibit enrichment for expression quantitative trait loci. Additionally, we uncover appreciable levels of structural variant complexity at different scales, including genic loci subject to clusters of repeated rearrangement and complex structural variants with multiple breakpoints likely to have formed through individual mutational events. Our catalogue will enhance future studies into structural variant demography, functional impact and disease association.

A general approach to single-nucleotide polymorphism discovery

Single-nucleotide polymorphisms (SNPs) are the most abundant form of human genetic variation and a resource for mapping complex genetic traits. The large volume of data produced by high-throughput sequencing projects is a rich and largely untapped source of SNPs (refs 2, 3, 4, 5). We present here a unified approach to the discovery of variations in genetic sequence data of arbitrary DNA sources. We propose to use the rapidly emerging genomic sequence as a template on which to layer often unmapped, fragmentary sequence data and to use base quality values to discern true allelic variations from sequencing errors. By taking advantage of the genomic sequence we are able to use simpler yet more accurate methods for sequence organization: fragment clustering, paralogue identification and multiple alignment. We analyse these sequences with a novel, Bayesian inference engine, POLYBAYES, to calculate the probability that a given site is polymorphic. Rigorous treatment of base quality permits completely automated evaluation of the full length of all sequences, without limitations on alignment depth. We demonstrate this approach by accurate SNP predictions in human ESTs aligned to finished and working-draft quality genomic sequences, a data set representative of the typical challenges of sequence-based SNP discovery.

Whole-genome sequencing and variant discovery in C. elegans

Massively parallel sequencing instruments enable rapid and inexpensive DNA sequence data production. Because these instruments are new, their data require characterization with respect to accuracy and utility. To address this, we sequenced a Caernohabditis elegans N2 Bristol strain isolate using the Solexa Sequence Analyzer, and compared the reads to the reference genome to characterize the data and to evaluate coverage and representation. Massively parallel sequencing facilitates strain-to-reference comparison for genome-wide sequence variant discovery. Owing to the short-read-length sequences produced, we developed a revised approach to determine the regions of the genome to which short reads could be uniquely mapped. We then aligned Solexa reads from C. elegans strain CB4858 to the reference, and screened for single-nucleotide polymorphisms (SNPs) and small indels. This study demonstrates the utility of massively parallel short read sequencing for whole genome resequencing and for accurate discovery of genome-wide polymorphisms.

Demographic history and rare allele sharing among human populations

High-throughput sequencing technology enables population-level surveys of human genomic variation. Here, we examine the joint allele frequency distributions across continental human populations and present an approach for combining complementary aspects of whole-genome, low-coverage data and targeted high-coverage data. We apply this approach to data generated by the pilot phase of the Thousand Genomes Project, including whole-genome 2–4× coverage data for 179 samples from HapMap European, Asian, and African panels as well as high-coverage target sequencing of the exons of 800 genes from 697 individuals in seven populations. We use the site frequency spectra obtained from these data to infer demographic parameters for an Out-of-Africa model for populations of African, European, and Asian descent and to predict, by a jackknife-based approach, the amount of genetic diversity that will be discovered as sample sizes are increased. We predict that the number of discovered nonsynonymous coding variants will reach 100,000 in each population after ∼1,000 sequenced chromosomes per population, whereas ∼2,500 chromosomes will be needed for the same number of synonymous variants. Beyond this point, the number of segregating sites in the European and Asian panel populations is expected to overcome that of the African panel because of faster recent population growth. Overall, we find that the majority of human genomic variable sites are rare and exhibit little sharing among diverged populations. Our results emphasize that replication of disease association for specific rare genetic variants across diverged populations must overcome both reduced statistical power because of rarity and higher population divergence.

Human Diallelic Insertion/Deletion Polymorphisms

We report the identification and characterization of 2,000 human diallelic insertion/deletion polymorphisms (indels) distributed throughout the human genome. Candidate indels were identified by comparison of overlapping genomic or cDNA sequences. Average confirmation rate for indels with a > or =2-nt allele-length difference was 58%, but the confirmation rate for indels with a 1-nt length difference was only 14%. The vast majority of the human diallelic indels were monomorphic in chimpanzees and gorillas. The ratio of deletionrcolon;insertion mutations was 4.1. Allele frequencies for the indels were measured in Europeans, Africans, Japanese, and Native Americans. New alleles were generally lower in frequency than old alleles. This tendency was most pronounced for the Africans, who are likely to be closest among the four groups to the original modern human population. Diallelic indels comprise approximately 8% of all human polymorphisms. Their abundance and ease of analysis make them useful for many applications.

The Allele Frequency Spectrum in Genome-Wide Human Variation Data Reveals Signals of Differential Demographic History in Three Large World Populations

We have studied a genome-wide set of single-nucleotide polymorphism (SNP) allele frequency measures for African-American, East Asian, and European-American samples. For this analysis we derived a simple, closed mathematical formulation for the spectrum of expected allele frequencies when the sampled populations have experienced nonstationary demographic histories. The direct calculation generates the spectrum orders of magnitude faster than coalescent simulations do and allows us to generate spectra for a large number of alternative histories on a multidimensional parameter grid. Model-fitting experiments using this grid reveal significant population-specific differences among the demographic histories that best describe the observed allele frequency spectra. European and Asian spectra show a bottleneck-shaped history: a reduction of effective population size in the past followed by a recent phase of size recovery. In contrast, the African-American spectrum shows a history of moderate but uninterrupted population expansion. These differences are expected to have profound consequences for the design of medical association studies. The analytical methods developed for this study, i.e., a closed mathematical formulation for the allele frequency spectrum, correcting the ascertainment bias introduced by shallow SNP sampling, and dealing with variable sample sizes provide a general framework for the analysis of public variation data.

Rapid whole-genome mutational profiling using next-generation sequencing technologies.

Forward genetic mutational studies, adaptive evolution, and phenotypic screening are powerful tools for creating new variant organisms with desirable traits. However, mutations generated in the process cannot be easily identified with traditional genetic tools. We show that new high-throughput, massively parallel sequencing technologies can completely and accurately characterize a mutant genome relative to a previously sequenced parental (reference) strain. We studied a mutant strain of Pichia stipitis, a yeast capable of converting xylose to ethanol. This unusually efficient mutant strain was developed through repeated rounds of chemical mutagenesis, strain selection, transformation, and genetic manipulation over a period of seven years. We resequenced this strain on three different sequencing platforms. Surprisingly, we found fewer than a dozen mutations in open reading frames. All three sequencing technologies were able to identify each single nucleotide mutation given at least 10-15-fold nominal sequence coverage. Our results show that detecting mutations in evolved and engineered organisms is rapid and cost-effective at the whole-genome level using new sequencing technologies. Identification of specific mutations in strains with altered phenotypes will add insight into specific gene functions and guide further metabolic engineering efforts.