1000 Genomes Phase 3 Download _HOT

During the main 1000 Genomes Project, the NCBI acted as a mirror of the EBI hosted 1000 Genomes Project FTP site and also uploaded alignments and variant calls to an Amazon S3 bucket. This mirroring process stopped in September 2015. The NCBI FTP site and the Amazon S3 bucket still host 1000 Genomes Project data but no longer mirror new data. Both these locations reflect the structure of the FTP site in August 2015 and hold all the pilot, phase 1 and phase 3 data. NCBI and Amazon do not hold new alignments based on GRCh38, the current reference genome.

The phase 1 variants list released in 2012 and the phase 3 variants list released in 2014 overlap but phase 3 is not a complete superset of phase 1. The variant positions between phase 3 and phase 1 releases were compared using their positions. This shows that 2.3M phase 1 sites are not present in phase 3. Of the 2.3M sites, 1.92M are SNPs, the rest are either indels or structural variations (SVs).

1000 Genomes Phase 3 Download

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Our input sequence data is different. In phase 1 we had a mixture of both read lengths 36bp to >100bp and a mixture of sequencing platforms, Illumina, ABI SOLiD and 454. In phase 3 we only used data from the Illumina sequencing platform and we only used read lengths of 70bp+. We believe that these calls are higher quality, and that variants excluded this way were probably not real.

891k of the 1.37M sites missing from phase 1 were not identified by any phase 3 variant caller. These 891k SNPs have relatively high Ts/Tv ratio (1.84), which means these were likely missed in phase 3 because they are very rare, not because they are wrong; the increase in sample number in phase 3 made it harder to detect very rare events especially if the extra 1400 samples in phase 3 did not carry the alternative allele.

481k of these SNPs were initially called in phase 3. 340k of them failed our initial SVM filter so were not included in our final merged variant set. 57k overlapped with larger variant events so were not accurately called. 84k sites did not make it into our final set of genotypes due to losses in our pipeline. Some of these sites will be false positives but we have no strong evidence as to which of these sites are wrong and which were lost for other reasons.

The reference genomes used for our alignments are different. Phase 1 alignments were aligned to the standard GRCh37 primary reference including unplaced contigs. In phase 3 we added EBV and a decoy set to the reference to reduce mismapping. This will have reduced our false positive variant calling as it will have reduced mismapping leading to false SNP calls. We cannot quantify this effect.

We have made no attempt to eludcidate why our SV and indel numbers changed. Since the release of phase 1 data, the algorithms to detect and validate indels and SVs have improved dramatically. By and large, we assume the indels and SVs in phase 1 that are missing from phase 3 are false positive in phase 1.

For the analysis we have used a multi-caller site discovery approach along with imputation and phasing to produce a phased biallelic single nucleotide variant (SNV) and insertion/deletion (INDEL) call set. Variation had not previously been explored on the GRCh38 human genome assembly for 387 of the samples. Compared to our previous work with the 1000 Genomes Project data on GRCh38 described here, we identified over nine million novel SNVs and over 870 thousand novel INDELs.

In this issue of Cell, Bryska-Bishop et al. report the release of the expanded, high-depth sequencing data that characterize the fourth phase of the 1000 Genomes Project. Using extensive comparisons and benchmarks, they demonstrate how this dataset is positioned to serve as a more comprehensive and accurate resource for global genomics.

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.

The 1000 Genomes Project has already elucidated the properties and distribution of common and rare variation, provided insights into the processes that shape genetic diversity, and advanced understanding of disease biology1,2. This resource provides a benchmark for surveys of human genetic variation and constitutes a key component for human genetic studies, by enabling array design3,4, genotype imputation5, cataloguing of variants in regions of interest, and filtering of likely neutral variants6,7.

In this final phase, individuals were sampled from 26 populations in Africa (AFR), East Asia (EAS), Europe (EUR), South Asia (SAS), and the Americas (AMR) (Fig. 1a; see Supplementary Table 1 for population descriptions and abbreviations). All individuals were sequenced using both whole-genome sequencing (mean depth = 7.4) and targeted exome sequencing (mean depth = 65.7). In addition, individuals and available first-degree relatives (generally, adult offspring) were genotyped using high-density SNP microarrays. This provided a cost-effective means to discover genetic variants and estimate individual genotypes and haplotypes1,2.

In contrast to earlier phases of the project, we expanded analysis beyond bi-allelic events to include multi-allelic SNPs, indels, and a diverse set of structural variants (SVs). An overview of the sample collection, data generation, data processing, and analysis is given in Extended Data Fig. 1. Variant discovery used an ensemble of 24 sequence analysis tools (Supplementary Table 2), and machine-learning classifiers to separate high-quality variants from potential false positives, balancing sensitivity and specificity. Construction of haplotypes started with estimation of long-range phased haplotypes using array genotypes for project participants and, where available, their first degree relatives; continued with the addition of high confidence bi-allelic variants that were analysed jointly to improve these haplotypes; and concluded with the placement of multi-allelic and structural variants onto the haplotype scaffold one at a time (Box 1). Overall, we discovered, genotyped, and phased 88 million variant sites (Supplementary Table 3). The project has now contributed or validated 80 million of the 100 million variants in the public dbSNP catalogue (version 141 includes 40 million SNPs and indels newly contributed by this analysis). These novel variants especially enhance our catalogue of genetic variation within South Asian (which account for 24% of novel variants) and African populations (28% of novel variants).

The total number of observed non-reference sites differs greatly among populations (Fig. 1b). Individuals from African ancestry populations harbour the greatest numbers of variant sites, as predicted by the out-of-Africa model of human origins. Individuals from recently admixed populations show great variability in the number of variants, roughly proportional to the degree of recent African ancestry in their genomes.

The sharing of haplotypes among individuals is widely used for imputation in GWAS, a primary use of 1000 Genomes data. To assess imputation based on the phase 3 data set, we used Complete Genomics data for 9 or 10 individuals from each of 6 populations (CEU, CHS, LWK, PEL, PJL, and YRI). After excluding these individuals from the reference panel, we imputed genotypes across the genome using sites on a typical one million SNP microarray. The squared correlation between imputed and experimental genotypes was >95% for common variants in each population, decreasing gradually with minor allele frequency (Fig. 4a). Compared to phase 1, rare variation imputation improved considerably, particularly for newly sampled populations (for example, PEL and PJL, Extended Data Fig. 9a). Improvements in imputations restricted to overlapping samples suggest approximately equal contributions from greater genotype and sequence quality and from increased sample size (Fig. 4a, inset). Imputation accuracy is now similar for bi-allelic SNPs, bi-allelic indels, multi-allelic SNPs, and sites where indels and SNPs overlap, but slightly reduced for multi-allelic indels, which typically map to regions of low-complexity sequence and are much harder to genotype and phase (Extended Data Fig. 9b). Although imputation of rare variation remains challenging, it appears to be most accurate in African ancestry populations, where greater genetic diversity results in a larger number of haplotypes and improves the chances that a rare variant is tagged by a characteristic haplotype.

a, The power of discovery within the main data set for SNPs and indels identified within an overlapping sample of 284 genomes sequenced to high coverage by Complete Genomics (CG), and against a panel of >60,000 haplotypes constructed by the Haplotype Reference Consortium (HRC)9. To provide a measure of uncertainty, one curve is plotted for each chromosome. b, Improved power of discovery in phase 3 compared to phase 1, as assessed in a sample of 170 Complete Genomics genomes that are included in both phase 1 and phase 3. c, Heterozygote discordance in phase 3 for SNPs, indels, and SVs compared to 284 Complete Genomics genomes. d, Heterozygote discordance for phase 3 compared to phase 1 within the intersecting sample. e, Sensitivity to detect Complete Genomics SNPs as a function of sequencing depth. f, Heterozygote genotype discordance as a function of sequencing depth, as compared to Complete Genomics data. ff782bc1db