Genomic Variation

1000 Genomes Project

Genetic variation
Image source: Wikipedia

The overall goal of the 1000 Genomes Project is the generation of a nearly complete catalog of common human genetic variants (defined as having a frequency of 1% or higher). This catalog will include Single Nucleotide Polymorphisms (SNPs), copy number variants (CNVs), and short insertion and deletion polymorphisms (Indels) and will be generated by high-quality sequence data obtained from a geographically diverse collection of individuals. These data will be critical to genetic association studies of complex diseases such as cardiovascular disease, various cancers and neurological diseases such a schizophrenia and autism. The project is international in scope with researchers and funding contributions from the US, UK, China and Germany.

The 1000 Genomes Project will also offer insight into the performance of so-called “next generation sequencing” platforms. These technologies offer both substantially increased throughput and reduced cost when compared to traditional Sanger dideoxynucleotide capillary platforms, but are still new enough that questions of required sequencing depth of coverage and error models have yet to be conclusively defined. The HGSC is using both the Applied Biosystems (AB) SOLiD and Roche/454 platforms and is making significant contributions to each sector of the Project pilot. The three pilot project sectors are: Pilot 1 - low-depth of coverage sequencing of whole genomes for 180 individuals; Pilot 2 - deep sequencing of whole genomes for two parent-offspring trios; Pilot 3 - targeted sequencing of coding sequences for 1000 genes in ~1000 individuals.

Additional Resources

Learn more about the 1000 Genomes Project

BCM-HGSC and the International HapMap Project

BCM-HGSC is actively involved in the International HapMap Project.

This project is a partnership of scientists from around the world who are working to develop a distinctive map of the human genome that will help researchers find the genes associated with human diseases.

Common diseases, such as mental illness, epilepsy, diabetes, cancer, and heart disease, are affected by both DNA and environmental factors. Tiny variations in human DNA, called “single nucleotide polymorphisms” or SNPs, can influence how people differ in their risk for these diseases.

The HapMap project is developing a map of the common patterns of these DNA variations, in the hope that identifying them can lead to an understanding of the complex causes of disease in humans. This map will be a key resource for researchers to use when searching for genes that affect our health and our body’s responses to drugs and the environment.

As part of the International HapMap Project, BCM-HGSC is resequencing 2.5 million bases (Mb) of the “ENCODE” region of the human genome in order to identify novel SNPs. This 2.5 Mb target is actually five separate, equal-size segments located on chromosomes 7p15, 8q24, 9q34, 12q12, and 18q12. These five segments contain a number of candidate disease genes associated with hypertension, diabetes, and bone disorders.

For this project, we sequenced DNA samples from 48 individuals who are from four different regions of the world, including the United States, China, Japan, and Nigeria. All novel SNPs will be genotyped in 270 samples from these same four populations.

HapMap 3 and ENCODE 3 (Janary 2009 draft release 3)

Current data download

HapMap3 draft release 3 FTP data

ENCODE 3 FTP data

Previous releases

HapMap3 draft release 2 FTP data

HapMap3 draft release 1 FTP data

About the Project

This is draft release 3 for genome-wide SNP genotyping and targeted sequencing in DNA samples from a variety of human populations (sometimes referred to as the "HapMap 3" samples).

This release contains the following data:

  • SNP genotype data generated from 1184 samples, collected using two platforms: the Illumina Human1M (by the Wellcome Trust Sanger Institute) and the Affymetrix SNP 6.0 (by the Broad Institute). Data from the two platforms have been merged for this release.

  • PCR-based resequencing data (by Baylor College of Medicine Human Genome Sequencing Center) across ten 100-kb regions (collectively referred to as "ENCODE 3") in 712 samples.

Since this is a draft release, we ask you to check this site regularly for updates and new releases.

Data Production Institutions

Funding Agencies

HapMap 3 Samples

The HapMap 3 sample collection comprises 1,301 samples (including the original 270 samples used in Phase I and II of the International HapMap Project) from 11 populations, listed below alphabetically by their 3-letter labels. For more information about these samples, click here.

label population sample number of samples
ASW African ancestry in Southwest USA 90
CEU Utah residents with Northern and Western European ancestry from the CEPH collection 180
CHB Han Chinese in Beijing, China 90
CHD Chinese in Metropolitan Denver, Colorado 100
GIH Gujarati Indians in Houston, Texas 100
JPT Japanese in Tokyo, Japan 91
LWK Luhya in Webuye, Kenya 100
MEX Mexican ancestry in Los Angeles, California 90
MKK Maasai in Kinyawa, Kenya 180
TSI Toscans in Italy 100
YRI Yoruba in Ibadan, Nigeria 180

ENCODE 3 Regions

Five of the ten ENCODE 3 regions overlap with the HapMap-ENCODE regions; the other five are regions selected at random from the ENCODE target regions (excluding the 10 HapMap-ENCODE regions). All ENCODE 3 regions are 100-kb in size, and are centered within each respective ENCODE region. Read more about the ENCODE project here.

region chromosome coordinates (NCBI build 36) status
ENm010 7 27,124,046-27,224,045 HapMap-ENCODE
ENr321 8 119,082,221-119,182,220 HapMap-ENCODE
ENr232 9 130,925,123-131,025,122 HapMap-ENCODE
ENr123 12 38,826,477-38,926,476 HapMap-ENCODE
ENr213 18 23,919,232-24,019,231 HapMap-ENCODE
ENr331 2 220,185,590-220,285,589 New
ENr221 5 56,071,007-56,171,006 New
ENr233 15 41,720,089-41,820,088 New
ENr313 16 61,033,950-61,133,949 New
ENr133 21 39,444,467-39,544,466 New

Data Content of This Release

A. SNP Genotype Data

label number of samples number of QC+ SNPs number of polymorphic QC+ SNPs
ASW 71 1,632,186 1,536,247
CEU 162 1,634,020 1,403,896
CHB 82 1,637,672 1,311,113
CHD 70 1,619,203 1,270,600
GIH 83 1,631,060 1,391,578
JPT 82 1,637,610 1,272,736
LWK 83 1,631,688 1,507,520
MEX 71 1,614,892 1,430,334
MKK 171 1,621,427 1,525,239
TSI 77 1,629,957 1,393,925
YRI 163 1,634,666 1,484,416
consensus 1,115 1,525,445 1,490,422

 

B. PCR Resequencing Data

label number of samples
ASW 55
CEU 119
CHB 90
CHD 30
GIH 60
JPT 97
LWK 60
MEX 27
MKK 0
TSI 60
YRI 120
total 712

Quality Control of This Release

A. SNP Genotype Data

Genotyping concordance between the two platforms was 0.9931 (computed over 249,889 overlapping SNPs). Data from the two platforms was merged using PLINK (--merge-mode 1), keeping only genotype calls if there is consensus between non-missing genotype calls (that is, merged genotype is set to missing if the two platforms give different, non-missing calls).

Quality control at the individual level was performed separately by the two sites. Only individuals with genotype data on both platforms were kept in this release. The following criteria were used to keep SNPs in the QC+ data sets:

  • Hardy-Weinberg p>0.000001 (per population)

  • missingness <0.05 (per population)

  • <3 Mendel errors (per population; only applies to YRI, CEU, ASW, MEX, MKK)

  • SNP must have a rsID and map to a unique genomic location

The "consensus" data set contains data for 1115 individuals (558 males, 557 females; 924 founders and 191 non-founders), only keeping SNPs that passed QC in all populations (overall call rate is 0.998). The "consensus|polymorphic" data set has 35,023 monomorphic SNPs (across the entire data set) removed.

In all genotype files, alleles are expressed as being on the (+/fwd) strand of NCBI build 36.

B. PCR Resequencing Data

The sequence-based variant calls were generated by tiling with PCR primer sets spaced approximately 800 bases apart across the ENCODE 3 regions. Following filtering low-quality reads the data were analyzed with SNP Detector version 3, for polymorphic site discovery and individual genotype calling. Various QC filters were then applied. Specifically, we filtered out PCR amplicons with too many SNPs, and SNPs with discordant allele calls in multiple amplicons. We also filtered out SNPs with low completeness in samples, or with too many conflicting genotype calls in two different strands.

In the QC+ data set, we filtered out samples with low completeness, and filtered out SNPs with low call rate in each population (<80%) and not in HWE (p<0.001). In the QC+ data set, the overall false positive rate is ~3.2%, based on a limited number of validation assays.

Caveats in This Release

A. SNP Genotype Data

  • Missing from this release are Illumina SNPs that are A/T or C/G due to strandedness issues.

  • Missing from this release are Illumina SNPs that are mitochondrial (as they do not have rsIDs).

  • There may be few remaining SNPs (Illumina) in this release that are still on (-/rev) strand of NCBI build 36, but they are not A/T or C/G SNPs, so easy to identify downstream.

B. PCR Resequencing Data

All variant calls have not yet been validated: we estimate that there is currently a false positive rate of ~12% among all calls, with a slightly higher rate (~14%) if considering just the singletons. Additional validation is ongoing. PCR sequencing of additional samples (MKK) is also ongoing.

How to Download This Release

A. SNP Genotype Data

To download the HapMap 3 data from our ftp site, click here.

B. PCR Resequencing Data

To download the ENCODE 3 data from our ftp site, click here.

Analysis Plans

Listed below are the analysis plans that we are currently pursuing:

  • SNP allele frequency estimation

  • Population differentiation

  • Linkage disequilibrium analysis

  • SNP tagging

  • Imputation efficiency

  • Genomic locations of human CNVs

  • Genotypes for CNVs

  • Population genetic properties of CNVs (allele frequencies, population differentiation, etc.)

  • Mutation rate (frequency of de novo CNV) and potential mutational mechanisms

  • Linkage disequilibrium properties of CNVs

  • Tagging and imputation of CNVs

  • Signals of selection around CNVs

  • Association of SNPs and CNVs with expression phenotypes

Data Release Policy

The release of pre-publication data from large resource-generating scientific projects was the subject of a meeting held in January 2003, the "Fort Lauderdale" meeting. An NHGRI policy statement based on the outcome of the meeting is on the NHGRI web site (https://www.genome.gov/10506537/reaffirmation-and-extension-of-nhgri-rapid-data-release-policies).

The recommendations of the Fort Lauderdale meeting address the roles and responsibilities of data producers, data users, and funders of "community resource projects", with the aim of establishing and maintaining an appropriate balance between the interests of data users in rapid access to data and the needs of data producers to receive recognition for their work. The conclusion of the attendees at the meeting was that responsible use of the data is necessary to ensure that first-rate data producers will continue to participate in such projects and produce and quickly release valuable large-scale data sets. "Responsible use" was defined as allowing the data producers to have the opportunity to publish the initial global analyses of the data, as articulated at the outset of the project. Doing so also will ensure that the data generated are fully described.

Mirror Sites

HapMap 3 site at Broad Institute of Harvard and MIT

HapMap 3 site at The Wellcome Trust Sanger Institute

 

Related Publications

Song J, Xue C, Preisser JS, Cramer DW, Houck KL, Liu G, et al.. Association of Single Nucleotide Polymorphisms in the ST3GAL4 Gene with VWF Antigen and Factor VIII Activity. PLoS One. 2016 ;11(9):e0160757.

van Leeuwen EM, Sabo A, Bis JC, Huffman JE, Manichaikul A, Smith AV, et al.. Meta-analysis of 49 549 individuals imputed with the 1000 Genomes Project reveals an exonic damaging variant in ANGPTL4 determining fasting TG levels. J Med Genet. 2016 ;53(7):441-9.

Xue C, Chen H, Yu F. Base-Biased Evolution of Disease-Associated Mutations in the Human Genome. Hum Mutat. 2016 ;37(11):1209-1214.

Li JN, Carrero IG, Dong JF, Yu FL. Complexity and diversity of F8 genetic variations in the 1000 genomes. J Thromb Haemost. 2015 ;13(11):2031-40.

Ramasamy R, M Bakırcıoğlu E, Cengiz C, Karaca E, Scovell J, Jhangiani SN, et al.. Whole-exome sequencing identifies novel homozygous mutation in NPAS2 in family with nonobstructive azoospermia. Fertil Steril. 2015 ;104(2):286-91.

Challis D, Antunes L, Garrison E, Banks E, Evani US, Muzny DM, et al.. The distribution and mutagenesis of short coding INDELs from 1,128 whole exomes. BMC Genomics. 2015 ;16(1):143.

Verhaaren BFJ, Debette S, Bis JC, Smith JA, M Ikram K, Adams HH, et al.. Multiethnic genome-wide association study of cerebral white matter hyperintensities on MRI. Circ Cardiovasc Genet. 2015 ;8(2):398-409.

van Leeuwen EM, Huffman JE, Bis JC, Isaacs A, Mulder M, Sabo A, et al.. Fine mapping the region reveals a common intronic insertion associated to HDL-C. NPJ Aging Mech Dis. 2015 ;1:15011.

Nikpay M, Goel A, Won H-H, Hall LM, Willenborg C, Kanoni S, et al.. A comprehensive 1,000 Genomes-based genome-wide association meta-analysis of coronary artery disease. Nat Genet. 2015 ;47(10):1121-1130.

Liu P, Kaplan A, Yuan B, Hanna JH, Lupski JR, Reiner O. Passage number is a major contributor to genomic structural variations in mouse iPSCs. Stem Cells. 2014 ;32(10):2657-67.