International HapMap Project

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The International HapMap Project was an organization that aimed to develop a haplotype map (HapMap) of the human genome, to describe the common patterns of human genetic variation. HapMap is used to find genetic variants affecting health, disease and responses to drugs and environmental factors. The information produced by the project is made freely available for research.

Contents

The International HapMap Project is a collaboration among researchers at academic centers, non-profit biomedical research groups and private companies in Canada, China (including Hong Kong), Japan, Nigeria, the United Kingdom, and the United States. It officially started with a meeting on October 27 to 29, 2002, and was expected to take about three years. It comprises two phases; the complete data obtained in Phase I were published on 27 October 2005. [1] The analysis of the Phase II dataset was published in October 2007. [2] The Phase III dataset was released in spring 2009 and the publication presenting the final results published in September 2010. [3]

Background

Unlike with the rarer Mendelian diseases, combinations of different genes and the environment play a role in the development and progression of common diseases (such as diabetes, cancer, heart disease, stroke, depression, and asthma), or in the individual response to pharmacological agents. [4] To find the genetic factors involved in these diseases, one could in principle do a genome-wide association study: obtain the complete genetic sequence of several individuals, some with the disease and some without, and then search for differences between the two sets of genomes. At the time, this approach was not feasible because of the cost of full genome sequencing. The HapMap project proposed a shortcut.

Although any two unrelated people share about 99.5% of their DNA sequence, their genomes differ at specific nucleotide locations. Such sites are known as single nucleotide polymorphisms (SNPs), and each of the possible resulting gene forms is called an allele. [5] The HapMap project focuses only on common SNPs, those where each allele occurs in at least 1% of the population.

Each person has two copies of all chromosomes, except the sex chromosomes in males. For each SNP, the combination of alleles a person has is called a genotype. Genotyping refers to uncovering what genotype a person has at a particular site. The HapMap project chose a sample of 269 individuals and selected several million well-defined SNPs, genotyped the individuals for these SNPs, and published the results. [6]

The alleles of nearby SNPs on a single chromosome are correlated. Specifically, if the allele of one SNP for a given individual is known, the alleles of nearby SNPs can often be predicted, a process known as genotype imputation. [7] This is because each SNP arose in evolutionary history as a single point mutation, and was then passed down on the chromosome surrounded by other, earlier, point mutations. SNPs that are separated by a large distance on the chromosome are typically not very well correlated, because recombination occurs in each generation and mixes the allele sequences of the two chromosomes. A sequence of consecutive alleles on a particular chromosome is known as a haplotype. [8]

To find the genetic factors involved in a particular disease, one can proceed as follows. First a certain region of interest in the genome is identified, possibly from earlier inheritance studies. In this region one locates a set of tag SNPs from the HapMap data; these are SNPs that are very well correlated with all the other SNPs in the region. Using these, genotype imputation can be used to determine (impute) the other SNPs and thus the entire haplotype with high confidence. Next, one determines the genotype for these tag SNPs in several individuals, some with the disease and some without. By comparing the two groups, one determines the likely locations and haplotypes that are involved in the disease.

Samples used

Haplotypes are generally shared between populations, but their frequency can differ widely. Four populations were selected for inclusion in the HapMap: 30 adult-and-both-parents Yoruba trios from Ibadan, Nigeria (YRI), 30 trios of Utah residents of northern and western European ancestry (CEU), 44 unrelated Japanese individuals from Tokyo, Japan (JPT) and 45 unrelated Han Chinese individuals from Beijing, China (CHB). Although the haplotypes revealed from these populations should be useful for studying many other populations, parallel studies are currently examining the usefulness of including additional populations in the project.

All samples were collected through a community engagement process with appropriate informed consent. The community engagement process was designed to identify and attempt to respond to culturally specific concerns and give participating communities input into the informed consent and sample collection processes. [9]

In phase III, 11 global ancestry groups have been assembled: ASW (African ancestry in Southwest USA); CEU (Utah residents with Northern and Western European ancestry from the CEPH collection); CHB (Han Chinese in Beijing, China); CHD (Chinese in Metropolitan Denver, Colorado); GIH (Gujarati Indians in Houston, Texas); JPT (Japanese in Tokyo, Japan); LWK (Luhya in Webuye, Kenya); MEX (Mexican ancestry in Los Angeles, California); MKK (Maasai in Kinyawa, Kenya); TSI (Tuscans in Italy); YRI (Yoruba in Ibadan, Nigeria). [10]

PhaseIDPlacePopulationDetail
I/IICEU Flag of the United States.svg Utah residents with Northern and Western European ancestry from the CEPH collection Detail
I/IICHB Flag of the People's Republic of China.svg Han Chinese in Beijing, China Detail
I/IIJPT Flag of Japan.svg Japanese in Tokyo, Japan Detail
I/IIYRI Flag of Nigeria.svg Yoruba in Ibadan, Nigeria Detail
IIIASW Flag of the United States.svg African ancestry in the Southwest USA Detail
IIICHD Flag of the United States.svg Chinese in metropolitan Denver, CO, United States Detail
IIIGIH Flag of the United States.svg Gujarati Indians in Houston, TX, United States Detail
IIILWK Flag of Kenya.svg Luhya in Webuye, Kenya Detail
IIIMKK Flag of Kenya.svg Maasai in Kinyawa, Kenya Detail
IIIMXL Flag of the United States.svg Mexican ancestry in Los Angeles, CA, United States Detail
IIITSI Flag of Italy.svg Toscani in Italia Detail

Three combined panels have also been created, which allow better identification of SNPs in groups outside the nine homogenous samples: CEU+TSI (Combined panel of Utah residents with Northern and Western European ancestry from the CEPH collection and Tuscans in Italy); JPT+CHB (Combined panel of Japanese in Tokyo, Japan and Han Chinese in Beijing, China) and JPT+CHB+CHD (Combined panel of Japanese in Tokyo, Japan, Han Chinese in Beijing, China and Chinese in Metropolitan Denver, Colorado). CEU+TSI, for instance, is a better model of UK British individuals than is CEU alone. [10]

Scientific strategy

It was expensive in the 1990s to sequence patients’ whole genomes. So the National Institutes of Health embraced the idea for a "shortcut", which was to look just at sites on the genome where many people have a variant DNA unit. The theory behind the shortcut was that, since the major diseases are common, so too would be the genetic variants that caused them. Natural selection keeps the human genome free of variants that damage health before children are grown, the theory held, but fails against variants that strike later in life, allowing them to become quite common (In 2002 the National Institutes of Health started a $138 million project called the HapMap to catalog the common variants in European, East Asian and African genomes). [11]

For the Phase I, one common SNP was genotyped every 5,000 bases. Overall, more than one million SNPs were genotyped. The genotyping was carried out by 10 centres using five different genotyping technologies. Genotyping quality was assessed by using duplicate or related samples and by having periodic quality checks where centres had to genotype common sets of SNPs.

The Canadian team was led by Thomas J. Hudson at McGill University in Montreal and focused on chromosomes 2 and 4p. The Chinese team was led by Huanming Yang in Beijing and Shanghai, and Lap-Chee Tsui in Hong Kong and focused on chromosomes 3, 8p and 21. The Japanese team was led by Yusuke Nakamura at the University of Tokyo and focused on chromosomes 5, 11, 14, 15, 16, 17 and 19. The British team was led by David R. Bentley at the Sanger Institute and focused on chromosomes 1, 6, 10, 13 and 20. There were four United States' genotyping centres: a team led by Mark Chee and Arnold Oliphant at Illumina Inc. in San Diego (studying chromosomes 8q, 9, 18q, 22 and X), a team led by David Altshuler and Mark Daly at the Broad Institute in Cambridge, USA (chromosomes 4q, 7q, 18p, Y and mitochondrion), a team led by Richard Gibbs at the Baylor College of Medicine in Houston (chromosome 12), and a team led by Pui-Yan Kwok at the University of California, San Francisco (chromosome 7p).

To obtain enough SNPs to create the Map, the Consortium funded a large re-sequencing project to discover millions of additional SNPs. These were submitted to the public dbSNP database. As a result, by August 2006, the database included more than ten million SNPs, and more than 40% of them were known to be polymorphic. By comparison, at the start of the project, fewer than 3 million SNPs were identified, and no more than 10% of them were known to be polymorphic.

During Phase II, more than two million additional SNPs were genotyped throughout the genome by David R. Cox, Kelly A. Frazer and others at Perlegen Sciences and 500,000 by the company Affymetrix.

Data access

All of the data generated by the project, including SNP frequencies, genotypes and haplotypes, were placed in the public domain and are available for download. [12] This website also contains a genome browser which allows to find SNPs in any region of interest, their allele frequencies and their association to nearby SNPs. A tool that can determine tag SNPs for a given region of interest is also provided. These data can also be directly accessed from the widely used Haploview program.

Publications

See also

Related Research Articles

<span class="mw-page-title-main">Human genome</span> Complete set of nucleic acid sequences for humans

The human genome is a complete set of nucleic acid sequences for humans, encoded as DNA within the 23 chromosome pairs in cell nuclei and in a small DNA molecule found within individual mitochondria. These are usually treated separately as the nuclear genome and the mitochondrial genome. Human genomes include both protein-coding DNA sequences and various types of DNA that does not encode proteins. The latter is a diverse category that includes DNA coding for non-translated RNA, such as that for ribosomal RNA, transfer RNA, ribozymes, small nuclear RNAs, and several types of regulatory RNAs. It also includes promoters and their associated gene-regulatory elements, DNA playing structural and replicatory roles, such as scaffolding regions, telomeres, centromeres, and origins of replication, plus large numbers of transposable elements, inserted viral DNA, non-functional pseudogenes and simple, highly repetitive sequences. Introns make up a large percentage of non-coding DNA. Some of this non-coding DNA is non-functional junk DNA, such as pseudogenes, but there is no firm consensus on the total amount of junk DNA.

<span class="mw-page-title-main">Single-nucleotide polymorphism</span> Single nucleotide in genomic DNA at which different sequence alternatives exist

In genetics and bioinformatics, a single-nucleotide polymorphism is a germline substitution of a single nucleotide at a specific position in the genome that is present in a sufficiently large fraction of considered population.

<span class="mw-page-title-main">Haplotype</span> Group of genes from one parent

A haplotype is a group of alleles in an organism that are inherited together from a single parent.

In population genetics, linkage disequilibrium (LD) is the non-random association of alleles at different loci in a given population. Loci are said to be in linkage disequilibrium when the frequency of association of their different alleles is higher or lower than expected if the loci were independent and associated randomly.

<span class="mw-page-title-main">Identity by descent</span> Identical nucleotide sequence due to inheritance without recombination from a common ancestor

A DNA segment is identical by state (IBS) in two or more individuals if they have identical nucleotide sequences in this segment. An IBS segment is identical by descent (IBD) in two or more individuals if they have inherited it from a common ancestor without recombination, that is, the segment has the same ancestral origin in these individuals. DNA segments that are IBD are IBS per definition, but segments that are not IBD can still be IBS due to the same mutations in different individuals or recombinations that do not alter the segment.

Genetic association is when one or more genotypes within a population co-occur with a phenotypic trait more often than would be expected by chance occurrence.

<span class="mw-page-title-main">Human genetic variation</span> Genetic diversity in human populations

Human genetic variation is the genetic differences in and among populations. There may be multiple variants of any given gene in the human population (alleles), a situation called polymorphism.

In molecular biology, SNP array is a type of DNA microarray which is used to detect polymorphisms within a population. A single nucleotide polymorphism (SNP), a variation at a single site in DNA, is the most frequent type of variation in the genome. Around 335 million SNPs have been identified in the human genome, 15 million of which are present at frequencies of 1% or higher across different populations worldwide.

A tag SNP is a representative single nucleotide polymorphism (SNP) in a region of the genome with high linkage disequilibrium that represents a group of SNPs called a haplotype. It is possible to identify genetic variation and association to phenotypes without genotyping every SNP in a chromosomal region. This reduces the expense and time of mapping genome areas associated with disease, since it eliminates the need to study every individual SNP. Tag SNPs are useful in whole-genome SNP association studies in which hundreds of thousands of SNPs across the entire genome are genotyped.

<span class="mw-page-title-main">Genome-wide association study</span> Study of genetic variants in different individuals

In genomics, a genome-wide association study, is an observational study of a genome-wide set of genetic variants in different individuals to see if any variant is associated with a trait. GWA studies typically focus on associations between single-nucleotide polymorphisms (SNPs) and traits like major human diseases, but can equally be applied to any other genetic variants and any other organisms.

Minor allele frequency (MAF) is the frequency at which the second most common allele occurs in a given population. They play a surprising role in heritability since MAF variants which occur only once, known as "singletons", drive an enormous amount of selection.

<span class="mw-page-title-main">1000 Genomes Project</span> International research effort on genetic variation

The 1000 Genomes Project, launched in January 2008, was an international research effort to establish by far the most detailed catalogue of human genetic variation. Scientists planned to sequence the genomes of at least one thousand anonymous participants from a number of different ethnic groups within the following three years, using newly developed technologies which were faster and less expensive. In 2010, the project finished its pilot phase, which was described in detail in a publication in the journal Nature. In 2012, the sequencing of 1092 genomes was announced in a Nature publication. In 2015, two papers in Nature reported results and the completion of the project and opportunities for future research.

dbSNP Genetics database

The Single Nucleotide Polymorphism Database (dbSNP) is a free public archive for genetic variation within and across different species developed and hosted by the National Center for Biotechnology Information (NCBI) in collaboration with the National Human Genome Research Institute (NHGRI). Although the name of the database implies a collection of one class of polymorphisms only, it in fact contains a range of molecular variation: (1) SNPs, (2) short deletion and insertion polymorphisms (indels/DIPs), (3) microsatellite markers or short tandem repeats (STRs), (4) multinucleotide polymorphisms (MNPs), (5) heterozygous sequences, and (6) named variants. The dbSNP accepts apparently neutral polymorphisms, polymorphisms corresponding to known phenotypes, and regions of no variation. It was created in September 1998 to supplement GenBank, NCBI’s collection of publicly available nucleic acid and protein sequences.

WGAViewer is a bioinformatics software tool which is designed to visualize, annotate, and help interpret the results generated from a genome wide association study (GWAS). Alongside the P values of association, WGAViewer allows a researcher to visualize and consider other supporting evidence, such as the genomic context of the SNP, linkage disequilibrium (LD) with ungenotyped SNPs, gene expression database, and the evidence from other GWAS projects, when determining the potential importance of an individual SNP.

In genetics, association mapping, also known as "linkage disequilibrium mapping", is a method of mapping quantitative trait loci (QTLs) that takes advantage of historic linkage disequilibrium to link phenotypes to genotypes, uncovering genetic associations.

<span class="mw-page-title-main">Reference genome</span>

A reference genome is a digital nucleic acid sequence database, assembled by scientists as a representative example of the set of genes in one idealized individual organism of a species. As they are assembled from the sequencing of DNA from a number of individual donors, reference genomes do not accurately represent the set of genes of any single individual organism. Instead a reference provides a haploid mosaic of different DNA sequences from each donor. For example, one of the most recent human reference genomes, assembly GRCh38/hg38, is derived from >60 genomic clone libraries. There are reference genomes for multiple species of viruses, bacteria, fungus, plants, and animals. Reference genomes are typically used as a guide on which new genomes are built, enabling them to be assembled much more quickly and cheaply than the initial Human Genome Project. Reference genomes can be accessed online at several locations, using dedicated browsers such as Ensembl or UCSC Genome Browser.

In genetics, haplotype estimation refers to the process of statistical estimation of haplotypes from genotype data. The most common situation arises when genotypes are collected at a set of polymorphic sites from a group of individuals. For example in human genetics, genome-wide association studies collect genotypes in thousands of individuals at between 200,000-5,000,000 SNPs using microarrays. Haplotype estimation methods are used in the analysis of these datasets and allow genotype imputation of alleles from reference databases such as the HapMap Project and the 1000 Genomes Project.

Imputation in genetics refers to the statistical inference of unobserved genotypes. It is achieved by using known haplotypes in a population, for instance from the HapMap or the 1000 Genomes Project in humans, thereby allowing to test for association between a trait of interest and experimentally untyped genetic variants, but whose genotypes have been statistically inferred ("imputed"). Genotype imputation is usually performed on SNPs, the most common kind of genetic variation.

SNV calling from NGS data is any of a range of methods for identifying the existence of single nucleotide variants (SNVs) from the results of next generation sequencing (NGS) experiments. These are computational techniques, and are in contrast to special experimental methods based on known population-wide single nucleotide polymorphisms. Due to the increasing abundance of NGS data, these techniques are becoming increasingly popular for performing SNP genotyping, with a wide variety of algorithms designed for specific experimental designs and applications. In addition to the usual application domain of SNP genotyping, these techniques have been successfully adapted to identify rare SNPs within a population, as well as detecting somatic SNVs within an individual using multiple tissue samples.

<span class="mw-page-title-main">Yusuke Nakamura (geneticist)</span> Medical researcher in Japan

Yusuke Nakamura is a Japanese prominent geneticist and cancer researcher best known for developing Genome-Wide Association Study (GWAS). He is one of the world's pioneers in applying genetic variations and whole genome sequencing, leading the research field of personalized medicine.

References

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