The International HapMap Project

Belmont, John W.; Hardenbol, Paul; Willis, T. D.; Yu, Fuli; Yang, Huanming; Ch'Ang, Lan Yang; Huang, Wei; Liu, Bin; Shen, Yan; Tam, Paul Kwong Hang; Tsui, Lap Chee; Waye, Mary Miu Yee; Wong, Jeffrey Tze Fei; Zeng, Changqing; Zhang, Qingrun; Chee, Mark S.; Galver, Luana; Kruglyak, Semyon; Murray, Sarah; Oliphant, Arnold; Montpetit, Alexandre; Chagnon, Fanny; Ferretti, Vincent; Leboeuf, Martin; Phillips, Michael; Verner, Andrei; Duan, Shenghui; Lind, Denise L.; Miller, Raymond D.; Rice, John P.; Saccone, Nancy L.; Taillon-Miller, Patricia; Xiao, Ming; Sekine, Akihiro; Sorimachi, Koki; Tanaka, Yoïchi; Tsunoda, Tatsuhiko; Yoshino, Eiji; Bentley, David; Hunt, Sarah; Powell, Don; Zhang, Houcan; Matsuda, Ichiro; Fukushima, Yoshimitsu; Macer, Darryl; Suda, Eiko; Rotimi, Charles N.; Adebamowo, Clement; Aniagwu, Toyin; Marshall, Patricia A.; Matthew, Olayemi; Nkwodimmah, Chibuzor; Royal, Charmaine; Leppert, M.; Dixon, Missy; Cunningham, Fiona; Kanani, Ardavan; Þórisson, Guðmundur Á.; Chen, Peter E.; Cutler, David J.; Kashuk, Carl; Donnelly, Peter; Marchini, Jonathan; McVean, Gil; Myers, Simon; Cardon, Lon R.; Morris, Andrew P.; Weir, Bruce S.; Mullikin, James C.; Feolo, Michael; Daly, Mark J.; Qiu, Ren-Zong; Kent, Alastair; Dunston, Georgia M.; Kato, Kazuto; Niikawa, Norio; Watkin, Jessica; Gibbs, Richard A.; Sodergren, Erica; Weinstock, George M.; Wilson, Richard K.; Fulton, Lucinda L.; Rogers, Jane; Birren, Bruce W.; Han, Hua; Wang, Hongguang; Godbout, Martin; Wallenburg, John C.; L'Archevêque, Paul; Bellemare, Guy; Todani, Kazuo; Fujita, Takashi; Tanaka, Satoshi; Holden, Arthur L.; Collins, Francis S.; Brooks, Lisa; McEwen, Jean; Guyer, Mark S.; Jordan, Elizabeth; Peterson, Jane; Spiegel, Jack; Sung, Lawrence M.; Zacharia, Lynn F.; Kennedy, Karen; Dunn, Michael; Seabrook, Richard; Shillito, Mark; Skene, Barbara; Stewart, John; Valle, David; Clayton, Ellen Wright; Jorde, Lynn B.; Chakravarti, Aravinda; Cho, Mildred K.; Duster, Troy; Foster, Morris W.; Jasperse, Maria; Knoppers, Bartha Maria; Kwok, Pui‐Yan; Licinio, Júlio; Long, Jeffrey C.; Ossorio, Pilar N.; Wang, Vivian Ota; Spallone, Patricia; Terry, Sharon F.; Lander, Eric S.; Lai, Eric; Nickerson, Deborah A.; Abecasis, Gonçalo R.; Altshuler, David; Boehnke, Michael; Deloukas, Panos; Douglas, Julie A.; Gabriel, Stacey; Hudson, Richard R.; Hudson, Thomas J.; Kruglyak, Leonid; Nakamura, Yusuke; Nussbaum, Robert L.; Schaffner, Stephen F.; Sherry, Stephen T.; Stein, Lincoln; Tanaka, Toshihiro

doi:10.1038/nature02168

Cited by 5,321 publications

(1,773 citation statements)

References 69 publications

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“…The HapMap project, which was started in 2003[89], has genotyped four million Single Nucleotide Polymorphisms (SNPs) in 1301 individuals from eleven populations distributed across the world[90]. The data has provided an abundance of information on common SNPs and their association with human disease, but many variants, including disease-causing variants, that occur at a low allele frequency in the population have been missed.…”

Section: Mps Applications In Mutation Analysismentioning

confidence: 99%

Direct mutation analysis by high-throughput sequencing: From germline to low-abundant, somatic variants

Gundry

Vijg

2012

Mutation Research/Fundamental and Molecular Mechanisms of Mutag

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DNA mutations are the source of genetic variation within populations. The majority of mutations with observable effects are deleterious. In humans mutations in the germ line can cause genetic disease. In somatic cells multiple rounds of mutations and selection lead to cancer. The study of genetic variation has progressed rapidly since the completion of the draft sequence of the human genome. Recent advances in sequencing technology, most importantly the introduction of massively parallel sequencing (MPS), have resulted in more than a hundred-fold reduction in the time and cost required for sequencing nucleic acids. These improvements have greatly expanded the use of sequencing as a practical tool for mutation analysis. While in the past the high cost of sequencing limited mutation analysis to selectable markers or small forward mutation targets assumed to be representative for the genome overall, current platforms allow whole genome sequencing for less than $5,000. This has already given rise to direct estimates of germline mutation rates in multiple organisms including humans by comparing whole genome sequences between parents and offspring. Here we present a brief history of the field of mutation research, with a focus on classical tools for the measurement of mutation rates. We then review MPS, how it is currently applied and the new insight into human and animal mutation frequencies and spectra that has been obtained from whole genome sequencing. While great progress has been made, we note that the single most important limitation of current MPS approaches for mutation analysis is the inability to address low-abundance mutations that turn somatic tissues into mosaics of cells. Such mutations are at the basis of intra-tumor heterogeneity, with important implications for clinical diagnosis, and could also contribute to somatic diseases other than cancer, including aging. Some possible approaches to gain access to low-abundance mutations are discussed, with a brief overview of new sequencing platforms that are currently waiting in the wings to advance this exploding field even further.

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Section: Mps Applications In Mutation Analysismentioning

confidence: 99%

Direct mutation analysis by high-throughput sequencing: From germline to low-abundant, somatic variants

Gundry

Vijg

2012

Mutation Research/Fundamental and Molecular Mechanisms of Mutag

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“…These samples include the HapMap collection of individuals with different ethnicities that has been anonomized and immortalized by Coriell [13,14]. These populations form the population allele frequencies most often used in dbSNP [15] and a subset of these samples are also used for population allele frequencies on the Cancer500 website [12].…”

Section: Introductionmentioning

confidence: 99%

Population based allele frequencies of disease associated polymorphisms in the Personalized Medicine Research Project

et al. 2010

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BackgroundThere is a lack of knowledge regarding the frequency of disease associated polymorphisms in populations and population attributable risk for many populations remains unknown. Factors that could affect the association of the allele with disease, either positively or negatively, such as race, ethnicity, and gender, may not be possible to determine without population based allele frequencies.Here we used a panel of 51 polymorphisms previously associated with at least one disease and determined the allele frequencies within the entire Personalized Medicine Research Project population based cohort. We compared these allele frequencies to those in dbSNP and other data sources stratified by race. Differences in allele frequencies between self reported race, region of origin, and sex were determined.ResultsThere were 19544 individuals who self reported a single racial category, 19027 or (97.4%) self reported white Caucasian, and 11205 (57.3%) individuals were female. Of the 11,208 (57%) individuals with an identifiable region of origin 8337 or (74.4%) were German.41 polymorphisms were significantly different between self reported race at the 0.05 level. Stratification of our Caucasian population by self reported region of origin revealed 19 polymorphisms that were significantly different (p = 0.05) between individuals of different origins. Further stratification of the population by gender revealed few significant differences in allele frequencies between the genders.ConclusionsThis represents one of the largest population based allele frequency studies to date. Stratification by self reported race and region of origin revealed wide differences in allele frequencies not only by race but also by region of origin within a single racial group. We report allele frequencies for our Asian/Hmong and American Indian populations; these two minority groups are not typically selected for population allele frequency detection. Population wide allele frequencies are important for the design and implementation of studies and for determining the relevance of a disease associated polymorphism for a given population.

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“…We calculated quality indices and established their empirical distributions based on SNP array data from the Taiwan Han Chinese Cell and Genome Bank [52] and the International HapMap Project [13-16]. Values of quality indices were fitted by lognormal distributions and examined by Kolmogorov-Smirnov goodness-of-fit tests [55].…”

Section: Resultsmentioning

confidence: 99%

“…Samples used in our analyses were from three genomic projects, the Taiwan Han Chinese Cell and Genome Bank [52], the International HapMap Project [13-16], and the Taiwan Young-Onset Hypertension Study [5]. The first project provides 367 and 448 Han Chinese samples from the Taiwan (TWN) population genotyped using the Affymetrix Human Mapping 100K Set and 500K Set, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…Single-nucleotide polymorphisms (SNPs), the most abundant genetic markers in the human genome, have been widely used in genetic and genomic research such as studies of disease gene mapping [1-6], medical and clinical diagnostics [7-9], forensic tests [10-12], genome structure of linkage disequilibrium and recombination [13-18], chromosomal aberrations [19-24], and genetic diversity [25-27]. Modern high-throughput and high-resolution SNP array genotyping techniques, such as the Affymetrix GeneChip (Affymetrix Inc., Santa Clara, CA, USA) [28,29] and Illumina BeadChip (Illumina Inc., San Diego, CA, USA) [30-32], provide genotype and fluorescence intensity data on hundreds of thousands of SNPs for each study sample.…”

Section: Introductionmentioning

confidence: 99%

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SAQC: SNP Array Quality Control

et al. 2011

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BackgroundGenome-wide single-nucleotide polymorphism (SNP) arrays containing hundreds of thousands of SNPs from the human genome have proven useful for studying important human genome questions. Data quality of SNP arrays plays a key role in the accuracy and precision of downstream data analyses. However, good indices for assessing data quality of SNP arrays have not yet been developed.ResultsWe developed new quality indices to measure the quality of SNP arrays and/or DNA samples and investigated their statistical properties. The indices quantify a departure of estimated individual-level allele frequencies (AFs) from expected frequencies via standardized distances. The proposed quality indices followed lognormal distributions in several large genomic studies that we empirically evaluated. AF reference data and quality index reference data for different SNP array platforms were established based on samples from various reference populations. Furthermore, a confidence interval method based on the underlying empirical distributions of quality indices was developed to identify poor-quality SNP arrays and/or DNA samples. Analyses of authentic biological data and simulated data show that this new method is sensitive and specific for the detection of poor-quality SNP arrays and/or DNA samples.ConclusionsThis study introduces new quality indices, establishes references for AFs and quality indices, and develops a detection method for poor-quality SNP arrays and/or DNA samples. We have developed a new computer program that utilizes these methods called SNP Array Quality Control (SAQC). SAQC software is written in R and R-GUI and was developed as a user-friendly tool for the visualization and evaluation of data quality of genome-wide SNP arrays. The program is available online (http://www.stat.sinica.edu.tw/hsinchou/genetics/quality/SAQC.htm).

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The International HapMap Project

Cited by 5,321 publications

References 69 publications

Direct mutation analysis by high-throughput sequencing: From germline to low-abundant, somatic variants

Direct mutation analysis by high-throughput sequencing: From germline to low-abundant, somatic variants

Population based allele frequencies of disease associated polymorphisms in the Personalized Medicine Research Project

SAQC: SNP Array Quality Control

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