Literature DB >> 19878600

GLIDERS--a web-based search engine for genome-wide linkage disequilibrium between HapMap SNPs.

Robert Lawrence1, Aaron G Day-Williams, Richard Mott, John Broxholme, Lon R Cardon, Eleftheria Zeggini.   

Abstract

BACKGROUND: A number of tools for the examination of linkage disequilibrium (LD) patterns between nearby alleles exist, but none are available for quickly and easily investigating LD at longer ranges (>500 kb). We have developed a web-based query tool (GLIDERS: Genome-wide LInkage DisEquilibrium Repository and Search engine) that enables the retrieval of pairwise associations with r2 >or= 0.3 across the human genome for any SNP genotyped within HapMap phase 2 and 3, regardless of distance between the markers. DESCRIPTION: GLIDERS is an easy to use web tool that only requires the user to enter rs numbers of SNPs they want to retrieve genome-wide LD for (both nearby and long-range). The intuitive web interface handles both manual entry of SNP IDs as well as allowing users to upload files of SNP IDs. The user can limit the resulting inter SNP associations with easy to use menu options. These include MAF limit (5-45%), distance limits between SNPs (minimum and maximum), r2 (0.3 to 1), HapMap population sample (CEU, YRI and JPT+CHB combined) and HapMap build/release. All resulting genome-wide inter-SNP associations are displayed on a single output page, which has a link to a downloadable tab delimited text file.
CONCLUSION: GLIDERS is a quick and easy way to retrieve genome-wide inter-SNP associations and to explore LD patterns for any number of SNPs of interest. GLIDERS can be useful in identifying SNPs with long-range LD. This can highlight mis-mapping or other potential association signal localisation problems.

Entities:  

Mesh:

Year:  2009        PMID: 19878600      PMCID: PMC2777181          DOI: 10.1186/1471-2105-10-367

Source DB:  PubMed          Journal:  BMC Bioinformatics        ISSN: 1471-2105            Impact factor:   3.169


Background

The discovery of the block structure of haplotypes has led to much research into patterns of local linkage disequilibrium (LD) in the genome [1-3]. The International HapMap Project was motivated by these discoveries to create a fine-scale catalogue of common single nucleotide polymorphisms (SNPs) in different populations to allow further investigations into LD [4,5]. The HapMap has allowed researchers to better understand patterns of LD and utilize the information in the design of genome-wide association studies (GWAS). Several tools have been developed allowing researchers to utilize the HapMap data to investigate LD, including Haploview and SNAP [6,7]. Analysis of HapMap has revealed wide-spread, and often complex, patterns of LD which has made the localization of causal variants difficult in regions identified to be associated with disease through GWAS. Research thus far has been focused on regional patterns of LD and has revealed that LD in most genomic regions decays substantially over several kilobases (Kb) to several hundreds of Kb. Long-range (> 500 Kb) LD has been less well characterized, and at present there are no known resources for researchers to investigate long-range LD. The SNAP server, for example, only allows researchers to investigate markers that are separated by a maximum of 500 Kb. Although regional patterns of LD are indeed very useful, for example in delineating intervals for the follow-up of interesting association signals, it can also be useful to examine patterns of long-range LD. There is a possibility that misplaced SNPs (or potentially epistasis) could produce very long-range cis- (intra-) and even trans- (inter-) chromosomal associations between SNPs. To address the need for a resource investigating long-range LD we have developed the Genome-wide LInkage DisEquilibrium Repository and Search engine (GLIDERS). GLIDERS is a web-based tool allowing researchers to investigate both local and long-range associations between all HapMap phase 2 and 3 SNPs.

Construction and content

Implementation

We computed pairwise r2 and D' among all pairs of SNPs based on genotype data from HapMap2 (release 21 and 23) and HapMap3 (release 2) in three analysis panels (CEU, CHB+JPT and YRI) [4,5]. We store the physical position of each SNP, which is based on genome build 35 for HapMap2 release 21, and build 36 for HapMap2 release 23 and HapMap3 release 2. Before analysis, we performed quality control (QC), in addition to HapMap QC, to minimize genotyping artifacts. The QC analysis was based on insights gained from the Wellcome Trust Case Control Consortium (WTCCC) study [8] and excluded SNPs with ≥ 5% genotype failure rate, MAF < 5%, heterozygosity > 75%, and HWE p-value < 5.7 × 10-7. Additionally, HapMap3 central QC removed many SNPs investigated in HapMap2. The final number of SNPs analyzed after the above QC procedures are seen in Table 1 for each population and HapMap data-set. We based LD calculations in both HapMap2 datasets on 60 founders for the CEU and YRI populations, and 90 founders for the CHB+JPT population. We based HapMap3 LD calculations on 112 CEU, 113 YRI, and 170 CHB+JPT founders. The sample size for the LD calculations is small thereby limiting the power to detect LD. To assess the significance of the LD results GLIDERS has calculated and returns the chi-squared statistic and associated p-value (Bonferroni corrected and uncorrected) for each LD result. In addition to capturing the physical position for each SNP, we also examine its inclusion in the following commercially available genotyping arrays: Affymetrix 100K Mapping Array, Affymetrix 500K Mapping Array, Affymetrix 6.0 Array, Illumina Human-1, Illumina HumanHap300, Illumina HumanCNV370, Illumina HumanHap550, Illumina Human610, Illumina HumanHap650Y, and Illumina Human1M [9,10]. For every SNP examined GLIDERS stores information on all SNPs genome-wide (i.e. for all possible distances along a chromosome as well as across chromosomes) with an r2 ≥ 0.3. GLIDERS does not handle SNP aliasing created by dbSNP updating. The data are stored as text files in a tree-based directory structure for fast query performance. Our analysis shows a nearly linear-time query performance with the number of query SNPs. The parameters that affect query performance are the distance and r^2parameters. The data are accessed and queried by a Perl CGI script.
Table 1

HapMap SNPs analyzed for genome-wide LD post-QC.

HapMap Data-setCEUYRICHB+JPT
HapMap2 r21193700921308271733922
HapMap2 r23202195922025711824262
HapMap3 r2118165912613711086818

This table shows the number of SNPs that passed QC and were analyzed for genome-wide LD patterns for each population in the three HapMap datasets.

HapMap SNPs analyzed for genome-wide LD post-QC. This table shows the number of SNPs that passed QC and were analyzed for genome-wide LD patterns for each population in the three HapMap datasets.

Web Server

The GLIDERS search engine is publicly available at . Detailed documentation can be accessed by a link at the top of the page and includes examples of how to use the application. Users can select which HapMap version and release, and which study population they want to investigate from drop-down lists as seen in Figure 1 (default selections are HapMap3 release 2 and CEU). The user can then enter query SNP(s) by manually entering rsIDs or by uploading a text file of rsIDs. GLIDERS allows users to further restrict their analysis by filtering the results on a MAF cut-off for all returned SNPs, a minimum distance between SNPs, a maximum distance between SNPs, and a minimum r2 value between SNPs. The user is then presented with a table of results, as seen in Figure 2, for each of the query SNPs entered. For each query SNP entered, GLIDERS returns all the SNPs that satisfy the user-defined filters and displays their chromosome, position in base-pairs, MAF, distance from the query SNP in base-pairs, r2 with query SNP, D' with the query SNP, and all the commercially available chips that the SNP is included in. Further information is available by clicking the rsID, which takes the user to the dbSNP record for that SNP [11]. In addition to the web-based table, users can download a text file of the results by clicking a button at the top or bottom of the results page. If any of the query SNPs were not analyzed because they failed the QC analysis detailed above, the user is informed and told which of the QC filters the SNP did not pass. Users are also informed if a query SNP cannot be located in the HapMap data.
Figure 1

GLIDERS home page. The top of the home page provides a link to detailed instructions about how to use the application. Below the instructions link are the search configuration controls. The user can select the HapMap population and HapMap release to query, and the user can restrict the results based on distance, MAF, and r2 values. The user can also select to be informed of the inclusion of the resulting SNP(s) on several commercial genotyping arrays.

Figure 2

GLIDERS results page. The top of the page displays the HapMap phase, build, and population queried, as well as any user specified query restrictions. Below the query information are the results, with the results for each query SNP in its own table. The first SNP in each table is the query SNP, followed by all the SNPs in the genome that meet the search criteria. The information displayed for each SNP includes the chromosome, base pair position, MAF, distance from query SNP, r2 with query SNP, D' with query SNP, chi-squared statistic and p-value (Bonferroni corrected and uncorrected), and information about its membership on selected commercial arrays. Additional information on each SNP can be obtained by clicking the SNP name which takes the user to the dbSNP record for that SNP. The results page also informs users if any query SNP failed the QC criteria, is not a SNP in HapMap, or whether no SNPs met the specified search criteria. The user is also provided with a link at the top and bottom of the page to download a tab-delimited text file of the results.

GLIDERS home page. The top of the home page provides a link to detailed instructions about how to use the application. Below the instructions link are the search configuration controls. The user can select the HapMap population and HapMap release to query, and the user can restrict the results based on distance, MAF, and r2 values. The user can also select to be informed of the inclusion of the resulting SNP(s) on several commercial genotyping arrays. GLIDERS results page. The top of the page displays the HapMap phase, build, and population queried, as well as any user specified query restrictions. Below the query information are the results, with the results for each query SNP in its own table. The first SNP in each table is the query SNP, followed by all the SNPs in the genome that meet the search criteria. The information displayed for each SNP includes the chromosome, base pair position, MAF, distance from query SNP, r2 with query SNP, D' with query SNP, chi-squared statistic and p-value (Bonferroni corrected and uncorrected), and information about its membership on selected commercial arrays. Additional information on each SNP can be obtained by clicking the SNP name which takes the user to the dbSNP record for that SNP. The results page also informs users if any query SNP failed the QC criteria, is not a SNP in HapMap, or whether no SNPs met the specified search criteria. The user is also provided with a link at the top and bottom of the page to download a tab-delimited text file of the results.

Utility and Discussion

GLIDERS is an efficient and easy tool allowing researchers to find genome-wide LD between all SNPs investigated in HapMap phase 2 and 3. GLIDERS is not the only tool for interrogating LD in the HapMap data, but it has certain features that set it apart. Firstly, GLIDERS does not require users to download and install software on their local machine, like the popular Haploview program requires[6]. Additionally, GLIDERS has pre-calculated and compiled all the LD information therefore taking the computational burden off the individual researcher. It would also be possible for researchers to perform the same analysis using Haploview by downloading all the phased HapMap genotypes, setting the maximum distance parameter to 0, and exporting the LD values. Then the researcher would have to parse all of that output. Of the available LD resources, SNAP [7] is the tool that most closely resembles GLIDERS. SNAP is a very good utility but is limited to investigating LD between markers that are a maximum of 500 Kb apart, whereas GLIDERS has an unlimited distance search space. However, because of the greatly expanded distance search space, GLIDERS has an r2 lower bound of 0.3, whereas SNAP has an unlimited r2 search space for SNP associations. Therefore, GLIDERS should be viewed as a complementary tool to SNAP. GLIDERS provides a quick list of potential proxy SNPs and an indication of the extent of LD between the potential proxies and the query SNP(s). GLIDERS also provides insight into potential SNP mapping errors or more interesting biological processes by revealing strong associations between SNPs on different chromosomes. The number of HapMap phase 3 SNPs demonstrating at least one trans-chromosomal association with r2 ≥ 0.3 in the three panel populations are: 17,562 in CEU, 27,064 in YRI, and 1,497 in JPT+CHB. These associations either reveal mapping errors, interesting biology, or that these populations are still young populations that have not had enough time to degrade the LD amongst unlinked loci. Our analysis has also revealed potential data quality issues between HapMap2 and HapMap3, since many of the long-range associations we discovered in the HapMap2 data disappear in HapMap3. This, we believe, is likely to be due to the increased sample size in phase 3 and the removal of poorly performing phase 2 SNPs from phase 3. Therefore we recommend querying your SNPs against HapMap3, with the trade off that there are fewer SNPs analyzed in HapMap3 because of the more stringent QC analysis.

Conclusion

With the large number of GWAS being carried out and the large number of SNPs showing disease association, it is important to be able to check quickly and easily for distant SNP proxies which might affect disease gene localization. GLIDERS is an ideal utility for this as it contains all genome-wide inter-SNP associations between HapMap markers from the three main population samples calculated using both phase 2 and phase 3 genotypes. GLIDERS is an easy to use inter-SNP association web database tool that can be used by any researcher with internet access.

Availability and requirements

The GLIDERS search engine is publicly available at . There are no restrictions to the use of GLIDERS by academic or commercial entities.

List of abbreviations

GWAS: (genome-wide association study); LD: (linkage disequilibrium); MAF: (minor allele frequency); SNP: (single nucleotide polymorphism); GLIDERS: (Genome-wide LInkage DisEquilibrium Repository and Search engine); CEU: (CEPH (Utah residents with ancestry from northern and western Europe)); YRI: (Yoruba in Ibadan, Nigeria); JPT: (Japanese in Tokyo, Japan); CHB: (Han Chinese in Beijing, China); CGI: (Common Gateway Interface); SNAP: (SNP Annotation and Proxy Search); rsID: (reference SNP identification); QC: (quality control); Kb: (kilobase).

Authors' contributions

RL carried out LD calculations, developed the database and drafted the manuscript. ADW drafted the manuscript and contributed to the web server set-up. RM provided software for LD calculation. JB contributed to website design. LC contributed to database design and supervised the project. EZ supervised the project and drafted the manuscript. All authors have read and approved the manuscript.
  7 in total

1.  High-resolution haplotype structure in the human genome.

Authors:  M J Daly; J D Rioux; S F Schaffner; T J Hudson; E S Lander
Journal:  Nat Genet       Date:  2001-10       Impact factor: 38.330

2.  The structure of haplotype blocks in the human genome.

Authors:  Stacey B Gabriel; Stephen F Schaffner; Huy Nguyen; Jamie M Moore; Jessica Roy; Brendan Blumenstiel; John Higgins; Matthew DeFelice; Amy Lochner; Maura Faggart; Shau Neen Liu-Cordero; Charles Rotimi; Adebowale Adeyemo; Richard Cooper; Ryk Ward; Eric S Lander; Mark J Daly; David Altshuler
Journal:  Science       Date:  2002-05-23       Impact factor: 47.728

3.  Haploview: analysis and visualization of LD and haplotype maps.

Authors:  J C Barrett; B Fry; J Maller; M J Daly
Journal:  Bioinformatics       Date:  2004-08-05       Impact factor: 6.937

4.  SNAP: a web-based tool for identification and annotation of proxy SNPs using HapMap.

Authors:  Andrew D Johnson; Robert E Handsaker; Sara L Pulit; Marcia M Nizzari; Christopher J O'Donnell; Paul I W de Bakker
Journal:  Bioinformatics       Date:  2008-10-30       Impact factor: 6.937

Review 5.  Haplotype blocks and linkage disequilibrium in the human genome.

Authors:  Jeffrey D Wall; Jonathan K Pritchard
Journal:  Nat Rev Genet       Date:  2003-08       Impact factor: 53.242

6.  A second generation human haplotype map of over 3.1 million SNPs.

Authors:  Kelly A Frazer; Dennis G Ballinger; David R Cox; David A Hinds; Laura L Stuve; Richard A Gibbs; John W Belmont; Andrew Boudreau; Paul Hardenbol; Suzanne M Leal; Shiran Pasternak; David A Wheeler; Thomas D Willis; Fuli Yu; Huanming Yang; Changqing Zeng; Yang Gao; Haoran Hu; Weitao Hu; Chaohua Li; Wei Lin; Siqi Liu; Hao Pan; Xiaoli Tang; Jian Wang; Wei Wang; Jun Yu; Bo Zhang; Qingrun Zhang; Hongbin Zhao; Hui Zhao; Jun Zhou; Stacey B Gabriel; Rachel Barry; Brendan Blumenstiel; Amy Camargo; Matthew Defelice; Maura Faggart; Mary Goyette; Supriya Gupta; Jamie Moore; Huy Nguyen; Robert C Onofrio; Melissa Parkin; Jessica Roy; Erich Stahl; Ellen Winchester; Liuda Ziaugra; David Altshuler; Yan Shen; Zhijian Yao; Wei Huang; Xun Chu; Yungang He; Li Jin; Yangfan Liu; Yayun Shen; Weiwei Sun; Haifeng Wang; Yi Wang; Ying Wang; Xiaoyan Xiong; Liang Xu; Mary M Y Waye; Stephen K W Tsui; Hong Xue; J Tze-Fei Wong; Luana M Galver; Jian-Bing Fan; Kevin Gunderson; Sarah S Murray; Arnold R Oliphant; Mark S Chee; Alexandre Montpetit; Fanny Chagnon; Vincent Ferretti; Martin Leboeuf; Jean-François Olivier; Michael S Phillips; Stéphanie Roumy; Clémentine Sallée; Andrei Verner; Thomas J Hudson; Pui-Yan Kwok; Dongmei Cai; Daniel C Koboldt; Raymond D Miller; Ludmila Pawlikowska; Patricia Taillon-Miller; Ming Xiao; Lap-Chee Tsui; William Mak; You Qiang Song; Paul K H Tam; Yusuke Nakamura; Takahisa Kawaguchi; Takuya Kitamoto; Takashi Morizono; Atsushi Nagashima; Yozo Ohnishi; Akihiro Sekine; Toshihiro Tanaka; Tatsuhiko Tsunoda; Panos Deloukas; Christine P Bird; Marcos Delgado; Emmanouil T Dermitzakis; Rhian Gwilliam; Sarah Hunt; Jonathan Morrison; Don Powell; Barbara E Stranger; Pamela Whittaker; David R Bentley; Mark J Daly; Paul I W de Bakker; Jeff Barrett; Yves R Chretien; Julian Maller; Steve McCarroll; Nick Patterson; Itsik Pe'er; Alkes Price; Shaun Purcell; Daniel J Richter; Pardis Sabeti; Richa Saxena; Stephen F Schaffner; Pak C Sham; Patrick Varilly; David Altshuler; Lincoln D Stein; Lalitha Krishnan; Albert Vernon Smith; Marcela K Tello-Ruiz; Gudmundur A Thorisson; Aravinda Chakravarti; Peter E Chen; David J Cutler; Carl S Kashuk; Shin Lin; Gonçalo R Abecasis; Weihua Guan; Yun Li; Heather M Munro; Zhaohui Steve Qin; Daryl J Thomas; Gilean McVean; Adam Auton; Leonardo Bottolo; Niall Cardin; Susana Eyheramendy; Colin Freeman; Jonathan Marchini; Simon Myers; Chris Spencer; Matthew Stephens; Peter Donnelly; Lon R Cardon; Geraldine Clarke; David M Evans; Andrew P Morris; Bruce S Weir; Tatsuhiko Tsunoda; James C Mullikin; Stephen T Sherry; Michael Feolo; Andrew Skol; Houcan Zhang; Changqing Zeng; Hui Zhao; Ichiro Matsuda; Yoshimitsu Fukushima; Darryl R Macer; Eiko Suda; Charles N Rotimi; Clement A Adebamowo; Ike Ajayi; Toyin Aniagwu; Patricia A Marshall; Chibuzor Nkwodimmah; Charmaine D M Royal; Mark F Leppert; Missy Dixon; Andy Peiffer; Renzong Qiu; Alastair Kent; Kazuto Kato; Norio Niikawa; Isaac F Adewole; Bartha M Knoppers; Morris W Foster; Ellen Wright Clayton; Jessica Watkin; Richard A Gibbs; John W Belmont; Donna Muzny; Lynne Nazareth; Erica Sodergren; George M Weinstock; David A Wheeler; Imtaz Yakub; Stacey B Gabriel; Robert C Onofrio; Daniel J Richter; Liuda Ziaugra; Bruce W Birren; Mark J Daly; David Altshuler; Richard K Wilson; Lucinda L Fulton; Jane Rogers; John Burton; Nigel P Carter; Christopher M Clee; Mark Griffiths; Matthew C Jones; Kirsten McLay; Robert W Plumb; Mark T Ross; Sarah K Sims; David L Willey; Zhu Chen; Hua Han; Le Kang; Martin Godbout; John C Wallenburg; Paul L'Archevêque; Guy Bellemare; Koji Saeki; Hongguang Wang; Daochang An; Hongbo Fu; Qing Li; Zhen Wang; Renwu Wang; Arthur L Holden; Lisa D Brooks; Jean E McEwen; Mark S Guyer; Vivian Ota Wang; Jane L Peterson; Michael Shi; Jack Spiegel; Lawrence M Sung; Lynn F Zacharia; Francis S Collins; Karen Kennedy; Ruth Jamieson; John Stewart
Journal:  Nature       Date:  2007-10-18       Impact factor: 49.962

7.  Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls.

Authors: 
Journal:  Nature       Date:  2007-06-07       Impact factor: 49.962

  7 in total
  17 in total

1.  Sporadic, Global Linkage Disequilibrium Between Unlinked Segregating Sites.

Authors:  Daniel A Skelly; Paul M Magwene; Eric A Stone
Journal:  Genetics       Date:  2015-12-29       Impact factor: 4.562

2.  Genome-wide "pleiotropy scan" identifies HNF1A region as a novel pancreatic cancer susceptibility locus.

Authors:  Brandon L Pierce; Habibul Ahsan
Journal:  Cancer Res       Date:  2011-04-15       Impact factor: 12.701

3.  The rs4774 CIITA missense variant is associated with risk of systemic lupus erythematosus.

Authors:  P G Bronson; B A Goldstein; P P Ramsay; K B Beckman; J A Noble; J A Lane; M F Seldin; J A Kelly; J B Harley; K L Moser; P M Gaffney; T W Behrens; L A Criswell; L F Barcellos
Journal:  Genes Immun       Date:  2011-05-26       Impact factor: 2.676

4.  Association study of type 2 diabetes genetic susceptibility variants and risk of pancreatic cancer: an analysis of PanScan-I data.

Authors:  Brandon L Pierce; Melissa A Austin; Habibul Ahsan
Journal:  Cancer Causes Control       Date:  2011-03-29       Impact factor: 2.506

Review 5.  Candidate gene association studies: a comprehensive guide to useful in silico tools.

Authors:  Radhika Patnala; Judith Clements; Jyotsna Batra
Journal:  BMC Genet       Date:  2013-05-09       Impact factor: 2.797

6.  Maps of open chromatin guide the functional follow-up of genome-wide association signals: application to hematological traits.

Authors:  Dirk S Paul; James P Nisbet; Tsun-Po Yang; Stuart Meacham; Augusto Rendon; Katta Hautaviita; Jonna Tallila; Jacqui White; Marloes R Tijssen; Suthesh Sivapalaratnam; Hanneke Basart; Mieke D Trip; Berthold Göttgens; Nicole Soranzo; Willem H Ouwehand; Panos Deloukas
Journal:  PLoS Genet       Date:  2011-06-30       Impact factor: 5.917

7.  Effect modification by population dietary folate on the association between MTHFR genotype, homocysteine, and stroke risk: a meta-analysis of genetic studies and randomised trials.

Authors:  Michael V Holmes; Paul Newcombe; Jaroslav A Hubacek; Reecha Sofat; Sally L Ricketts; Jackie Cooper; Monique M B Breteler; Leonelo E Bautista; Pankaj Sharma; John C Whittaker; Liam Smeeth; F Gerald R Fowkes; Ale Algra; Veronika Shmeleva; Zoltan Szolnoki; Mark Roest; Michael Linnebank; Jeppe Zacho; Michael A Nalls; Andrew B Singleton; Luigi Ferrucci; John Hardy; Bradford B Worrall; Stephen S Rich; Mar Matarin; Paul E Norman; Leon Flicker; Osvaldo P Almeida; Frank M van Bockxmeer; Hiroshi Shimokata; Kay-Tee Khaw; Nicholas J Wareham; Martin Bobak; Jonathan A C Sterne; George Davey Smith; Philippa J Talmud; Cornelia van Duijn; Steve E Humphries; Jackie F Price; Shah Ebrahim; Debbie A Lawlor; Graeme J Hankey; James F Meschia; Manjinder S Sandhu; Aroon D Hingorani; Juan P Casas
Journal:  Lancet       Date:  2011-07-29       Impact factor: 79.321

8.  Systematic detection of epistatic interactions based on allele pair frequencies.

Authors:  Marit Ackermann; Andreas Beyer
Journal:  PLoS Genet       Date:  2012-02-09       Impact factor: 5.917

9.  A genome-wide association study of resistance to stripe rust (Puccinia striiformis f. sp. tritici) in a worldwide collection of hexaploid spring wheat (Triticum aestivum L.).

Authors:  Marco Maccaferri; Junli Zhang; Peter Bulli; Zewdie Abate; Shiaoman Chao; Dario Cantu; Eligio Bossolini; Xianming Chen; Michael Pumphrey; Jorge Dubcovsky
Journal:  G3 (Bethesda)       Date:  2015-01-20       Impact factor: 3.154

10.  Mixed modeling of meta-analysis P-values (MixMAP) suggests multiple novel gene loci for low density lipoprotein cholesterol.

Authors:  Andrea S Foulkes; Gregory J Matthews; Ujjwal Das; Jane F Ferguson; Rongheng Lin; Muredach P Reilly
Journal:  PLoS One       Date:  2013-02-06       Impact factor: 3.240

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.