DNAprint Genomics (DNAG)

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Professor of Metabolic and Genetic Epidemiology: Paul McKeigue - http://www.lshtm.ac.uk/ncdeu/genetics/index.html
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Genetic Epidemiology Group
Professor of Metabolic and Genetic Epidemiology: Paul McKeigue, PhD, FFPHM
Clinical Lecturer: Mariam Molokhia, MRCGP
Research Fellow: Clive Hoggart, PhD


Contents of this page
Current research
ADMIXMAP program
EUDRAGENE
Other Software
Recent Publications
Current research

The main focus of our research is on admixture mapping. This is a novel approach to finding genes that underlie ethnic variation in disease risk, based on studying populations of mixed descent. Admixture mapping is based on the same principles as linkage analysis of an experimental cross between inbred strains. Using panels of markers that are chosen to be highly informative for ancestry, it is possible in principle to extend this approach to admixed human populations where the history of admixture is not under experimental control and the ancestral populations are not inbred strains [19]. Our work in this area is an extension of earlier work on the epidemiology of ethnic variation in risk of cardiovascular disease and diabetes.

The advantage of admixture mapping, in comparison with conventional approaches to localizing disease genes based on family linkage studies, is that in principle it has far greater power than family linkage studies to detect genes of modest effect. This is because admixture mapping is a based on a direct (fixed effects) comparison, whereas family linkage studies are based on an indirect (random effects) statistical comparison. Applying admixture mapping to search the genome requires development of statistical methods that can be applied to phenotypic traits and marker data to extract the information about genetic linkage that is generated by admixture. We have developed a first working version of ADMIXMAP, a statistical analysis program that can be used to model admixture and to test for linkage. This program is based on a Bayesian approach in which the posterior distribution of parental admixture and individual ancestry at each locus is generated by Markov chain Monte Carlo simulation [8].

Our research on admixture mapping is supported by the US National Institutes of Health, the UK Medical Research Council, the Arthritis Research Campaign, and GlaxoSmithKline. We are working closely with the Department of Anthropology, Penn State University on the application of admixture mapping to African-American populations, and on the development of marker sets for admixture mapping. We are collaborating with other researchers in the Caribbean region.

ADMIXMAP program

This is a general-purpose program for modelling admixture, using marker genotypes and trait data on a sample of individuals from an admixed population (such as African-Americans), where the markers have been chosen to have extreme differentials in allele frequencies between two or more of the ancestral populations between which admixture has occurred. The main difference between ADMIXMAP and classical programs for estimation of admixture such as ADMIX is that ADMIXMAP is based on a multilevel model for the distribution of individual admixture in the population and the stochastic variation of ancestry on hybrid chromosomes. This makes it possible to model the associations of ancestry between linked marker loci, and the association of a trait with individual admixture or with ancestry at a linked marker locus.

Possible uses of the ADMIXMAP program


Modelling the distribution of individual admixture values and the history of admixture (inferred by modelling the stochastic variation of ancestry along chromosomes).
Case-control, cross-sectional or cohort studies that test for a relationship between disease risk and individual admixture
Localizing genes underlying ethnic differences in disease risk by admixture mapping
Controlling for population structure (variation in individual admixture) in genetic association studies so as to eliminate associations with unlinked genes
Reconstructing the genetic structure of an ancestral population where unadmixed modern descendants are not available for study
ADMIXMAP can model admixture between more than two populations, and can use data from multi-allelic or biallelic marker polymorphisms. The program has been developed for application to admixed human populations, but can also be used to model admixture in livestock or for fine mapping of quantitative trait loci in outbred stocks of mice.
A manual for the program is available which describes the statistical model in more detail. Downloads of the program compiled for various platforms are also available. We recommend that before trying to run the program, you consult us first about your requirements.

Download ADMIXMAP

ADMIXMAP documentation
Download ADMIXMAP for Linux admix-1.2-linux.tar.gz
Download ADMIXMAP for Windows
ADMIXMAP tutorial for Windows
ADMIXMAP tutorial (HTML) (pdf)
EUDRAGENE: European collaboration to establish a case-control DNA collection for studying the genetic basis of adverse drug reactions

The specific objectives of this proposed collection is to establish a freely-shared case-control collection of DNA samples as a resource for studying genetic predictors of adverse drug reactions. Identifying genetic variants that influence susceptibility to adverse reactions will advance understanding of the molecular basis of adverse drug reactions and may also lead to the development of tests that can predict individual susceptibility to adverse reactions, with obvious benefits to human health. This study has received infrastructure funding for 3 years (starting Jan 03) from the EC 5th Framework Quality of Life Program.
Adverse drug reactions (ADRs) are important causes of morbidity and mortality, limit the usefulness of many otherwise effective drugs, and are under strong genetic influence. Identifying genetic variants that influence susceptibility to ADRs has obvious practical applications, and more generally will contribute to understanding of the molecular basis of adverse drug reactions. Research in this area is hampered by the lack of a resource in which to study genetic determinants of susceptibility to ADRs. As most such ADRs are rare, a case-control design is the only feasible approach, and a multicentre European collaboration is necessary as no single country will generate enough cases of any given ADR within a reasonable time.

We propose to establish a freely-shared resource consisting of clinical data and DNA samples from cases of ADRs, together with a control group. In the first year we plan to select for study an initial set of six ADRs that are important because they cause serious illness in a small minority of those exposed to drugs that are otherwise more effective than any alternative, and that are easily identified because they have distinctive manifestations that are not related to the disease for which the drug was prescribed. At least 500 cases of each ADR will be collected, together with an equal number of controls. The collection will be extended to include more ADRs after the first 1-2 years, based on problems of current concern.


Other Software

prepKbio1.1.zip - Windows program for: concatenating multiple Kbioscience results into a single file, reordering the loci into the order they appear on the chromosome, and adding pedigree information to the Kbioscience results. SnpViewer can then be used to turn this single set of results into text file in the pedfile format, which is a data format required by many genetic analysis programs. Full instructions on how to use prepKbio come with the program. If you have any problems email: Richard.Sharplshtm.ac.uk

Recent publications


Hoggart CJ, Parra EJ, Shriver MD, Bonilla C, Kittles RA, Clayton DG, McKeigue PM. Control of confounding of genetic associations in stratified populations. Am J Hum Genet. 2003, in press.
Colhoun HM, McKeigue PM, Davey Smith G. Problems of reporting genetic associations with complex outcomes. Lancet. 2003 Mar 8;361(9360):865-72. Review.
Shriver MD, Parra EJ, Dios S, Bonilla C, Norton H, Jovel C, Pfaff C, Jones C, Massac A, Cameron N, Baron A, Jackson T, Argyropoulos G, Jin L, Hoggart CJ, McKeigue PM, Kittles RA. Skin pigmentation, biogeographical ancestry and admixture mapping. Hum Genet. 2003 Apr;112(4):387-99.
Molokhia M, Hoggart C, Patrick AL, Shriver M, Parra E, Ye J, Silman AJ, McKeigue PM. Relation of risk of systemic lupus erythematosus to west African admixture in a Caribbean population. Hum Genet. 2003 Mar;112(3):310-8.
Reynolds RM, Chapman KE, Seckl JR, Walker BR, McKeigue PM, Lithell HO. Skeletal muscle glucocorticoid receptor density and insulin resistance. JAMA. 2002 May 15;287(19):2505-6.
Clayton D, McKeigue PM. Epidemiological methods for studying genes and environmental factors in complex diseases. Lancet. 2001 Oct 20;358(9290):1356-60. Review.
Molokhia M, McKeigue PM, Cuadrado M, Hughes G. Systemic lupus erythematosus in migrants from west Africa compared with Afro-Caribbean people in the UK. Lancet. 2001 May 5;357(9266):1414-5.
McKeigue PM, Carpenter JR, Parra EJ, Shriver MD. Estimation of admixture and detection of linkage in admixed populations by a Bayesian approach: application to African-American populations. Ann Hum Genet. 2000 Mar;64(Pt 2):171-86.
Parra EJ, Kittles RA, Argyropoulos G, Pfaff CL, Hiester K, Bonilla C et al. Ancestral proportions and admixture dynamics in geographically defined African-Americans living in South Carolina. American Journal of Physical Anthropology 2001;114:18-29.
Pfaff CL, Parra EJ, Bonilla C, Hiester K, McKeigue PM, Kamboh MI, Hutchinson RG, Ferrell RE, Boerwinkle E, Shriver MD. Population structure in admixed populations: effect of admixture dynamics on the pattern of linkage disequilibrium. Am J Hum Genet. 2001 Jan;68(1):198-207.
McKeigue PM. Multipoint admixture mapping. Genet Epidemiol. 2000 Dec;19(4):464-7.
Molokhia M, McKeigue P. Risk for rheumatic disease in relation to ethnicity and admixture. Arthritis Res. 2000;2(2):115-25. Review.
McKeigue PM. Efficiency of estimation of haplotype frequencies: use of marker phenotypes of unrelated individuals versus counting of phase-known gametes. Am J Hum Genet. 2000 Dec;67(6):1626-7.
Aitman TJ, Cooper LD, Norsworthy PJ, Wahid FN, Gray JK, Curtis BR, McKeigue PM, Kwiatkowski D, Greenwood BM, Snow RW, Hill AV, Scott J. Malaria susceptibility and CD36 mutation. Nature. 2000 Jun 29;405(6790):1015-6.
Zoratti R, Godsland IF, Chaturvedi N, Crook D, Crook D, Stevenson JC, McKeigue PM. Relation of plasma lipids to insulin resistance, nonesterified fatty acid levels, and body fat in men from three ethnic groups: relevance to variation in risk of diabetes and coronary disease. Metabolism. 2000 Feb;49(2):245-52.
Davey G, Ramachandran A, Snehalatha C, Hitman GA, McKeigue PM. Familial aggregation of central obesity in Southern Indians. Int J Obes Relat Metab Disord. 2000 Nov;24(11):1523-7.
Forouhi N, Jenkinson G, Thomas EL, Mierisova S, Bhonsle J, McKeigue PM et al. Relation of triglyceride stores in skeletal muscle cells to central obesity and insulin sensitivity in South Asian and European men. Diabetologia 1999;42:932-5.
McKeigue PM. Ethnic variation in insulin resistance and risk of Type 2 diabetes. In: Reaven G, Laws A, eds. Insulin Resistance, Totowa, NJ: Humana, 1999: 35-51.
Al-Mahroos F, McKeigue PM. High prevalence of diabetes mellitus in Bahrainis: associations with ethnicity and raised plasma cholesterol. Diabetes Care 1998; 21: 936-42.
McKeigue PM. Mapping genes that underlie ethnic differences in disease risk: methods for detecting linkage in admixed populations by conditioning on parental admixture. American Journal of Human Genetics 1998; 63: 241-51.
McKeigue PM. Mapping genes underlying ethnic differences in disease risk by linkage disequilibrium in recently admixed populations. American Journal of Human Genetics 1997; 60: 188-96.
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