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Open Access Research article

Enhanced genetic maps from family-based disease studies: population-specific comparisons

Chunsheng He12, Daniel E Weeks3, Steven Buyske14, Goncalo R Abecasis5, William C Stewart6, Tara C Matise1* and The Enhanced Map Consortium7

Author Affiliations

1 Department of Genetics, Rutgers University, Piscataway, NJ, USA

2 Laboratory of Statistical Genetics, Rockefeller University, New York, NY, USA

3 Departments of Human Genetics and Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA

4 Department of Statistics and Biostatistics, Rutgers University, Piscataway, NJ, USA

5 Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA

6 Department of Biostatistics, Columbia University, New York, NY, USA

7 See Acknowledgments for listing of members of the Enhanced Map Consortium

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BMC Medical Genetics 2011, 12:15  doi:10.1186/1471-2350-12-15

Published: 19 January 2011

Abstract

Background

Accurate genetic maps are required for successful and efficient linkage mapping of disease genes. However, most available genome-wide genetic maps were built using only small collections of pedigrees, and therefore have large sampling errors. A large set of genetic studies genotyped by the NHLBI Mammalian Genotyping Service (MGS) provide appropriate data for generating more accurate maps.

Results

We collected a large sample of uncleaned genotype data for 461 markers generated by the MGS using the Weber screening sets 9 and 10. This collection includes genotypes for over 4,400 pedigrees containing over 17,000 genotyped individuals from different populations. We identified and cleaned numerous relationship and genotyping errors, as well as verified the marker orders. We used this dataset to test for population-specific genetic maps, and to re-estimate the genetic map distances with greater precision; standard errors for all intervals are provided. The map-interval sizes from the European (or European descent), Chinese, and Hispanic samples are in quite good agreement with each other. We found one map interval on chromosome 8p with a statistically significant size difference between the European and Chinese samples, and several map intervals with significant size differences between the African American and Chinese samples. When comparing Palauan with European samples, a statistically significant difference was detected at the telomeric region of chromosome 11p. Several significant differences were also identified between populations in chromosomal and genome lengths.

Conclusions

Our new population-specific screening set maps can be used to improve the accuracy of disease-mapping studies. As a result of the large sample size, the average length of the 95% confidence interval (CI) for a 10 cM map interval is only 2.4 cM, which is considerably smaller than on previously published maps.