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Comparative Study
. 2010 Jun 17:11:51.
doi: 10.1186/1471-2156-11-51.

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

Affiliations
Comparative Study

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

Deanna S Cross et al. BMC Genet. .

Abstract

Background: There 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.

Results: There 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.

Conclusions: This 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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Figures

Figure 1
Figure 1
Polymorphisms with major and minor alleles that vary with race. Minor allele frequency for the total population and the same allele for each racial group, with 95% confidence intervals.
Figure 2
Figure 2
Minor allele frequencies of 3 polymorphisms tested in different National populations stratified by race. Comparison populations include NHANES III, CLUE II, PMRP, and dbSNP. A. Caucasian population B. African American Population C. Hispanic Population. Minor allele frequencies include 95% confidence intervals for the population minor allele frequency.
Figure 3
Figure 3
Minor allele frequencies that vary significantly by region of origin. Minor allele frequency for the Caucasian population and the same allele for each region of origin, with 95% confidence intervals.

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References

    1. Galvan A, Ioannidis JP, Dragani TA. Beyond genome-wide association studies: genetic heterogeneity and individual predisposition to cancer. Trends Genet. 2010;26(3):132–141. doi: 10.1016/j.tig.2009.12.008. - DOI - PMC - PubMed
    1. Dadd T, Weale ME, Lewis CM. A critical evaluation of genomic control methods for genetic association studies. Genet Epidemiol. 2009;33(4):290–298. doi: 10.1002/gepi.20379. - DOI - PubMed
    1. Wang K. Testing for genetic association in the presence of population stratification in genome-wide association studies. Genet Epidemiol. 2009;33(7):637–645. doi: 10.1002/gepi.20415. - DOI - PubMed
    1. Guan W, Liang L, Boehnke M, Abecasis GR. Genotype-based matching to correct for population stratification in large-scale case-control genetic association studies. Genet Epidemiol. 2009;33(6):508–517. doi: 10.1002/gepi.20403. - DOI - PMC - PubMed
    1. Yesupriya A, Evangelou E, Kavvoura FK, Patsopoulos NA, Clyne M, Walsh MC, Lin BK, Yu W, Gwinn M, Ioannidis JP. Reporting of human genome epidemiology (HuGE) association studies: an empirical assessment. BMC Med Res Methodol. 2008;8:31. doi: 10.1186/1471-2288-8-31. - DOI - PMC - PubMed

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