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Results for “population stratification”

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PubMed2005

Promotor genotype of the platelet-derived growth factor receptor-alpha gene shows population stratification but not association with spina bifida meningomyelocele.

Neural tube defects (NTDs) constitute a major group of congenital malformations with an overall incidence of approximately 1-2 in 1,000 live births in the United States. Hispanic Americans have a 2.5 times higher risk than the Caucasian population. Spina bifida meningomyelocele (SBMM) is a major clinical presentation of NTDs resulting from lack of closure of the spinal cord caudal to the head. In a previous study of spina bifida (SB) patients of European Caucasian descent, it was suggested that specific haplotypes of the platelet-derived growth factor receptor-alpha (PDGFRA) gene P1 promoter strongly affected the rate of NTD genesis. In our study, we evaluated the association of PDGFRA P1 in a group of 407 parent-child triads (167 Caucasian, 240 Hispanics) and 164 unrelated controls (89 Caucasian, 75 Hispanic). To fully evaluate the association of PDGFRA P1, we performed both transmission-disequilibrium test (TDT) and association analyses to test the hypotheses that PDGFRA P1 was (1) transmitted preferentially in SBMM affected children and (2) associated with the condition of SBMM comparing affected children to unaffected controls. We did find that there was a different allelic and genotypic distribution of PDGFRA P1 when comparing Hispanics and Caucasians. However, neither ethnic group showed strong association between SBMM and the PDGFRA P1 region. These findings suggest that PDGFRA P1 does not have a major role in the development of SBMM.

Adult↗
PubMed1994

DNA profile match probability calculation: how to allow for population stratification, relatedness, database selection and single bands.

In DNA profile analysis, uncertainty arises due to a number of factors such as sampling error, single bands and correlations within and between loci. One of the most important of these factors is kinship: criminal and innocent suspect may share one or more bands through identity by descent from a common ancestor. Ignoring this uncertainty is consistently unfair to innocent suspects. The effect is usually small, but may be important in some cases. The report of the US National Research Committee proposed a complicated, ad-hoc and overly-conservative method of dealing with some of these problems. We propose an alternative approach which addresses directly the effect of kinship. Whilst remaining conservative, it is simple, logically coherent and makes efficient use of the data.

Alleles↗
PubMed2003

On a semiparametric test to detect associations between quantitative traits and candidate genes using unrelated individuals.

Although genetic association studies using unrelated individuals may be subject to bias caused by population stratification, alternative methods that are robust to population stratification such as family-based association designs may be less powerful. Recently, various statistical methods robust to population stratification were proposed for association studies, using unrelated individuals to identify associations between candidate markers and traits of interest (both qualitative and quantitative). Here, we propose a semiparametric test for association (SPTA). SPTA controls for population stratification through a set of genomic markers by first deriving a genetic background variable for each sampled individual through his/her genotypes at a series of independent markers, and then modeling the relationship between trait values, genotypic scores at the candidate marker, and genetic background variables through a semiparametric model. We assume that the exact form of relationship between the trait value and the genetic background variable is unknown and estimated through smoothing techniques. We evaluate the performance of SPTA through simulations both with discrete subpopulation models and with continuous admixture population models. The simulation results suggest that our procedure has a correct type I error rate in the presence of population stratification and is more powerful than statistical association tests for family-based association designs in all the cases considered. Moreover, SPTA is more powerful than the Quantitative Similarity-Based Association Test (QSAT) developed by us under continuous admixture populations, and the number of independent markers needed by SPTA to control for population stratification is substantially fewer than that required by QSAT.

Analysis of Variance↗
PubMed2005

Comparison of three methods to estimate genetic ancestry and control for stratification in genetic association studies among admixed populations.

Population stratification may confound the results of genetic association studies among unrelated individuals from admixed populations. Several methods have been proposed to estimate the ancestral information in admixed populations and used to adjust the population stratification in genetic association tests. We evaluate the performances of three different methods: maximum likelihood estimation, ADMIXMAP and Structure through various simulated data sets and real data from Latino subjects participating in a genetic study of asthma. All three methods provide similar information on the accuracy of ancestral estimates and control type I error rate at an approximately similar rate. The most important factor in determining accuracy of the ancestry estimate and in minimizing type I error rate is the number of markers used to estimate ancestry. We demonstrate that approximately 100 ancestry informative markers (AIMs) are required to obtain estimates of ancestry that correlate with correlation coefficients more than 0.9 with the true individual ancestral proportions. In addition, after accounting for the ancestry information in association tests, the excess of type I error rate is controlled at the 5% level when 100 markers are used to estimate ancestry. However, since the effect of admixture on the type I error rate worsens with sample size, the accuracy of ancestry estimates also needs to increase to make the appropriate correction. Using data from the Latino subjects, we also apply these methods to an association study between body mass index and 44 AIMs. These simulations are meant to provide some practical guidelines for investigators conducting association studies in admixed populations.

Asthma↗
PubMed2005

Large-scale SNP analysis reveals clustered and continuous patterns of human genetic variation.

Understanding the distribution of human genetic variation is an important foundation for research into the genetics of common diseases. Some of the alleles that modify common disease risk are themselves likely to be common and, thus, amenable to identification using gene-association methods. A problem with this approach is that the large sample sizes required for sufficient statistical power to detect alleles with moderate effect make gene-association studies susceptible to false-positive findings as the result of population stratification. Such type I errors can be eliminated by using either family-based association tests or methods that sufficiently adjust for population stratification. These methods require the availability of genetic markers that can detect and, thus, control for sources of genetic stratification among populations. In an effort to investigate population stratification and identify appropriate marker panels, we have analysed 11,555 single nucleotide polymorphisms in 203 individuals from 12 diverse human populations. Individuals in each population cluster to the exclusion of individuals from other populations using two clustering methods. Higher-order branching and clustering of the populations are consistent with the geographic origins of populations and with previously published genetic analyses. These data provide a valuable resource for the definition of marker panels to detect and control for population stratification in population-based gene identification studies. Using three US resident populations (European-American, African-American and Puerto Rican), we demonstrate how such studies can proceed, quantifying proportional ancestry levels and detecting significant admixture structure in each of these populations.

Chromosomes, Human, X↗
PubMed2005

Demonstrating stratification in a European American population.

Population stratification occurs in case-control association studies when allele frequencies differ between cases and controls because of ancestry. Stratification may lead to false positive associations, although this issue remains controversial. Empirical studies have found little evidence of stratification in European-derived populations, but potentially significant levels of stratification could not be ruled out. We studied a European American panel discordant for height, a heritable trait that varies widely across Europe. Genotyping 178 SNPs and applying standard analytical methods yielded no evidence of stratification. But a SNP in the gene LCT that varies widely in frequency across Europe was strongly associated with height (P < 10(-6)). This apparent association was largely or completely due to stratification; rematching individuals on the basis of European ancestry greatly reduced the apparent association, and no association was observed in Polish or Scandinavian individuals. The failure of standard methods to detect this stratification indicates that new methods may be required.

Genetics, Population↗
PubMed2001

Quantitative similarity-based association tests using population samples.

Although genetic association studies using unrelated individuals may be subject to bias caused by population stratification, alternative methods that are robust to population stratification, such as family-based association designs, may be less powerful. Furthermore, it is often more feasible and less expensive to collect unrelated individuals. Recently, several statistical methods have been proposed for case-control association tests in a structured population; these methods may be robust to population stratification. In the present study, we propose a quantitative similarity-based association test (QSAT) to identify association between a candidate marker and a quantitative trait of interest, through use of unrelated individuals. For the QSAT, we first determine whether two individuals are from the same subpopulation or from different subpopulations, using genotype data at a set of independent markers. We then perform an association test between the candidate marker and the quantitative trait, through incorporation of such information. Simulation results based on either coalescent models or empirical population genetics data show that the QSAT has a correct type I error rate in the presence of population stratification and that the power of the QSAT is higher than that of family-based association designs.

Computer Simulation↗
PubMed2003

Qualitative semi-parametric test for genetic associations in case-control designs under structured populations.

Recently, statistical methods have been proposed using genomic markers to control for population stratification in genetic association studies. However, these methods either have unacceptable low power when population stratification becomes strong or cannot control for population stratification well under admixture population models. In this paper, we propose a semiparametric association test to detect genetic association between a candidate marker and a qualitative trait of interest in case-control designs. The performance of the test is compared to other existing methods through simulations. The results show that our method gives correct type I error rate both under discrete population models and admixture population models, and our method is robust to the extent of the population stratification. In most of the cases we considered, our method has higher power and, in some cases, substantially higher power than that of existing methods.

Case-Control Studies↗
PubMed2006

Principal components analysis corrects for stratification in genome-wide association studies.

Population stratification--allele frequency differences between cases and controls due to systematic ancestry differences-can cause spurious associations in disease studies. We describe a method that enables explicit detection and correction of population stratification on a genome-wide scale. Our method uses principal components analysis to explicitly model ancestry differences between cases and controls. The resulting correction is specific to a candidate marker's variation in frequency across ancestral populations, minimizing spurious associations while maximizing power to detect true associations. Our simple, efficient approach can easily be applied to disease studies with hundreds of thousands of markers.

Algorithms↗
PubMed2000

Assessing candidate genes as risk factors for mental disorders: the value of population-based epidemiological studies.

BACKGROUND: It is now possible to routinely collect DNA for the investigation of genetic risk factors in psychiatric epidemiology. However, concerns have been raised about the investigation of genetic risk factors in population surveys and in case-control studies with population controls, because of spurious associations arising through population stratification. To overcome this problem, some geneticists advocate the use of family controls and studies on isolated populations. However, such approaches may not be the best for psychiatric epidemiology. METHOD: A critical analysis is made of arguments against the investigation of genetic risk factors in studies with population controls. RESULTS: It is argued that concerns about population stratification have been overblown. It is simply a form of confounding and can occur in studies of environmental as well as genetic risk factors. Epidemiologists have traditionally dealt with this and other types of confounding by matching on potential confounders in the design of the study, or stratification by confounders in the analysis. Family controls overcome population stratification, but not other forms of confounding. They are a good choice when the sole interest is genetic risk factors, but psychiatric epidemiologists are typically interested in both genetic and environmental factors. Family controls are poor for examining environmental risk factors because they can give rise to over-matching. Isolated populations may produce genetic associations that do not generalize to other populations, and they may have a restricted range of environments. CONCLUSION: We conclude that population surveys and case-control studies using population controls are suitable for the investigation of both genetic and environmental risk factors and have greater public health relevance than alternative designs.

Epidemiologic Studies↗
PubMed2000

Comparison of tests for association and linkage in incomplete families.

To analyze incomplete families, the following statistical tests can be used: LRAT-a simple likelihood-based association test, TRANSMIT, SIBASSOC/STDT, and RCTDT. We compared these four tests, for the diallelic case, on simulated data sets. The comparisons focused on the power to detect linkage and association when different familial structures, resistance to population stratification, resistance to misclassification of the disease status of the healthy sib, and the effect of nonpaternity were considered. The simulations lead to the following conclusions. The type I errors of TRANSMIT, SIBASSOC/STDT, and RCTDT were not affected by population stratification. LRAT showed bias under strong population stratification. High nonpaternity rates can lead to inflated type I errors, highlighting the importance of identification of half sibs. Under different homogeneous models, the power of TRANSMIT was very similar to that of LRAT, and, similarly, no difference in power was observed between SIBASSOC/STDT and RCTDT. Under various recessive and additive models, TRANSMIT was slightly more powerful than SIBASSOC/STDT when monoparental families with one affected and one unaffected sib were analyzed. Under various dominant models, SIBASSOC/STDT was slightly more powerful than TRANSMIT. Misclassification of the disease status of healthy sibs, as well as the discarding of incomplete families, resulted in a consistent loss of power.

Adult↗
PubMed2004

Geographic stratification of linkage disequilibrium: a worldwide population study in a region of chromosome 22.

Recent studies of haplotype diversity in a number of genomic regions have suggested that long stretches of DNA are preserved in the same chromosome, with little evidence of recombination events. The knowledge of the extent and strength of these haplotypes could become a powerful tool for future genetic analysis of complex traits. Different patterns of linkage disequilibrium (LD) have been found when comparing individuals of African and European descent, but there is scarce knowledge about the worldwide population stratification. Thus, the study of haplotype composition and the pattern of LD from a global perspective are relevant for elucidating their geographical stratification, as it may have implications in the future analysis of complex traits. We have typed 12 single nucleotide polymorphisms in a chromosome 22 region--previously described as having high LD levels in European populations--in 39 different world populations. Haplotype structure has a clear continental structure with marked heterogeneity within some continents (Africa, America). The pattern of LD among neighbouring markers exhibits a strong clustering of all East Asian populations on the one hand and of Western Eurasian populations (including Europe) on the other, revealing only two major LD patterns, but with some very specific outliers due to specific demographic histories. Moreover, it should be taken into account that African populations are highly heterogeneous. The present results support the existence of a wide (but not total) communality in LD patterns in human populations from different continental regions, despite differences in their demographic histories, as population factors seem to be less relevant compared with genomic forces in shaping the patterns of LD.

Asia↗