[How about the uncertainty in the haplotypes in the population-based KORA studies?].
In the KORA surveys, numerous candidate genes in the context of type 2 diabetes, myocardial infarction, atherosclerosis or obesity are under investigation. Current focus is on genotyping single nucleotide polymorphism (SNPs). Haplotypes are also of increasing interest: haplotypes are combinations of alleles within a certain section of one chromosome. Analysing haplotypes in genetic association studies is often more efficient than studying the SNPs separately. A statistical problem in this context is the reconstruction of the phase: genotyping the SNPs determines the alleles of an individual at one particular locus of the DNA, but does not reveal which allele is located on which one of the two chromosomes. This information is required when talking about haplotypes. There are statistical approaches to identify the most likely two haplotypes of an individual given the genotypes. However, a certain error in prognosis is unavoidable. There are also errors in the genotypes. These errors are assumed to be small for one SNP but can accumulate over the SNPs involved in one haplotype and thus can induce further uncertainty in the haplotype. It is therefore the aim of our project to quantify the uncertainties in the haplotypes particularly for genes investigated in the KORA surveys. We conduct computer simulations based on the haplotypes and their frequencies observed in the KORA individuals and compare the results with simulations based on mathematical modelling of the evolutionary process ("coalescent models"). The uncertainties in the haplotypes have an impact on the search for association between genes and disease: an association may not be detected as the haplotype uncertainty obscures the haplotype frequency differences between cases and controls. It is a further aim of our project to elucidate the extent of this problem and to develop strategies for reducing it.
Heid
I M
IM
Institute of Epidemiology, GSF National Research Center for Environment and Health, Neuherberg, Germany. heid@gsf.de
Lamina
C
C
Bongardt
F
F
Fischer
G
G
Klopp
N
N
Huth
C
C
Küchenhoff
H
H
Kronenberg
F
F
Wichmann
H E
HE
Illig
T
T
D000328:Adult; D000368:Aged; D016022:Case-Control Studies; D002874:Chromosome Mapping / Q000379:methods*; D015331:Cohort Studies; D004252:DNA Mutational Analysis / Q000379:methods; D003627:Data Interpretation, Statistical; D005260:Female; D020022:Genetic Predisposition to Disease / Q000453:epidemiology* / Q000235:genetics*; D005820:Genetic Testing / Q000379:methods*; D014644:Genetic Variation / Q000235:genetics*; D005828:Genetics, Population / Q000379:methods*; D005858:Germany / Q000453:epidemiology; D006239:Haplotypes / Q000235:genetics*; D006801:Humans; D015994:Incidence; D015810:Linkage Disequilibrium; D008297:Male; D008875:Middle Aged; D008957:Models, Genetic; D015233:Models, Statistical; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D011159:Population Surveillance / Q000379:methods; D012042:Registries; D012107:Research Design; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors