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Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder caused by a complex interaction of genetic and environmental factors. Increasing evidence highlights a potential role for cholesterol in the pathophysiology of AD. The ABCA1 gene, located in close vicinity to the 9q linkage peaks identified by genome-wide AD linkage studies, plays an important role in cellular cholesterol efflux, and is likely a good candidate gene. However, results from published genetic association studies between ABCA1 and AD are ambiguous. In the present study, we examined the role of two ABCA1 polymorphisms, R219K (rs2230806) and G-17C (rs2740483) in modifying the risk of late-onset AD (LOAD) in a large American white cohort of 992 AD cases and 699 controls. We observed significant gender x R219K interaction (p=0.00008). Female carriers of the 219K allele showed a 1.75-fold increased risk of developing AD compared to non-219K carrier females (95% CI 1.34-2.29; p=0.00004). The overall two-site haplotype distribution was also significant between female AD cases and controls (p=0.017). The risk associated with the R219K polymorphism was independent of the recently reported significant association in the ubiquilin (UBQLN1) gene in this region on chromosome 9q. Our data suggest a gender-specific and APOE and UBQLN1 independent association between the ABCA1/R219K polymorphism and LOAD.
Pubmed ID: 16725228
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A research center associated with the University of Pittsburgh that specializes in the diagnosis of Alzheimer's disease and related disorders. The overall objective of the ADRC is to study the pathophysiology of Alzheimer's disease, with the aim of improving the reliability of diagnosis of Alzheimer's and developing effective treatment strategies. Current research foci emphasize neuropsychiatry and neuropsychology, molecular genetics and epidemiology, basic neuroscience, and structural and functional imaging that aid in the diagnosis and treatment of Alzheimer's disease. Specific services at the ADRC include: comprehensive diagnostic evaluation of patients with suspected Alzheimer's disease and other forms of dementia; evaluation of memory, language, judgment, and other cognitive abilities; and education and counseling for patients and families.
View all literature mentionsEH is a program to test and estimate linkage disequilibrium between different markers or between a disease locus and markers. This is an updated version in which the previous disease (case-control) option has been deleted (but see below how to work with case-control data). The program is written in Free Pascal, which is compatible with (but mch more flexible than) Turbo Pascal. Free Pascal is available for various platforms, e.g. Windows and Linux. The data are taken to consist of a number of individuals collected at random from a population. Based on these sample data, the EH program estimates allele frequencies for each marker. Haplotype frequencies are estimated with allelic association (H1) and without (H0). The EH program also provides log likelihood, chi-square and the number of degrees of freedom under hypotheses H0 and H1. For more information please refer to Terwilliger and Ott (1994). Notes: For sparse data (relatively few observations with large numbers of alleles), the chi-square approximation to the test statistics used by EH is unreliable. Then, more sophisticated programs are recommended. Another program for estimating haplotype frequencies is SNPHAP. It is very flexible although it is restricted to analyzing bi-allelic markers. Also, PHASE is very useful for estimating haplotype frequencies and for inferring haplotypes to individuals. See Marchini et al. (2006). Files in this package (Windows): EH.PAS: Source code of EH program. EH.EXE: Executable code of EH program, which is compiled with a maximum of 30 alleles per locus, 10 loci, 1000 haplotypes, and 3600 genotype patterns (product of numbers of genotypes at each locus). EH.DAT, EH.OUT, etc: Sample input and output files. Input file The EH program does not require additional programs although you need a Pascal compiler (Free Pascal) to recompile the program when you change program constants. There is one input file whose name the user can determine, for example, EH.DAT (this is the default name). It contains the numbers of alleles for each marker and the observations for each genotype. First line: Number of alleles at the first marker, number of alleles at the second marker, and so on. Assuming you have 2 markers, the first marker has 2 alleles and the second marker has 3, you write 2 3 in the first line. The order of markers in the remainder of the input file is determined by the order of markers you entered in the first line. Subsequent lines: Number of observations for given genotypes. These numbers must be arranged as follows: The number of columns is the number of the possible genotypes at the last locus. Let M be the number of alleles at the last locus, then the number of the possible genotypes equals M(M 1)/2. For example, if the last locus has two alleles, then there are 3 possible genotypes which are 1/1, 1/2 and 2/2. Therefore, in each row there are 3 columns corresponding to the genotypes 1/1, 1/2, 2/2. Similarly, if the last marker has three alleles, then there are 6 columns corresponding to 1/1, 1/2, 2/2, 1/3, 2/3, 3/3. The number of rows is the product of the number of the possible genotypes at the first (N - 1) markers, where N is the total number of markers. That is, no. of rows = L1(L1 1)/2 L2(L2 1)/2 ... Li(Li 1)/2 ..., where Li is the number of alleles at the i-th locus. For example, assume you have 3 loci and the first and the second locus each have 2 alleles, and the third locus has 3 alleles. Thus, there are 6 columns and 9 rows (see example 1 below). However, if the first locus has 3 alleles and the second and third have 2 alleles each, there are 18 rows and 3 columns. The output file from the EH program, EH.OUT by default, contains the estimated haplotype frequencies and their corresponding log likelihoods. Sponsor. supported by the Wellcome Trust, the National Institutes of Health (NIH), The SNP Consortium, the Wolfson Foundation, the Nuffield Trust, and the Engineering and Physical Sciences Research Council. M.S. is supported by NIH grant 1RO1HG/LM02585-01. N.P. is a recipient of a K-01 NIH career-transition award. G.R.A. is supported by NIH National Human Genome Research Institute grant HG02651. E.E. is supported by the California Institute for Telecommunications and Information Technology, Calit2. Computational resources for HAP were provided by Calit2 and National Biomedical Computational Resource grant P41 RR08605 (National Center for Research Resources, NIH).
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