Katrina AB Goddard

P300 amplitude is an electrophysiological quantitative trait that is correlated with both alcoholism and smoking status. Using the Collaborative Study on the Genetics of Alcoholism data, we performed model-free linkage analysis to investigate the relationship between alcoholism, P300 amplitude, and habitual smoking. We also analyzed the effect of parent-of-origin on alcoholism, and utilized both microsatellites (MS) markers and single-nucleotide polymorphisms (SNPs). We found significant evidence of linkage for alcoholism to chromosome 10; inclusion of P300 amplitude as a covariate provided additional evidence of linkage to chromosome 12. This same region on chromosome 12 showed some evidence for a parent-of-origin effect. We found evidence of linkage for the P300 phenotype to chromosome 7 in non-smokers, and to chromosome 17 in alcoholics. The effects of alcoholism and habitual smoking on P300 amplitude appear to have separate genetic determinants. Overall, there were few differences between MS and SNP genome scans. The use of covariates and parent-of-origin effects allowed detection of linkage not seen otherwise.

Errors while genotyping are inevitable and can reduce the power to detect linkage. However, does genotyping error have the same impact on linkage results for single-nucleotide polymorphism (SNP) and microsatellite (MS) marker maps? To evaluate this question we detected genotyping errors that are consistent with Mendelian inheritance using large changes in multipoint identity-by-descent sharing in neighboring markers. Only a small fraction of Mendelian consistent errors were detectable (e.g., 18% of MS and 2.4% of SNP genotyping errors). More SNP genotyping errors are Mendelian consistent compared to MS genotyping errors, so genotyping error may have a greater impact on linkage results using SNP marker maps. We also evaluated the effect of genotyping error on the power and type I error rate using simulated nuclear families with missing parents under 0, 0.14, and 2.8% genotyping error rates. In the presence of genotyping error, we found that the power to detect a true linkage signal was greater for SNP (75%) than MS (67%) marker maps, although there were also slightly more false-positive signals using SNP marker maps (5 compared with 3 for MS). Finally, we evaluated the usefulness of accounting for genotyping error in the SNP data using a likelihood-based approach, which restores some of the power that is lost when genotyping error is introduced.

Background: Maximum likelihood estimates of haplotype frequencies can be obtained from pooled DNA using the expectation maximization (EM) algorithm. Through simulation, we investigate the effect of genotyping error on the accuracy of haplotype frequency estimates obtained using this algorithm. We explore model parameters including allele frequency, inter-marker linkage disequilibrium (LD), genotyping error rate, and pool size. Results: Pool sizes of 2, 5, and 10 individuals achieved comparable levels of accuracy in the estimation procedure. Common marker allele frequencies and no inter-marker LD result in less accurate estimates. This pattern is observed regardless of the amount of genotyping error simulated. Conclusion: Genotyping error slightly decreases the accuracy of haplotype frequency estimates. However, the EM algorithm performs well even in the presence of genotyping error. Overall, pools of 2, 5, and 10 individuals yield similar accuracy of the haplotype frequency estimates, while reducing costs due to genotyping.

Recently, we reported evidence of linkage on chromosome 20 for Alzheimer disease (AD) using a novel statistical approach to incorporate covariates (e.g., age, ApoE genotype) into the analysis. These results suggest that very elderly subjects (>85 years), and individuals who carry an ε2 allele at the ApoE locus are more likely to be linked to this candidate region. The region on chromosome 20 includes a strong candidate gene, cystatin C (CST3), which has previously been associated with AD in case-control studies. We investigated these findings further by genotyping additional markers to narrow the candidate region, and to identify evidence of linkage disequilibrium as additional support for a susceptibility locus on chromosome 20. We selected 43 elderly sibships (89 subjects) from the NIMH AD Genetics Initiative based on current age older than 84 years, and identified 129 unrelated control subjects who were older than 84 years from the Oregon Brain Aging Study to conduct linkage and association studies in this region. Fourteen additional markers were evaluated, including 4 markers located within or near CST3. We narrowed the candidate region on chromosome 20 to an 11.8-cM region between markers D20S174 and D20S471, which includes the CST3 candidate gene. In addition, we observed evidence of association for markers located near the CST3 candidate gene, with P values between 0.002 and 0.08 for two-locus haplotypes. These results support the presence of a susceptibility locus for AD in the vicinity of CST3 for very elderly subjects with AD.

Background: Genetic heterogeneity and complex biologic mechanisms of blood pressure regulation pose significant challenges to the identification of susceptibility loci influencing hypertension. Previous linkage studies have reported regions of interest, but lack consistency across studies. Incorporation of covariates, in particular the interaction between two independent risk factors (gender and BMI) greatly improved our ability to detect linkage. Results: We report a highly significant signal for linkage to chromosome 2p, a region that has been implicated in previous linkage studies, along with several suggestive linkage regions. Conclusion: We demonstrate the importance of including covariates in the linkage analysis when the phenotype is complex.