Linkage analysis of alcohol dependence using both affected and discordant sib pairs

The basic idea of affected-sib-pair (ASP) linkage analysis is to test whether the inheritance pattern of a marker deviates from Mendelian expectation in a sample of ASPs. The test depends on an assumed Mendelian control distribution of the number of marker alleles shared identical by descent (IBD), i.e., 1/4, 1/2, and 1/4 for 2, 1, and 0 allele(s) IBD, respectively. However, Mendelian transmission may not always hold, for example because of inbreeding or meiotic drive at the marker or a nearby locus. A more robust and valid approach is to incorporate discordant-sib-pairs (DSPs) as controls to avoid possible false-positive results. To be robust to deviation from Mendelian transmission, here we analyzed Collaborative Study on the Genetics of Alcoholism data by modifying the ASP LOD score method to contrast the estimated distribution of the number of allele(s) shared IBD by ASPs with that by DSPs, instead of with the expected distribution under the Mendelian assumption. This strategy assesses the difference in IBD sharing between ASPs and the IBD sharing between DSPs. Further, it works better than the conventional LOD score ASP linkage method in these data in the sense of avoiding false-positive linkage evidence.     

Prediction of empirical p values from asymptotic p values for conditional logistic affected relative pair linkage analysis

OBJECTIVE: p Values are inaccurate for model-free linkage analysis using the conditional logistic model if we assume that the LOD score is asymptotically distributed as a simple mixture of chi-square distributions. When analyzing affected relative pairs alone, permuting the allele sharing of relative pairs does not lead to a useful permutation distribution. As an alternative, we have developed regression prediction models that provide more accurate p values. METHODS: Let E(alpha) be the empirical p value, which is the proportion of statistical tests whose LOD score under the null hypothesis exceeds a threshold determined by alpha, the nominal single test significance value. We used simulated data to obtain values of E(alpha) and compared them with alpha. We also developed a regression model, based on sample size, number of covariates in the model, alpha and marker density, to derive predicted p values for both single-point and multipoint analyses. To evaluate our predictions we used another set of simulated data, comparing the Ealpha for these data with those obtained by using the prediction model, referred to as predicted p values (P(alpha)). RESULTS: Under almost all circumstances the values of P(alpha) were closer to the E(alpha) than were the values of alpha. CONCLUSION: The regression models suggested by our analysis provide more accurate alternative p values for model-free linkage analysis when using the conditional logistic model.     

Effect of genotyping error in model-free linkage analysis using microsatellite or single-nucleotide polymorphism marker maps

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.     

Using overall allele-sharing to detect the presence of large-scale data errors and parameter misspecification in sib-pair linkage studies

Data errors and marker allele frequency misspecification can lead to incorrect inference in linkage analysis. Here we demonstrate the effect of each on an allele-sharing statistic in a sample of sib pairs. In the context of relationship testing, we propose a new test that compares the sample genome-wide sib-pair allele sharing to its expectation and show that this test can detect the presence of large-scale data and model errors.     

Optimizing the evidence for linkage by permuting marker order

We developed a new marker-reordering algorithm to find the best order of fine-mapping markers for multipoint linkage analysis. The algorithm searches for the best order of fine-mapping markers such that the sum of the squared differences in identity-by-descent distribution between neighboring markers is minimized. To test this algorithm, we examined its effect on the evidence for linkage in the simulated and the Collaborative Studies on Genetics of Alcoholism (COGA) data. We found enhanced evidence for linkage with the reordered map at the true location in the simulated data (p-value decreased from 1.16 x 10(-9) to 9.70 x 10(-10)). Analysis of the White population from the COGA data with the reordered map for alcohol dependence led to a significant change of the linkage signal (p = 0.0365 decreased to p = 0.0039) on chromosome 1 between marker D1S1592 and D1S1598. Our results suggest that reordering fine-mapping markers in candidate regions when the genetic map is uncertain can be a critical step when considering a dense map.     

An autosome-wide search using longitudinal data for loci linked to type 2 diabetes progression

Background

Aberrant and non-functional RHD alleles are much more frequent in Africans than in Europeans. The DAU cluster of RHD alleles exemplifies that the alleles frequent in Africans have evaded recognition until recently. A comprehensive survey of RHD alleles in any African population was lacking.

Results

We surveyed the molecular structure and frequency of RHD alleles in Mali (West Africa) by evaluating 116 haplotypes. Only 69% could be attributed to standard RHD (55%) or the RHD deletion (14%). The aberrant RHD allele DAU-0 was predicted for 19%, RHDΨ for 7% and Ccde ^s for 4% of all haplotypes. DAU-3 and the new RHD allele RHD(L207F), dubbed DMA, were found in one haplotype each. A PCR-RFLP for the detection of the hybrid Rhesus box diagnostic for the RHD deletion in Europeans was false positive in 9 individuals, including all carriers of RHDΨ . Including two silent mutations and the RHD deletion, a total of 9 alleles could be differentiated.

Conclusion

Besides standard RHD and the RHD deletion, DAU-0, RHDΨ and Ccde ^s are major alleles in Mali. Our survey proved that the most frequent alleles of West Africans have been recognized allowing to devise reliable genotyping and phenotyping strategies.     

An autosome-wide search using longitudinal data for loci linked to type 2 diabetes progression

A genome-wide screen was conducted for type 2 diabetes progression genes using measures of elevated fasting glucose levels as quantitative traits from the offspring enrolled in the Framingham Heart Study. We analyzed young (20-34 years) and old (>or= 35 years) subjects separately, using single-point and multipoint sibpair analysis, because of the possible differential impact of progression on the groups of interest. We observed significant linkage with change in fasting glucose levels on 1q25-32 (p = 5.21 x 10(-8)), 3p26.3-21.31 (p = 1 x 10(-11)), 8q23.1-24.13 (p = 2.94 x 10(-6)), 9p24.1-21.3 (p = 7 x 10(-7)), and 18p11.31-q22.1 (p < 10(-11)). The evidence for linkage on chromosomes 8 and 18 was consistent for the subset of study participants aged 43 through 55 years.