Efficiency control in large-scale genotyping using analysis of variance.

The efficiency of the genotyping process is determined by many simultaneous factors. In actual genotyping, a production run is often preceded by small-scale experiments to find optimal conditions. We propose to use statistical analysis of production run data as well, to gain insight into factors important for the outcome of genotyping. As an example, we show that analysis of variance (ANOVA) applied to the first-pass results of a genetic study reveals important determinants of genotyping success. The largest factor limiting genotyping appeared to be interindividual variation among DNA samples, explaining 20% of the variance, and a smaller reaction volume, sizing failure, and differences among markers all explained approximately 10%. Other potentially important factors, such as sample position within the plate and reusing electrophoresis matrix, appeared to be of minor influence. About 55% of the total variance could be explained by systematic factors. These results show that ANOVA can provide valuable feedback to improve genotyping efficiency. We propose to adjust genotype production runs using principles of experimental design in order to maximize genotyping efficiency at little additional cost.

Association testing by haplotype-sharing methods applicable to whole-genome analysis.

We propose two new haplotype-sharing methods for identifying disease loci: the haplotype sharing statistic (HSS), which compares length of shared haplotypes between cases and controls, and the CROSS test, which tests whether a case and a control haplotype show less sharing than two random haplotypes. The significance of the HSS is determined using a variance estimate from the theory of U-statistics, whereas the significance of the CROSS test is estimated from a sequential randomization procedure. Both methods are fast and hence practical, even for whole-genome screens with high marker densities. We analyzed data sets of Problems 2 and 3 of Genetic Analysis Workshop 15 and compared HSS and CROSS to conventional association methods. Problem 2 provided a data set of 2300 single-nucleotide polymorphisms (SNPs) in a 10-Mb region of chromosome 18q, which had shown linkage evidence for rheumatoid arthritis. The CROSS test detected a significant association at approximately position 4407 kb. This was supported by single-marker association and HSS. The CROSS test outperformed them both with respect to significance level and signal-to-noise ratio. A 20-kb candidate region could be identified. Problem 3 provided a simulated 10 k SNP data set covering the whole genome. Three known candidate regions for rheumatoid arthritis were detected. Again, the CROSS test gave the most significant results. Furthermore, both the HSS and the CROSS showed better fine-mapping accuracy than straightforward haplotype association. In conclusion, haplotype sharing methods, particularly the CROSS test, show great promise for identifying disease gene loci.

A factorial experiment for optimizing the PCR conditions in routine genotyping.

Although most PCRs would produce proper PCR products when first tried, a general optimization is required to yield the best results. This optimization is often achieved by changing one factor at a time. However, this may lead to suboptimal results, since interactions between conditions are difficult to detect with this approach. In the present study, we describe the factorial optimization of PCR conditions for microsatellite genotyping, by introducing small systematic variations in conditions during the genotyping process. The hypothesis was that small changes will not affect genotyping results, but will provide information about the optimality of current conditions. The conditions to vary were the concentrations of buffer, MgCl(2), dNTPs, primers, Taq polymerase and DNA, the annealing temperature (T(a)) and the number of cycles. We show that, by a 2(8) factorial experiment, it is possible to identify not only the factors responsible for obtaining good results, but also those responsible for bad results. However, the condition leading to the highest signals is not necessarily the best operational condition. The best operational condition is minimally sensitive to random pipetting fluctuations and yields reliable genotypes as well.