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Genome-wide scans of nucleotide variation in human subjects are providing an increasing number of replicated associations with complex disease traits. Most of the variants detected have small effects and, collectively, they account for a small fraction of the total genetic variance. Very large sample sizes are required to identify and validate findings. In this situation, even small sources of systematic or random error can cause spurious results or obscure real effects. The need for careful attention to data quality has been appreciated for some time in this field, and a number of strategies for quality control and quality assurance (QC/QA) have been developed. Here we extend these methods and describe a system of QC/QA for genotypic data in genome-wide association studies. This system includes some new approaches that (1) combine analysis of allelic probe intensities and called genotypes to distinguish gender misidentification from sex chromosome aberrations, (2) detect autosomal chromosome aberrations that may affect genotype calling accuracy, (3) infer DNA sample quality from relatedness and allelic intensities, (4) use duplicate concordance to infer SNP quality, (5) detect genotyping artifacts from dependence of Hardy-Weinberg equilibrium (HWE) test p-values on allelic frequency, and (6) demonstrate sensitivity of principal components analysis (PCA) to SNP selection. The methods are illustrated with examples from the ‘Gene Environment Association Studies’ (GENEVA) program. The results suggest several recommendations for QC/QA in the design and execution of genome-wide association studies.

Genome-wide association studies result in inflated false positive results when unrecognized cryptic relatedness exists. A number of methods have been proposed for testing association between markers and disease with a correction for known pedigree-based relationships. However, in most case-control studies, relationships are generally unknown, yet the design is predicated on the assumption of at least ancestral relatedness among cases. Here, we focus on adjusting cryptic relatedness when the genealogy of the sample is unknown, particularly in the context of samples from isolated populations where cryptic relatedness may be problematic. We estimate cryptic relatedness using maximum-likelihood methods and use a corrected chi-square test with estimated kinship coefficients for testing in the context of unknown cryptic relatedness. Estimated kinship coefficients characterize precisely the relatedness between truly related people, but are biased for unrelated pairs. The proposed test substantially reduces spurious positive results, producing a uniform null distribution of p-values. Especially with missing pedigree information, estimated kinship coefficients can still be used to correct non-independence among individuals. The corrected test was applied to real data sets from genetic isolates and created a distribution of p-value that was close to uniform. Thus the proposed test corrects the non-uniform distribution of p-values obtained with the uncorrected test and illustrates the advantage of the approach on real data.

A dense set of 5,000 SNPs on a 10-Mb region of human chromosome 20 has been typed on samples of African Americans, East Asians, and United Kingdom Caucasians. There are departures from Hardy-Weinberg equilibrium beyond the level at which markers are often discarded because of possible genotyping errors. The observation that markers showing such departures are often close together on the chromosome confirms the result that Hardy-Weinberg tests at two loci are correlated to an extent that depends on the linkage disequilibrium between those two markers. Linkage disequilibrium can be described by the composite linkage disequilibrium coefficient, the parameter that determines the behavior of case-control allelic tests of association. A useful preliminary investigation of datasets of this type is provided by counting the numbers of distinct multi-locus genotypes in windows of a few markers.

The pedigree disequilibrium test (PDT) has been proposed recently as a test for association in general pedigrees [Martin et al., Am J Hum Genet 67:146-54, 2000]. The Genetic Analysis Workshop (GAW) 12 simulated data, with many extended pedigrees, is an example the type of data to which the PDT is ideally suited. In replicate 42 from the general population the PDT correctly identifies candidate genes 1, 2, and 6 as containing single nucleotide polymorphisms (SNPs) that are significantly associated with the disease. We also applied the truncated product method (TPM) [Zaykin et al., Genet Epidemiol, in press] to combine p-values in overlapping windows across the genes. Our results show that the TPM is helpful in identifying significant SNPs as well as removing spurious false positives. Our results indicate that, using the PDT, functional disease-associated SNPs can be successfully identified with a dense map of moderately polymorphic SNPs.

We present a new procedure for combining P-values from a set of L hypothesis tests. Our procedure is to take the product of only those P-values less than some specified cut-off value and to evaluate the probability of such a product, or a smaller value, under the overall hypothesis that all L hypotheses are true. We give an explicit formulation for this P-value, and find by simulation that it can provide high power for detecting departures from the overall hypothesis. We extend the procedure to situations when tests are not independent. We present both real and simulated examples where the method is especially useful. These include exploratory analyses when L is large, such as genome-wide scans for marker-trait associations and meta-analytic applications that combine information from published studies, with potential for dealing with the "publication bias" phenomenon. Once the overall hypothesis is rejected, an adjustment procedure with strong family-wise error protection is available for smaller subsets of hypotheses, down to the individual tests.

A range of study designs, using unrelated or family controls, were used to investigate the pattern of association with disease of single nucleotide polymorphisms (SNPs) within candidate gene 1 (simulated data). Strong evidence of disease association at the functional locus was detected using all study designs, and in the "general" but not the "isolated" population the functional polymorphism displayed considerably higher association than surrounding SNPs. There was much variation in the strength of association of SNPs with disease, up to 70% of which was explained by SNP allele frequency and distance from the functional polymorphism. Some common polymorphisms very close to the functional locus however showed no association with disease. Analysis of short haplotypes of SNPs reduced but did not totally remove this feature.