Extensive Association of Common Disease Variants with Regulatory Sequence

Overlap between non-coding DNA regulatory sequences and common variant associations can help to identify specific cell and tissue types that are relevant for particular diseases. In a systematic manner, we analyzed variants from 94 genome-wide association studies (reporting at least 12 loci at p<5x10-8) by projecting them onto 466 epigenetic datasets (characterizing DNase I hypersensitive sites; DHSs) derived from various adult and fetal tissue samples and cell lines including many biological replicates. We were able to confirm many expected associations, such as the involvement of specific immune cell types in immune-related diseases and tissue types in diseases that affect specific organs, for example, inflammatory bowel disease and coronary artery disease. Other notable associations include adrenal glands in coronary artery disease, the immune system in Alzheimer's disease, and the kidney for bone marrow density. The association signals for some GWAS (for example, myopia or age at menarche) did not show a clear pattern with any of the cell or tissue types studied. In general, the identified variants from GWAS tend to be located outside coding regions. Altogether, we have performed an extensive characterization of GWAS signals in relation to cell and tissue-specific DHSs, demonstrating a key role for regulatory mechanisms in common diseases and complex traits

Inference of disease associations with unmeasured genetic variants by combining results from genome-wide association studies with linkage disequilibrium patterns in a reference data set

Results from whole-genome association studies of many common diseases are now available. Increasingly, these are being incorporated into meta-analyses to increase the power to detect weak associations with measured single-nucleotide polymorphisms (SNPs). Imputation of genotypes at unmeasured loci has been widely applied using patterns of linkage disequilibrium (LD) observed in the HapMap panels, but there is a need for alternative methods that can utilize the pooled effect estimates from meta-analyses and explore possible associations with SNPs and haplotypes that are not included in HapMap

Effective detection of human leukocyte antigen risk alleles in celiac disease using tag single nucleotide polymorphisms.

Background: The HLA genes, located in the MHC region on chromosome 6p21.3, play an important role in many autoimmune disorders, such as celiac disease (CD), type 1 diabetes (T1D), rheumatoid arthritis, multiple sclerosis, psoriasis and others. Known HLA variants that confer risk to CD, for example, include DQA1*05/DQB1*02 (DQ2.5) and DQA1*03/ DQB1*0302 (DQ8). To diagnose the majority of CD patients and to study disease susceptibility and progression, typing these strongly associated HLA risk factors is of utmost importance. However, current genotyping methods for HLA risk factors involve many reactions, and are complicated and expensive. We sought a simple experimental approach using tagging SNPs that predict the CD-associated HLA risk factors. Methodology: Our tagging approach exploits linkage disequilibrium between single nucleotide polymorphism (SNPs) and the CD-associated HLA risk factors DQ2.5 and DQ8 that indicate direct risk, and DQA1*0201/DQB1*0202 (DQ2.2) and DQA1*0505/DQB1*0301 (DQ7) that attribute to the risk of DQ2.5 to CD. To evaluate the predictive power of this approach, we performed an empirical comparison of the predicted DQ types, based on these six tag SNPs, with those executed with current validated laboratory typing methods of the HLA-DQA1 and -DQB1 genes in three large cohorts. The results were validated in three European celiac populations. Conclusion: Using this method, only six SNPs were needed to predict the risk types carried by .95% of CD patients. We determined that for this tagging approach the sensitivity was .0.991, specificity .0.996 and the predictive value .0.948. Our results show that this tag SNP method is very accurate an

SNP selection for genes of iron metabolism in a study of genetic modifiers of hemochromatosis

<p>Abstract</p> <p>Background</p> <p>We report our experience of selecting tag SNPs in 35 genes involved in iron metabolism in a cohort study seeking to discover genetic modifiers of hereditary hemochromatosis.</p> <p>Methods</p> <p>We combined our own and publicly available resequencing data with HapMap to maximise our coverage to select 384 SNPs in candidate genes suitable for typing on the Illumina platform.</p> <p>Results</p> <p>Validation/design scores above 0.6 were not strongly correlated with SNP performance as estimated by Gentrain score. We contrasted results from two tag SNP selection algorithms, LDselect and Tagger. Varying r²from 0.5 to 1.0 produced a near linear correlation with the number of tag SNPs required. We examined the pattern of linkage disequilibrium of three levels of resequencing coverage for the transferrin gene and found HapMap phase 1 tag SNPs capture 45% of the ≥ 3% MAF SNPs found in SeattleSNPs where there is nearly complete resequencing. Resequencing can reveal adjacent SNPs (within 60 bp) which may affect assay performance. We report the number of SNPs present within the region of six of our larger candidate genes, for different versions of stock genotyping assays.</p> <p>Conclusion</p> <p>A candidate gene approach should seek to maximise coverage, and this can be improved by adding to HapMap data any available sequencing data. Tag SNP software must be fast and flexible to data changes, since tag SNP selection involves iteration as investigators seek to satisfy the competing demands of coverage within and between populations, and typability on the technology platform chosen.</p

Effects of normalization on quantitative traits in association test

<p>Abstract</p> <p>Background</p> <p>Quantitative trait loci analysis assumes that the trait is normally distributed. In reality, this is often not observed and one strategy is to transform the trait. However, it is not clear how much normality is required and which transformation works best in association studies.</p> <p>Results</p> <p>We performed simulations on four types of common quantitative traits to evaluate the effects of normalization using the logarithm, Box-Cox, and rank-based transformations. The impact of sample size and genetic effects on normalization is also investigated. Our results show that rank-based transformation gives generally the best and consistent performance in identifying the causal polymorphism and ranking it highly in association tests, with a slight increase in false positive rate.</p> <p>Conclusion</p> <p>For small sample size or genetic effects, the improvement in sensitivity for rank transformation outweighs the slight increase in false positive rate. However, for large sample size and genetic effects, normalization may not be necessary since the increase in sensitivity is relatively modest.</p

Designing Genome-Wide Association Studies: Sample Size, Power, Imputation, and the Choice of Genotyping Chip

Genome-wide association studies are revolutionizing the search for the genes underlying human complex diseases. The main decisions to be made at the design stage of these studies are the choice of the commercial genotyping chip to be used and the numbers of case and control samples to be genotyped. The most common method of comparing different chips is using a measure of coverage, but this fails to properly account for the effects of sample size, the genetic model of the disease, and linkage disequilibrium between SNPs. In this paper, we argue that the statistical power to detect a causative variant should be the major criterion in study design. Because of the complicated pattern of linkage disequilibrium (LD) in the human genome, power cannot be calculated analytically and must instead be assessed by simulation. We describe in detail a method of simulating case-control samples at a set of linked SNPs that replicates the patterns of LD in human populations, and we used it to assess power for a comprehensive set of available genotyping chips. Our results allow us to compare the performance of the chips to detect variants with different effect sizes and allele frequencies, look at how power changes with sample size in different populations or when using multi-marker tags and genotype imputation approaches, and how performance compares to a hypothetical chip that contains every SNP in HapMap. A main conclusion of this study is that marked differences in genome coverage may not translate into appreciable differences in power and that, when taking budgetary considerations into account, the most powerful design may not always correspond to the chip with the highest coverage. We also show that genotype imputation can be used to boost the power of many chips up to the level obtained from a hypothetical “complete” chip containing all the SNPs in HapMap. Our results have been encapsulated into an R software package that allows users to design future association studies and our methods provide a framework with which new chip sets can be evaluated

Power analysis for genome-wide association studies

Abstract Background Genome-wide association studies are a promising new tool for deciphering the genetics of complex diseases. To choose the proper sample size and genotyping platform for such studies, power calculations that take into account genetic model, tag SNP selection, and the population of interest are required. Results The power of genome-wide association studies can be computed using a set of tag SNPs and a large number of genotyped SNPs in a representative population, such as available through the HapMap project. As expected, power increases with increasing sample size and effect size. Power also depends on the tag SNPs selected. In some cases, more power is obtained by genotyping more individuals at fewer SNPs than fewer individuals at more SNPs. Conclusion Genome-wide association studies should be designed thoughtfully, with the choice of genotyping platform and sample size being determined from careful power calculations.</p

Multiethnic Genetic Association Studies Improve Power for Locus Discovery

To date, genome-wide association studies have focused almost exclusively on populations of European ancestry. These studies continue with the advent of next-generation sequencing, designed to systematically catalog and test low-frequency variation for a role in disease. A complementary approach would be to focus further efforts on cohorts of multiple ethnicities. This leverages the idea that population genetic drift may have elevated some variants to higher allele frequency in different populations, boosting statistical power to detect an association. Based on empirical allele frequency distributions from eleven populations represented in HapMap Phase 3 and the 1000 Genomes Project, we simulate a range of genetic models to quantify the power of association studies in multiple ethnicities relative to studies that exclusively focus on samples of European ancestry. In each of these simulations, a first phase of GWAS in exclusively European samples is followed by a second GWAS phase in any of the other populations (including a multiethnic design). We find that nontrivial power gains can be achieved by conducting future whole-genome studies in worldwide populations, where, in particular, African populations contribute the largest relative power gains for low-frequency alleles (<5%) of moderate effect that suffer from low power in samples of European descent. Our results emphasize the importance of broadening genetic studies to worldwide populations to ensure efficient discovery of genetic loci contributing to phenotypic trait variability, especially for those traits for which large numbers of samples of European ancestry have already been collected and tested

Calibrating the Performance of SNP Arrays for Whole-Genome Association Studies

To facilitate whole-genome association studies (WGAS), several high-density SNP genotyping arrays have been developed. Genetic coverage and statistical power are the primary benchmark metrics in evaluating the performance of SNP arrays. Ideally, such evaluations would be done on a SNP set and a cohort of individuals that are both independently sampled from the original SNPs and individuals used in developing the arrays. Without utilization of an independent test set, previous estimates of genetic coverage and statistical power may be subject to an overfitting bias. Additionally, the SNP arrays' statistical power in WGAS has not been systematically assessed on real traits. One robust setting for doing so is to evaluate statistical power on thousands of traits measured from a single set of individuals. In this study, 359 newly sampled Americans of European descent were genotyped using both Affymetrix 500K (Affx500K) and Illumina 650Y (Ilmn650K) SNP arrays. From these data, we were able to obtain estimates of genetic coverage, which are robust to overfitting, by constructing an independent test set from among these genotypes and individuals. Furthermore, we collected liver tissue RNA from the participants and profiled these samples on a comprehensive gene expression microarray. The RNA levels were used as a large-scale set of quantitative traits to calibrate the relative statistical power of the commercial arrays. Our genetic coverage estimates are lower than previous reports, providing evidence that previous estimates may be inflated due to overfitting. The Ilmn650K platform showed reasonable power (50% or greater) to detect SNPs associated with quantitative traits when the signal-to-noise ratio (SNR) is greater than or equal to 0.5 and the causal SNP's minor allele frequency (MAF) is greater than or equal to 20% (N = 359). In testing each of the more than 40,000 gene expression traits for association to each of the SNPs on the Ilmn650K and Affx500K arrays, we found that the Ilmn650K yielded 15% times more discoveries than the Affx500K at the same false discovery rate (FDR) level