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See related article, pages 825–832. With a swiftly moving, highly technical field, it helps to have a reliable guide. In this issue of Stroke, Lanktree et al1 provide a timely overview of genomic analysis applied to stroke.1 Readers find a clear and concise synopsis of single-nucleotide polymorphisms, copy number variations, listings of the strengths and limitations of genome-wide association studies (GWAS), and a distillation of the findings from GWAS performed in 6 cohorts (5 of which were ischemic stroke cohorts). The review also covers a topic not usually found in clinical reviews, namely techniques to visually display quantitative information. Excellence in statistical graphics should, among other things, avoid distorting what the data have to say, present many numbers in a small space, and make large data sets coherent.2 The Manhattan and quantile–quantile plots are excellent examples of statistical graphics that have become invaluable for interpreting GWAS results. Along with visualization comes interpretation of data in the context of GWAS. Clinical investigators are well aware of the problem of multiple testing from such settings as interim and subgroup analyses in clinical trials, which can lead to wildly spurious conclusions, such as concluding that aspirin only helped individuals of certain astrological signs in the second International Study of Infarct Survival.3 GWAS simply escalates the problem of multiple testing by orders of magnitude. Because single-nucleotide polymorphism-based GWAS test hundreds of thousands …

Background Associations of several Single Nucleotide Polymorphisms (SNPs) with common diseases like Coronary Artery Disease (CAD), Crohn’s Disease (CD), Hypertension (HT), Bipolar Disorder (BD), Type 1 Diabetes (T1D), Type 2 Diabetes (T2D) and Rheumatoid Arthritis (RA) were identified in a study conducted by the Wellcome Trust Case Control Consortium (WTCCC) (1). WTCCC study compared the effects of genetic variations in 14,000 cases and 3000 shared controls and identified 24 independent associations with the diseases mentioned above using the genotype information of approximately 500,000 directly genotyped SNPs and genotype information simulated at 2.8 million loci studied by the International Hapmap Project(2). We hypothesize that there are more chances of finding association of rare SNPs with diseases by refined analysis of non-synonymous SNPs (nsSNPs) in genome wide association studies. In the present study we analyzed the association of 12,660 nsSNPs using a case control study in the WTCCC population.

Repeated exposure to HIV does not necessarily result in infection and HIV infection does not inevitably lead to the development of the AIDS. Multiple immunological and genetic features can confer resistance to HIV acquisition and progression at different steps in viral infection; a full understanding of these mechanisms could result in the development of novel therapeutic and vaccine approaches for HIV infection. In this review, we focus on the genetic mechanisms associated with resistance to HIV infection and to the progression to AIDS.

Evolutionary Anthropology: Issues, News, and ReviewsVolume 18, Issue 4 p. 125-130 Crotchets & QuidditiesFree Access Pieces of eight! Like centuries of pursuit of the Spanish treasure fleet, gene hunters seek elusive treasures in the human genome. But are they there? Kenneth M. Weiss, Kenneth M. Weiss [email protected] Ken Weiss is Evan Pugh Professor of Anthropology and Genetics at Penn State UniversitySearch for more papers by this author Kenneth M. Weiss, Kenneth M. Weiss [email protected] Ken Weiss is Evan Pugh Professor of Anthropology and Genetics at Penn State UniversitySearch for more papers by this author First published: 25 August 2009 https://doi.org/10.1002/evan.20218Citations: 2 AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL References 1 Shurz WL. 1939. The Manila galleon. Manila: RP Garcia Publishing. 2 Walton TR. 1994. The Spanish treasure fleets. Sarasota, FL: Pineapple Press. 3 G Norris, editor. 1994. Buccaneer explorer: William Dampier's voyages. Woodbridge, UK: Boydell Press. 4 Altshuler D,Daly MJ,Lander ES. 2008. Genetic mapping in human disease. Science 322: 881– 888. 5 Weiss KM. 2008. Tilting at quixotic trait loci (QTL): an evolutionary perspective on genetic causation. Genetics 179: 1741– 1756. 6 Manolio TA,Brooks LD,Collins FS. 2008. A HapMap harvest of insights into the genetics of common disease. J Clin Invest 118: 1590– 1605. 7 Bodmer W,Bonilla C. 2008. Common and rare variants in multifactorial susceptibility to common diseases. Nat Genet 40: 695– 701. 8 The Wellcome Trust Case Control Consortium. 2007. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447: 661– 678. 9 Maher B. 2008. Personal genomes: the case of the missing heritability. Nature 456: 18– 21. 10 Weedon MN,Frayling TM. 2008. Reaching new heights: insights into the genetics of human stature. Trends Genet 24: 595– 603. 11 Visscher PM. 2008. Sizing up human height variation. Nat Genet 40: 489– 490. 12 Weedon MN,Lango H,Lindgren CM,Wallace C,Evans DM,Mangino M,Freathy RM,Perry JR,Stevens S,Hall AS, et al. 2008. Genome-wide association analysis identifies 20 loci that influence adult height. Nat Genet 40: 575– 583. 13 Blakeslee AF. 1914. Corn and men. J. Hered 5: 511– 518. 14 Ruhli F,Henneberg M,Woitek U. 2008. Variability of height, weight, and body mass index in a Swiss armed forces 2005 census. Am J Phys Anthropol 137: 457– 468. 15 Weiss KM,Buchanan AV. 2009. The mermaid's tale: four billion years of cooperation in the making of living things. Cambridge: Harvard University Press. 16 Buchanan AV,Sholtis S,Richtsmeier J,Weiss KM. 2009. What are genes “for” or where are traits “from”? What is the question? Bioessays 31: 198– 208. 17 Weiss KM. n.d. Having a jolly good time—together or, evolution by cooperative interaction. Curr Anthropol. In press. Citing Literature Volume18, Issue4July/August 2009Pages 125-130 ReferencesRelatedInformation

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Abdominal aortic aneurysm (AAA) is a common cardiovascular disease among older people and demonstrates significant heritability. In contrast to similar complex diseases, relatively few genetic associations with AAA have been confirmed. We reanalyzed our genome-wide study and carried through to replication suggestive discovery associations at a lower level of significance. A genome-wide association study was conducted using 1830 cases from the United Kingdom, New Zealand, and Australia with infrarenal aorta diameter≥30 mm or ruptured AAA and 5435 unscreened controls from the 1958 Birth Cohort and National Blood Service cohort from the Wellcome Trust Case Control Consortium. Eight suggestive associations with P<1×10(-4) were carried through to in silico replication in 1292 AAA cases and 30,503 controls. One single-nucleotide polymorphism associated with P<0.05 after Bonferroni correction in the in silico study underwent further replication (706 AAA cases and 1063 controls from the United Kingdom, 507 AAA cases and 199 controls from Denmark, and 885 AAA cases and 1000 controls from New Zealand). Low-density lipoprotein receptor (LDLR) rs6511720 A was significantly associated overall and in 3 of 5 individual replication studies. The full study showed an association that reached genome-wide significance (odds ratio, 0.76; 95% confidence interval, 0.70-0.83; P=2.08×10(-10)). LDLR rs6511720 is associated with AAA. This finding is consistent with established effects of this variant on coronary artery disease. Shared causal pathways with other cardiovascular diseases may present novel opportunities for preventative and therapeutic strategies for AAA.

Rheumatoid arthritis (RA) is an archetypal, common, complex autoimmune disease with both genetic and environmental contributions to disease aetiology. Two novel RA susceptibility loci have been reported from recent genome-wide and candidate gene association studies. We, therefore, investigated the evidence for association of the STAT4 and TRAF1/C5 loci with RA using imputed data from the Wellcome Trust Case Control Consortium (WTCCC). No evidence for association of variants mapping to the TRAF1/C5 gene was detected in the 1860 RA cases and 2930 control samples tested in that study. Variants mapping to the STAT4 gene did show evidence for association (rs7574865, P = 0.04). Given the association of the TRAF1/C5 locus in two previous large case–control series from populations of European descent and the evidence for association of the STAT4 locus in the WTCCC study, single nucleotide polymorphisms mapping to these loci were tested for association with RA in an independent UK series comprising DNA from >3000 cases with disease and >3000 controls and a combined analysis including the WTCCC data was undertaken. We confirm association of the STAT4 and the TRAF1/C5 loci with RA bringing to 5 the number of confirmed susceptibility loci. The effect sizes are less than those reported previously but are likely to be a more accurate reflection of the true effect size given the larger size of the cohort investigated in the current study.

Large genome-wide association studies (GWAS) have been performed to detect common genetic variants involved in common diseases, but most of the variants found this way account for only a small portion of the trait variance. Furthermore, candidate gene-based resequencing suggests that many rare genetic variants contribute to the trait variance of common diseases. Here we propose two designs, sibpair and unrelated-case designs, to detect rare genetic variants in either a candidate gene-based or genome-wide association analysis. First we show that we can detect and classify together rare risk haplotypes using a relatively small sample with either of these designs, and then have increased power to test association in a larger case-control sample. This method can also be applied to resequencing data. Next we apply the method to the Wellcome Trust Case Control Consortium (WTCCC) coronary artery disease (CAD) and hypertension (HT) data, the latter being the only trait for which no genome-wide association evidence was reported in the original WTCCC study, and identify one interesting gene associated with HT and four associated with CAD at a genome-wide significance level of 5%. These results suggest that searching for rare genetic variants is feasible and can be fruitful in current GWAS, candidate gene studies or resequencing studies.

Genome-wide association studies (GWAS) have identified single-nucleotide polymorphisms (SNPs) at multiple loci that are significantly associated with coronary artery disease (CAD) risk. In this study, we sought to determine and compare the predictive capabilities of 9p21.3 alone and a panel of SNPs identified and replicated through GWAS for CAD. We used the Ottawa Heart Genomics Study (OHGS) (3323 cases, 2319 control subjects) and the Wellcome Trust Case Control Consortium (WTCCC) (1926 cases, 2938 control subjects) data sets. We compared the ability of allele counting, logistic regression, and support vector machines. Two sets of SNPs, 9p21.3 alone and a set of 12 SNPs identified by GWAS and through a model-fitting procedure, were considered. Performance was assessed by measuring area under the curve (AUC) for OHGS using 10-fold cross-validation and WTCCC as a replication set. AUC for logistic regression using OHGS increased significantly from 0.555 to 0.608 (P=3.59×10⁻¹⁴) for 9p21.3 versus the 12 SNPs, respectively. This difference remained when traditional risk factors were considered in a subgroup of OHGS (1388 cases, 2038 control subjects), with AUC increasing from 0.804 to 0.809 (P=0.037). The added predictive value over and above the traditional risk factors was not significant for 9p21.3 (AUC 0.801 versus 0.804, P=0.097) but was for the 12 SNPs (AUC 0.801 versus 0.809, P=0.0073). Performance was similar between OHGS and WTCCC. Logistic regression outperformed both support vector machines and allele counting. Using the collective of 12 SNPs confers significantly greater predictive capabilities for CAD than 9p21.3, whether traditional risks are or are not considered. More accurate models probably will evolve as additional CAD-associated SNPs are identified.

The Affymetrix GeneChip Human Mapping 500K array is common for genome-wide association studies (GWASs). Recent findings highlight the importance of accurate genotype calling algorithms to reduce the inflation in Type I and Type II error rates. Differential results due to genotype calling errors can introduce severe bias in case-control association study results. Using data from the Wellcome Trust Case Control Consortium, 1991 individuals with coronary artery disease (CAD) and 1500 controls from the UK Blood Services (NBS) were genotyped on the Affymetrix 500K array. Different batch sizes and compositions were used in the Bayesian Robust Linear Model with Mahalanobis distance classifier (BRLMM) genotype calling algorithm to assess the batch effect on downstream association analysis. Results show that composition (cases and controls genotyped simultaneously or separate) and size (number of individuals processed by BRLMM at a time) can create 2-3% discordance in the results for quality control and statistical analysis and may contribute to the lack of reproducibility between GWASs. The changes in batch size are largely responsible for differential single-nucleotide polymorphism results, yet we observe evidence of an interactive effect of batch size and composition that contributes to discordant results in the list of significantly associated loci.

With the development of high-throughput sequencing and genotyping technologies, the number of markers collected in genetic association studies is growing rapidly, increasing the importance of methods for correcting for multiple hypothesis testing. The permutation test is widely considered the gold standard for accurate multiple testing correction, but it is often computationally impractical for these large datasets. Recently, several studies proposed efficient alternative approaches to the permutation test based on the multivariate normal distribution (MVN). However, they cannot accurately correct for multiple testing in genome-wide association studies for two reasons. First, these methods require partitioning of the genome into many disjoint blocks and ignore all correlations between markers from different blocks. Second, the true null distribution of the test statistic often fails to follow the asymptotic distribution at the tails of the distribution. We propose an accurate and efficient method for multiple testing correction in genome-wide association studies—SLIDE. Our method accounts for all correlation within a sliding window and corrects for the departure of the true null distribution of the statistic from the asymptotic distribution. In simulations using the Wellcome Trust Case Control Consortium data, the error rate of SLIDE's corrected p-values is more than 20 times smaller than the error rate of the previous MVN-based methods' corrected p-values, while SLIDE is orders of magnitude faster than the permutation test and other competing methods. We also extend the MVN framework to the problem of estimating the statistical power of an association study with correlated markers and propose an efficient and accurate power estimation method SLIP. SLIP and SLIDE are available at http://slide.cs.ucla.edu.

Genome-wide association studies, exemplified by the Wellcome Trust Case Control Consortium and follow-up studies, have identified dozens of common variants robustly associated with common diseases, providing new clues about genetic architecture in humans. Finding all such loci, and fully defining genotype-phenotype correlation, will be a key to translating initial clues into pathophysiological understanding and clinical prediction.

Genome-wide association studies (GWA) try to identify the genetic polymorphisms associated with variation in phenotypes. However, the most significant genetic variants may have a small predictive power to forecast the future development of common diseases. We study the prediction of the risk of developing a disease given genome-wide genotypic data using classifiers with a reject option, which only make a prediction when they are sufficiently certain, but in doubtful situations may reject making a classification. To test the reliability of our proposal, we used the Wellcome Trust Case Control Consortium (WTCCC) data set, comprising 14,000 cases of seven common human diseases and 3,000 shared controls.

(Cell 175, 1679–1687.e1–e7; November 29, 2018) We the editors of Cell have been informed by the Lead Contact and corresponding author of this paper, Dr. Chris Cotsapas, that further analyses following the publication of the paper have indicated that two variants (rs61999302 encoding PRKRA.D33G and rs62176112 encoding PRKRA.P11L) reported in this paper to be associated with MS risk were in fact spurious associations due to the presence of a dispersed duplication event, in which this region of chromosome 2 is duplicated in the MHC region of chromosome 6. The potential for this dispersed duplication to lead to spurious associations has been previously documented (Wellcome Trust Case Control Consortium et al., 2010Craddock N. Hurles M.E. Cardin N. Pearson R.D. Plagnol V. Robson S. Vukcevic D. Barnes C. Conrad D.F. Giannoulatou E. et al.Wellcome Trust Case Control ConsortiumGenome-wide association study of CNVs in 16,000 cases of eight common diseases and 3,000 shared controls.Nature. 2010; 464: 713-720Google Scholar). Upon learning of the potential problems associated with signals in this region, the authors used the data reported in this paper to impute classical HLA alleles and amino acid variants using SNP2HLA (Jia et al., 2013Jia X. Han B. Onengut-Gumuscu S. Chen W.M. Concannon P.J. Rich S.S. Raychaudhuri S. de Bakker P.I.W. Imputing amino acid polymorphisms in human leukocyte antigens.PLoS ONE. 2013; 8: e64683Google Scholar) and found that the two reported variants in PRKRA showed extensive linkage disequilibrium with genotyped and imputed HLA variants, including known MS risk alleles. Conditioning on any of these known variants was sufficient to explain the reported association, leading to the conclusion that these two signals are driven by the duplication and are not true associations of the reported variants with MS risk. With this note, we and the authors would like to alert readers to this issue. The remaining findings in the paper are unchanged, as are the main conclusions of the paper. The authors would also like to thank Dr. Matthew Hurles (Wellcome Sanger Institute) for bringing this issue to their attention. Furthermore, in the printed version of the experimental model and subject details section of the STAR methods, Köln is listed as one of the patient recruitment centers, but Düsseldorf should have been listed instead. One of the authors, Dr. Clemens Warnke, although currently based in Köln, in fact contributed patient samples collected in Düsseldorf to the study. This has been corrected in the article online, and the authors apologize for any confusion these issues may have caused. Low-Frequency and Rare-Coding Variation Contributes to Multiple Sclerosis RiskMitrovič et al.CellOctober 18, 2018In BriefIn a large multi-cohort study, unexplained heritability for multiple sclerosis is detected in low-frequency coding variants that are missed by GWAS analyses, further underscoring the role of immune genes in MS pathology. Full-Text PDF Open AccessLow-Frequency and Rare-Coding Variation Contributes to Multiple Sclerosis RiskMitrovič et al.CellJanuary 23, 2020In Brief(Cell 175, 1679–1687.e1–e7; November 29, 2018) Full-Text PDF Open Access

Genome-wide association studies have identified at least 71 Crohn's disease (CD) genetic risk loci, but the role of gene-gene interactions is unclear. The value of genetic variants in clinical practice is not defined because of limited explained heritability. We examined model predictability of combining the 71 CD risk alleles and genetic interactions in an ongoing inflammatory bowel disease genome-wide association study. The Wellcome Trust Case Control Consortium inflammatory bowel disease genome-wide association study was used as a replicate cohort. We used logic regression, an adaptive regression methodology, to search for high-order binary predictors (e.g., single-nucleotide polymorphism [SNP] interactions). The combined 71 CD SNPs had good CD risk predictability (area under the curve of 0.75 and 0.73 in the 2 cohorts). Higher cumulative allele score predicted higher CD risk, but a relatively small difference in cumulative allele scores was observed between CD and controls (49 versus 47, P < 0.001). Through LR, we identified high-order genetic interactions and significantly improved the model predictability (area under the curve, from 0.75 to 0.77, P < 0.0001). A genetic interaction model, including NOD2, ATG16L1, IL10/IL19, C13orf31, and chr21q loci, was discovered and successfully replicated in the independent Wellcome Trust Case Control Consortium cohort. The explained heritability of the 71 CD SNPs alone was 24% and increased to 27% after adding the genetic interactions. A novel approach allowed the identification and replication of genetic interactions among NOD2, ATG16L1, IL10/IL19, C13orf31, and chr21q loci. CD risk can be predicted by a model of 71 CD loci and improved by adding genetic interactions.