Example calculation illustrating the methodology of a case-control<unk> . The allele count of each measured SNP is evaluated—in this case with a chi-squared test—to identify variants associated with the trait in question. The numbers in this example are taken from a 2007<unk>  of coronary artery disease (CAD) that showed that the individuals with the G-allele of SNP1 (rs1333049) were overrepresented amongst CAD-patients. Illustration of a simulated genotype by phenotype regression for a single SNP. Each dot represents an individual. A<unk>  of a continuous trait essentially consists of repeating this analysis at each SNP. The most common approach of<unk> es is the case-control setup, which compares two large groups of individuals, one healthy control group and one case group affected by a disease. All individuals in each group are genotyped for the majority of common known SNPs. The exact number of SNPs depends on the genotyping technology, but are typically one million or more. For each of these SNPs it is then investigated if the allele frequency is significantly altered between the case and the control group. In such setups, the fundamental unit for reporting effect sizes is the odds ratio. The odds ratio is the ratio of two odds, which in the context of<unk> es are the odds of case for individuals having a specific allele and the odds of case for individuals who do not have that same allele. Example: suppose that there are two alleles, T and C. The number of individuals in the case group having allele T is represented by 'A' and the number of individuals in the control group having allele T is represented by 'B'. Similarly, the number of individuals in the case group having allele C is represented by 'X' and the number of individuals in the control group having allele C is represented by 'Y'. In this case the odds ratio for allele T is A:B (meaning 'A to B', in standard odds terminology) divided by X:Y, which in mathematical notation is simply (A/B)/(X/Y). When the allele frequency in the case group is much higher than in the control group, the odds ratio is higher than 1, and vice versa for lower allele frequency. Additionally, a P-value for the significance of the odds ratio is typically calculated using a simple chi-squared test. Finding odds ratios that are significantly different from 1 is the objective of the<unk>  because this shows that a SNP is associated with disease. Because so many variants are tested, it is standard practice to require the p-value to be lower than to consider a variant significant. Variations on the case-control approach. A common alternative to case-control<unk> es is the analysis of quantitative phenotypic data, e.g. height or biomarker concentrations or even gene expression. Likewise, alternative statistics designed for dominance or recessive penetrance patterns can be used. Calculations are typically done using bioinformatics software such as SNPTEST and PLINK, which also include support for many of these alternative statistics.<unk>  focuses on the effect of individual SNPs. However, it is also possible that complex interactions among two or more SNPs, epistasis, might contribute to complex diseases. Due to the potentially exponential number of interactions, detecting statistically significant interactions in<unk>  data is both computationally and statistically challenging. This task has been tackled in existing publications that use algorithms inspired from data mining. Moreover, the researchers try to integrate GWA data with other biological data such as protein-protein interaction network to extract more informative results. A key step in the majority of<unk> es is the imputation of genotypes at SNPs not on the genotype chip used in the<unk> . This process greatly increases the number of SNPs that can be tested for<unk> , increases the power of the<unk> , and facilitates meta-analysis of<unk>  across distinct cohorts. Genotype imputation is carried out by statistical methods that combine the<unk>  data together with a reference panel of haplotypes. These methods take advantage of sharing of haplotypes between individuals over short stretches of sequence to impute alleles. Existing software packages for genotype imputation include IMPUTE2, Minimac, Beagle and MaCH. In addition to the calculation of<unk> , it is common to take into account any variables that could potentially confound the results. Sex and age are common examples of confounding variables. Moreover, it is also known that many genetic variations are associated with the geographical and historical populations in which the mutations first arose. Because of this<unk> ,<unk> es must take account of the geographic and ethnic background of participants by controlling for what is called population stratification. If they fail to do so, these<unk> es can produce false positive results. After odds ratios and P-values have been calculated for all SNPs, a common approach is to create a Manhattan plot. In the context of<unk> es, this plot shows the negative logarithm of the P-value as a function of<unk> c location. Thus the SNPs with the most significant<unk>  stand out on the plot, usually as stacks of points because of haploblock structure. Importantly, the P-value threshold for significance is corrected for multiple testing issues. The exact threshold varies by<unk> , but the conventional<unk> -wide significance threshold is to be significant in the face of hundreds of thousands to millions of tested SNPs.<unk> es typically perform the first analysis in a discovery cohort, followed by validation of the most significant SNPs in an independent validation cohort.
Genome-wide association study
Environment-wide association study
Risk panel
Genetic epidemiology
Molecular risk assessment
MIDAS Trial
Subgroup analysis
MOMS Trial