Such studies are particularly useful in finding genetic variations that contribute to common, complex diseases, such as asthma, cancer, diabetes, heart disease and mental illnesses. With the completion of the Human Genome Project in and the International HapMap Project in , researchers now have a set of research tools that make it possible to find the genetic contributions to common diseases.
The tools include computerized databases that contain the reference human genome sequence, a map of human genetic variation and a set of new technologies that can quickly and accurately analyze whole-genome samples for genetic variations that contribute to the onset of a disease.
The impact on medical care from genome-wide association studies could potentially be substantial. Such research is laying the groundwork for the era of personalized medicine, in which the current one size-fits-all approach to medical care will give way to more customized strategies.
In the future, after improvements are made in the cost and efficiency of genome-wide scans and other innovative technologies, health professionals will be able to use such tools to provide patients with individualized information about their risks of developing certain diseases. The information will enable health professionals to tailor prevention programs to each person's unique genetic makeup.
In addition, if a patient does become ill, the information can be used to select the treatments most likely to be effective and least likely to cause adverse reactions in that particular patient. Researchers already have reported considerable success using this new strategy. Start making these connections in your research and share your stories using the hashtag V2Fnow. Read Interview. Listen to Podcast.
The Infinium Global Screening Array v3. A cost-effective array for understanding complex disease in diverse human populations, focused on Hispanic and African American populations. Genome-wide association studies have identified thousands of variants with putative roles in different diseases.
However, going from statistical associations to true insight into disease mechanisms remains a challenge. Recent advances in sequencing technologies have facilitated the development of strategies for assaying GWAS SNPs for potential functional relevance. View Webinar. Genome-Wide Association Studies.
Multiple testing adjustments to control false positive probability are conducted in these tests for every SNP. The snpStats can handle both quantitative and qualitative phenotypes.
It can carry out single SNP tests adjusted for potential perplexing by quantitative and qualitative covariates. Tests having several SNPs taken together as 'tags' are also supported in these analyses. The snpStats package offers options for quality control using Hardy Weinberg equilibrium tests and filtering SNPs using minor allele frequencies. Similar to Plink, snpStats also offer popular multiple testing adjustments options.
Plink and snpStats are freely downloaded and the detailed instructions of the various functions in the programs can be found in the respective user manuals. Improving the statistical power using large sample size is not suitable for all genomic studies.
This is because of majority of studies have enough samples to generate huge data as required. Therefore, it is desired to improve the study power using novel statistical analysis methods.
The development of novel methods is gaining momentum over the last decades. Most novel statistical models are developed by properly incorporating the LD relationship among SNPs to allow the tests use information from each other. Hence, it is not realistic to obtain a precise estimation of LD matrix using moderate amount of samples in a genomic study. Hence, all reliable models incorporate LD information without clarity clearly.
These do not use estimated LD matrix as model parameters as described below. The genomic information is the input data and the disease is the outcome in the association study using a supervised machine-learning model.
There are various complex statistical models developed to improve the statistical power for SNP detection. Model-based clustering is an unsupervised machine learning method, which can be used to group SNPs.
The SNPs in the same group have similar relationship to the outcome, and could borrow information from each other in the GWAS analysis. A recent method proposed a one-step model. The patterns of clusters are specified by the difference in minor allele frequencies of SNPs between cases and controls. Thus, the pattern is enforced with a special prior distribution. This model-based clustering have shown more precise controls of FDR and higher statistical power in both simulation studies and real data analysis [ 23 ].
The limitation is that it can only handle case-control association studies. The other approach is based on data splitting strategy. The data can be randomly split into a screening set and a testing set. We use the screening set to remove the majority of SNPs with weak signals; and then investigate the retained SNPs in the testing set.
The test sets only consider a very small subset of SNPs. This leads to fewer penalties in the multiple test adjustment on testing set. So, this approach is much more powerful than analyzing the original data with all SNPs. The results of this type of analysis can be heavily affected by which samples are split into the testing set. We use resampling approaches to analyze multiple copies of the data with different random splits to remove unwanted 'split' effect [ 24 , 25 ].
These methods are not popular since they have multiple critical disadvantages. First, multiple testing adjustment method is not available for controlling false positives in these methods. GWAS identify common variants which tag a region of linkage disequilibrium LD containing causal variant s.
Additional or follow-on studies are usually required to narrow the region of association and identify the causal variant. Find out more about the theory and background of genetic variation here. A p-value indicates the significance of the difference in frequency of the allele tested between cases and controls i.
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