GWAX:Case-control association mapping without cases PREPRINT

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http://biorxiv.org/content/early/2016/03/25/045831

Abstract

The case-control association study is a powerful method for identifying genetic variants that influence disease risk. However, the collection of cases can be time-consuming and expensive; in some situations it is more practical to identify family members of cases. We show that replacing cases with their first-degree relatives enables genome-wide association studies by proxy (GWAX). In randomly-ascertained cohorts, this approach enables previously infeasible studies of diseases that are rare in the cohort, and can increase power to detect association by up to 30% for diseases that are more common in the cohort. As an illustration, we performed GWAX of 12 common diseases in 116,196 individuals from the UK Biobank. By combining these results with published GWAS summary statistics in a meta-analysis, we replicated established risk loci and identified 17 newly associated risk loci: four in Alzheimer's disease, eight in coronary artery disease, and five in type 2 diabetes. In addition to informing disease biology, our results demonstrate the utility of association mapping using family history of disease as a phenotype to be mapped. We anticipate that this approach will prove useful in future genetic studies of complex traits in large population cohorts.

PLINK gPLINK Haploview WGAS software tutorial S. Purcell

PLINK gPLINK Haploview Whole genome association software tutorial

Bioinformatics. 2012 May 3. [Epub ahead of print]

SEQCHIP: A Powerful Method to Integrate Sequence and Genotype Data for the Detection of Rare Variant Associations.

Liu DJ, Leal SM.

Source

1. Department of Biostatistics, Center of Statistical Genetics, University of Michigan, Ann Arbor, MI, 48109.

Abstract

MOTIVATION:

Next-generation sequencing greatly increases the capacity to detect rare-variant complex-trait associations. However, it is still expensive to sequence a large number of samples and therefore often small datasets are used. Given cost constraints, a potentially more powerful two-step strategy is to sequence a subset of the sample to discover variants, and genotype the identified variants in the remaining sample. If only cases are sequenced, directly combining sequence and genotype data will lead to inflated type-I errors in rare-variant association analysis. Although several methods have been developed to correct for the bias, they are either underpowered or theoretically invalid. We proposed a new method SEQCHIP to integrate genotype and sequence data, which can be used with most existing rare-variant tests.

RESULTS:

It is demonstrated using both simulated and real datasets that the SEQCHIP method has controlled type-I errors, and is substantially more powerful than all other currently available methods.

AVAILABILITY:

SEQCHIP is implemented in an R-Package and is available at http://linkage.rockefeller.edu/suzanne/seqchip/Seqchip.htmContacts: dajiang@umich.edu (D.J.L.), sleal@bcm.edu (S.M.L.)

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

PMID:

 

22556370

 

[PubMed - as supplied by publisher]

EMMAX — efficient mixed-model association expedited new software for GWAS

New Software Promises to Ramp up GWAS
"While genome-wide association studies have certainly proven their worth when it comes to pinpointing which genes play a role in human disease development, they are far from perfect. Sometimes, the genealogy of the individuals included in these large-scale studies can throw a wrench in the works because rarely are pairs of individuals in a study completely unrelated. This pairwise relatedness has occasionally led researchers to believe they have discovered a gene involved in a particular disease when in fact it is an artifact. While most researchers have statistical approaches for dealing with different levels of relatedness that come in the form of population structure or hidden relatedness, a team of scientists from the University of Michigan and the University of California, Los Angeles, has developed a statistical approach for dealing with both forms of relatedness. The method has the added benefit of dramatically speeding up the analysis process from years to just a few hours. "