Sample reproducibility of genetic association using different multimarker TDTs in genome-wide association studies: characterization and a new approach

Detecting multi-way epistasis in family-based association studies

The era of genome-wide association studies (GWAS) has led to the discovery of numerous genetic variants associated with disease. Better understanding of whether these or other variants interact leading to differential risk compared with individual marker effects will increase our understanding of the genetic architecture of disease, which may be investigated using the family-based study design. We present M-TDT (the multi-locus transmission disequilibrium test), a tool for detecting family-based multi-locus multi-allelic effects for qualitative or quantitative traits, extended from the original transmission disequilibrium test (TDT). Tests to handle the comparison between additive and epistatic models, lack of independence between markers and multiple offspring are described. Performance of M-TDT is compared with a multifactor dimensionality reduction (MDR) approach designed for investigating families in the hypothesis-free genome-wide setting (the multifactor dimensionality reduction pedigree disequilibrium test, MDR-PDT). Other methods derived from the TDT or MDR to investigate genetic interaction in the family-based design are also discussed. The case of three independent biallelic loci is illustrated using simulations for one- to three-locus alternative hypotheses. M-TDT identified joint-locus effects and distinguished effectively between additive and epistatic models. We showed a practical example of M-TDT based on three genes already known to be implicated in malaria susceptibility. Our findings demonstrate the value of M-TDT in a hypothesis-driven context to test for multi-way epistasis underlying common disease etiology, whereas MDR-PDT-based methods are more appropriate in a hypothesis-free genome-wide setting.

A comparison of genomic profiles of complex diseases under different models

Background

Various approaches are being used to predict individual risk to polygenic diseases from data provided by genome-wide association studies. As there are substantial differences between the diseases investigated, the data sets used and the way they are tested, it is difficult to assess which models are more suitable for this task.

Results

We compared different approaches for seven complex diseases provided by the Wellcome Trust Case Control Consortium (WTCCC) under a within-study validation approach. Risk models were inferred using a variety of learning machines and assumptions about the underlying genetic model, including a haplotype-based approach with different haplotype lengths and different thresholds in association levels to choose loci as part of the predictive model. In accordance with previous work, our results generally showed low accuracy considering disease heritability and population prevalence. However, the boosting algorithm returned a predictive area under the ROC curve (AUC) of 0.8805 for Type 1 diabetes (T1D) and 0.8087 for rheumatoid arthritis, both clearly over the AUC obtained by other approaches and over 0.75, which is the minimum required for a disease to be successfully tested on a sample at risk, which means that boosting is a promising approach. Its good performance seems to be related to its robustness to redundant data, as in the case of genome-wide data sets due to linkage disequilibrium.

Conclusions

In view of our results, the boosting approach may be suitable for modeling individual predisposition to Type 1 diabetes and rheumatoid arthritis based on genome-wide data and should be considered for more in-depth research.

Electronic supplementary material

The online version of this article (doi:10.1186/s12920-015-0157-2) contains supplementary material, which is available to authorized users.

Current strategies for mutation detection in phenotype-driven screens utilising next generation sequencing

Mutagenesis-based screens in mice are a powerful discovery platform to identify novel genes or gene functions associated with disease phenotypes. An N-ethyl-N-nitrosourea (ENU) mutagenesis screen induces single nucleotide variants randomly in the mouse genome. Subsequent phenotyping of mutant and wildtype mice enables the identification of mutated pathways resulting in phenotypes associated with a particular ENU lesion. This unbiased approach to gene discovery conducts the phenotyping with no prior knowledge of the functional mutations. Before the advent of affordable next generation sequencing (NGS), ENU variant identification was a limiting step in gene characterization, akin to ‘finding a needle in a haystack’. The emergence of a reliable reference genome alongside advances in NGS has propelled ENU mutation discovery from an arduous, time-consuming exercise to an effective and rapid form of mutation discovery. This has permitted large mouse facilities worldwide to use ENU for novel mutation discovery in a high-throughput manner, helping to accelerate basic science at the mechanistic level. Here, we describe three different strategies used to identify ENU variants from NGS data and some of the subsequent steps for mutation characterisation.