Linkage Disequilibrium in Genetic Association Studies Improves the Performance of Grammatical Evolution Neural Networks

Abstract

One of the most important goals in genetic epidemiology is the identification of genetic factors/ features that predict complex diseases. The ubiquitous nature of gene-gene interactions in the underlying etiology of common diseases creates an important analytical challenge, spurring the introduction of novel, computational approaches. One such method is a grammatical evolution neural network (GENN) approach. GENN has been shown to have high power to detect such interactions in simulation studies, but previous studies have ignored an important feature of most genetic data: linkage disequilibrium (LD). LD describes the non-random association of alleles not necessarily on the same chromosome. This results in strong correlation between variables in a dataset, which can complicate analysis. In the current study, data simulations with a range of LD patterns are used to assess the impact of such correlated variables on the performance of GENN. Our results show that not only do patterns of strong LD not decrease the power of GENN to detect genetic associations, they actually increase its power.

Machine learning methods applied to genotyping data capture interactions between single nucleotide variants in late onset Alzheimer's disease

“…Other studies indicated that ML methods are robust in terms of performance when dealing with SNVs in LD. 44,45,46 As in other works, 16 we found that tree-based ML methods can add an important layer of information to the disease-related variants obtained with other population genomic approaches such as GWAS.…”

“…Other studies indicated that ML methods are robust in terms of performance when dealing with SNVs in LD. 44 , 45 , 46 …”

“…However, with the data we had we could not demonstrate that the three SNVs were independently associated with AD. Other studies indicated that ML methods are robust in terms of performance when dealing with SNVs in LD 44,45,46 …”

Spatially Uniform ReliefF (SURF) for computationally-efficient filtering of gene-gene interactions

“…Stochastic search approaches such as genetic programming [ 11 - 13 ] and ant colony optimization [ 14 ] can successfully develop models in this domain when information from the Relief family of algorithms is used to assist the search, although they fail to detect purely epistatic associations without this additional information [ 12 ]. Motsinger et al [ 15 ] have shown that patterns of correlation between SNPs can make the problem easier to solve in the absence of expert knowledge, although here we specifically examine uncorrelated SNPs. Moore et al briefly discuss both filter and wrapper options as part of an overall epistasis analysis strategy for human disease susceptibility [ 16 ] and Greene et al [ 17 ] provide a theoretical analysis of both approaches.…”