Machine learning approaches to genome-wide association studies

Genome-wide Association Studies (GWAS) are conducted to identify single nucleotide polymorphisms (variants) associated with a phenotype within a specific population. These variants associated with diseases have a complex molecular aetiology with which they cause the disease phenotype. The genotyping data generated from subjects of study is of high dimensionality, which is a challenge. The problem is that the dataset has a large number of features and a relatively smaller sample size. However, statistical testing is the standard approach being applied to identify these variants that influence the phenotype of interest. The wide applications and abilities of Machine Learning (ML) algorithms promise to understand the effects of these variants better. The aim of this work is to discuss the applications and future trends of ML algorithms in GWAS towards understanding the effects of population genetic variant. It was discovered that algorithms such as classification, regression, ensemble, and neural networks have been applied to GWAS for which this work has further discussed comprehensively including their application areas. The ML algorithms have been applied to the identification of significant single nucleotide polymorphisms (SNP), disease risk assessment & prediction, detection of epistatic non-linear interaction, and integrated with other omics sets. This comprehensive review has highlighted these areas of application and sheds light on the promise of innovating machine learning algorithms into the computational and statistical pipeline of genome-wide association studies. This will be beneficial for better understanding of how variants are affected by disease biology and how the same variants can influence risk by developing a particular phenotype for favourable natural selection

Arsenic bioremediation by biogenic iron oxides and sulfides

Microcosms containing sediment from an aquifer in Cambodia with naturally elevated levels of arsenic in the associated groundwater were used to evaluate the effectiveness of microbially mediated production of iron minerals for in situ As remediation. The microcosms were first incubated without amendments for 28 days, and the release of As and other geogenic chemicals from the sediments into the aqueous phase was monitored. Nitrate or a mixture of sulfate and lactate was then added to stimulate biological Fe(II) oxidation or sulfate reduction, respectively. Without treatment, soluble As concentrations reached 3.9±0.9 μMat the end of the 143-day experiment. However, in the nitrate- and sulfate-plus-lactate-amended microcosms, soluble As levels decreased to 0.01 and 0.41±0.13 μM, respectively, by the end of the experiment. Analyses using a range of biogeochemical and mineralogical tools indicated that sorption onto freshly formed hydrous ferric oxide (HFO) and iron sulfide mineral phases are the likely mechanisms for As removal in the respective treatments. Incorporation of the experimental results into a one-dimensional transport-reaction model suggests that, under conditions representative of the Cambodian aquifer, the in situ precipitation of HFO would be effective in bringing groundwater into compliance with the World Health Organization (WHO) provisional guideline value for As (10 ppb or 0.13 μM), although soluble Mn release accompanying microbial Fe(II) oxidation presents a potential health concern. In contrast, production of biogenic iron sulfide minerals would not remediate the groundwater As concentration below the recommended WHO limit.</p