Estimating heritability in pharmacogenetic studies

A robust DF-REML framework for variance components estimation in genetic studies

Motivation

In genetic association studies, linear mixed models (LMMs) are used to test for associations between phenotypes and candidate single nucleotide polymorphisms (SNPs). These same models are also used to estimate heritability, which is central not only to evolutionary biology but also to the prediction of the response to selection in plant and animal breeding, as well as the prediction of disease risk in humans. However, when one or more of the underlying assumptions are violated, the estimation of variance components may be compromised and therefore so may the estimates of heritability and any other functions of these. Considering that datasets obtained from real life experiments are prone to several sources of contamination, which usually induce the violation of the assumption of the normality of the errors, a robust derivative-free restricted-maximum likelihood framework (DF-REML) together with a robust coefficient of determination are proposed for the LMM in the context of genetic studies of continuous traits.

Results

The proposed approach, in addition to the robust estimation of variance components and robust computation of the coefficient of determination, allows in particular for the robust estimation of SNP-based heritability by reducing the bias and increasing the precision of its estimates. The performance of both classical and robust DF-REML approaches is compared via a Monte Carlo simulation study. Additionally, three examples of application of the methodologies to real datasets are given in order to validate the usefulness of the proposed robust approach. Although the main focus of this article is on plant breeding applications, the proposed methodology is applicable to both human and animal genetic studies.

Availability and implementation

Source code implemented in R is available in the Supplementary Material.

Contact

vmml@fct.unl.pt.

Supplementary information

Supplementary data are available at Bioinformatics online.

Precision Medicine in Rheumatoid Arthritis

Treatment of rheumatoid arthritis (RA) has substantially improved in recent years because of the development of novel drugs. However, response is not universal for any of the treatment options, and selection of an effective therapy is currently based on a trial-and-error approach. Delayed treatment response increases the risk of progressive joint damage and resultant disability and also has a significant impact on quality of life for patients. For many drugs, the patient's genetic background influences response to therapy, and understanding the genetics of response to therapy in RA may allow for targeted personalized health care.

Cysteinyl Leukotrienes Pathway Genes, Atopic Asthma and Drug Response: From Population Isolates to Large Genome-Wide Association Studies

Genetic variants associated with asthma pathogenesis and altered response to drug therapy are discussed. Many studies implicate polymorphisms in genes encoding the enzymes responsible for leukotriene synthesis and intracellular signaling through activation of seven transmembrane domain receptors, such as the cysteinyl leukotriene 1 (CYSLTR1) and 2 (CYSLTR2) receptors. The leukotrienes are polyunsaturated lipoxygenated eicosatetraenoic acids that exhibit a wide range of pharmacological and physiological actions. Of the three enzymes involved in the formation of the leukotrienes, arachidonate 5 lipoxygenase 5 (ALOX5), leukotriene C4 synthase (LTC4S), and leukotriene hydrolase (LTA4H) are all polymorphic. These polymorphisms often result in variable production of the CysLTs (LTC4, LTD4, and LTE4) and LTB4. Variable number tandem repeat sequences located in the Sp1-binding motif within the promotor region of the ALOX5 gene are associated with leukotriene burden and bronchoconstriction independent of asthma risk. A 444A > C SNP polymorphism in the LTC4S gene, encoding an enzyme required for the formation of a glutathione adduct at the C-6 position of the arachidonic acid backbone, is associated with severe asthma and altered response to the CYSLTR1 receptor antagonist zafirlukast. Genetic variability in the CysLT pathway may contribute additively or synergistically to altered drug responses. The 601 A > G variant of the CYSLTR2 gene, encoding the Met201Val CYSLTR2 receptor variant, is associated with atopic asthma in the general European population, where it is present at a frequency of ∼2.6%. The variant was originally found in the founder population of Tristan da Cunha, a remote island in the South Atlantic, in which the prevalence of atopy is approximately 45% and the prevalence of asthma is 36%. In vitro work showed that the atopy-associated Met201Val variant was inactivating with respect to ligand binding, Ca²⁺ flux and inositol phosphate generation. In addition, the CYSLTR1 gene, located at Xq13-21.1, has been associated with atopic asthma. The activating Gly300Ser CYSLTR1 variant is discussed. In addition to genetic loci, risk for asthma may be influenced by environmental factors such as smoking. The contribution of CysLT pathway gene sequence variants to atopic asthma is discussed in the context of other genes and environmental influences known to influence asthma.

Combining genetic and nongenetic biomarkers to realize the promise of pharmacogenomics for inflammatory diseases

Many drugs used to treat inflammatory diseases are ineffective in a substantial proportion of patients. Identifying patients that are likely to respond to specific therapies would facilitate personalized treatment strategies that could improve outcomes while reducing costs and risks of adverse events. Despite these clear benefits, there are limited examples of predictive biomarkers of drug efficacy currently implemented into clinical practice for inflammatory diseases. We review efforts to identify genetic and nongenetic biomarkers of drug response in these diseases and consider potential benefits from combining multiple sources of biological data into multifeature predictive models.

Should we keep on? Looking into pharmacogenomics of ADHD in adulthood from a different perspective

A considerable proportion of adults with attention-deficit/hyperactivity disorder (ADHD) do not respond to the treatment with methylphenidate. This scenario could be due to inherited interindividual differences that may alter pharmacologic treatment response. In this sense, in 2012 we conducted a systematic search on PUBMED-indexed literature for articles containing information about pharmacogenomics of ADHD in adults. Five studies were found on methylphenidate pharmacogenomics and the only significant association was reported by one particular study. However, this single association with the SLC6A3 gene was not replicated in two subsequent reports. In the present review, although we could not find additional pharmacogenomics studies, we discuss these up-to-date findings and suggest new approaches for this field. Additionally, using systeomic-oriented databases, we provide a broad picture of new possible candidate genes as well as potential gene-gene interactions to be investigated in pharmacogenomics of persistent ADHD.

Missing heritability of common diseases and treatments outside the protein-coding exome

Genetic factors strongly influence risk of common human diseases and treatment outcomes but the causative variants remain largely unknown; this gap has been called the 'missing heritability'. We propose several hypotheses that in combination have the potential to narrow the gap. First, given a multi-stage path from wellness to disease, we propose that common variants under positive evolutionary selection represent normal variation and gate the transition between wellness and an 'off-well' state, revealing adaptations to changing environmental conditions. In contrast, genome-wide association studies (GWAS) focus on deleterious variants conveying disease risk, accelerating the path from off-well to illness and finally specific diseases, while common 'normal' variants remain hidden in the noise. Second, epistasis (dynamic gene-gene interactions) likely assumes a central role in adaptations and evolution; yet, GWAS analyses currently are poorly designed to reveal epistasis. As gene regulation is germane to adaptation, we propose that epistasis among common normal regulatory variants, or between common variants and less frequent deleterious variants, can have strong protective or deleterious phenotypic effects. These gene-gene interactions can be highly sensitive to environmental stimuli and could account for large differences in drug response between individuals. Residing largely outside the protein-coding exome, common regulatory variants affect either transcription of coding and non-coding RNAs (regulatory SNPs, or rSNPs) or RNA functions and processing (structural RNA SNPs, or srSNPs). Third, with the vast majority of causative variants yet to be discovered, GWAS rely on surrogate markers, a confounding factor aggravated by the presence of more than one causative variant per gene and by epistasis. We propose that the confluence of these factors may be responsible to large extent for the observed heritability gap.

Genetic factors affecting statin concentrations and subsequent myopathy: a HuGENet systematic review

Statins, 3-hydroxy-3-methyl-glutaryl-coenzyme A reductase inhibitors, have proven efficacy in both lowering low-density-lipoprotein levels and preventing major coronary events, making them one of the most commonly prescribed drugs in the United States. Statins exhibit a class-wide side effect of muscle toxicity and weakness, which has led regulators to impose both dosage limitations and a recall. This review focuses on the best-characterized genetic factors associated with increased statin muscle concentrations, including the genes encoding cytochrome P450 enzymes (CYP2D6, CYP3A4, and CYP3A5), a mitochondrial enzyme (GATM), an influx transporter (SLCO1B1), and efflux transporters (ABCB1 and ABCG2). A systematic literature review was conducted to identify relevant research evaluating the significance of genetic variants predictive of altered statin concentrations and subsequent statin-related myopathy. Studies eligible for inclusion must have incorporated genotype information and must have associated it with some measure of myopathy, either creatine kinase levels or self-reported muscle aches and pains. After an initial review, focus was placed on seven genes that were adequately characterized to provide a substantive review: CYP2D6, CYP3A4, CYP3A5, GATM, SLCO1B1, ABCB1, and ABCG2. All statins were included in this review. Among the genetic factors evaluated, statin-related myopathy appears to be most strongly associated with variants in SLCO1B1.