Motivation: The sequencing of the human genome has made it possible to identify an informative set of >1 million single nucleotide polymorphisms (SNPs) across the genome that can be used to carry out genome-wide association studies (GWASs). The availability of massive amounts of GWAS data has necessitated the development of new biostatistical methods for quality control, imputation and analysis issues including multiple testing. This work has been successful and has enabled the discovery of new associations that have been replicated in multiple studies. However, it is now recognized that most SNPs discovered via GWAS have small effects on disease susceptibility and thus may not be suitable for improving health care through genetic testing. One likely explanation for the mixed results of GWAS is that the current biostatistical analysis paradigm is by design agnostic or unbiased in that it ignores all prior knowledge about disease pathobiology. Further, the linear modeling framework that is employed in GWAS often considers only one SNP at a time thus ignoring their genomic and environmental context. There is now a shift away from the biostatistical approach toward a more holistic approach that recognizes the complexity of the genotype–phenotype relationship that is characterized by significant heterogeneity and gene–gene and gene–environment interaction. We argue here that bioinformatics has an important role to play in addressing the complexity of the underlying genetic basis of common human diseases. The goal of this review is to identify and discuss those GWAS challenges that will require computational methods.

Contact: jason.h.moore@dartmouth.edu

/pdf/bioinformatics-challenges-for-genome-wide-association-bzxf7nsjsj.pdf

Bioinformatics challenges for genome-wide association studies

The widespread availability of high-throughput genotyping technology has opened the door to the era of personal genetics, which brings to consumers the promise of using genetic variations to predict individual susceptibility to common diseases. Despite easy access to commercial personal genetics services, our knowledge of the genetic architecture of common diseases is still very limited and has not yet fulfilled the promise of accurately predicting most people at risk. This is partly because of the complexity of the mapping relationship between genotype and phenotype that is a consequence of epistasis (gene-gene interaction) and other phenomena such as gene-environment interaction and locus heterogeneity. Unfortunately, these aspects of genetic architecture have not been addressed in most of the genetic association studies that provide the knowledge base for interpreting large-scale genetic association results. We provide here an introductory review of how epistasis can affect human health and disease and how it can be detected in population-based studies. We provide some thoughts on the implications of epistasis for personal genetics and some recommendations for improving personal genetics in light of this complexity.

https://www.cell.com/ajhg/pdf/S0002-9297(09)00349-8.pdf

Epistasis and Its Implications for Personal Genetics

The search for the missing heritability in genome-wide association studies (GWAS) has become an important focus for the human genetics community. One suspected location of these genetic effects is in gene-gene interactions, or epistasis. The computational burden of exploring gene-gene interactions in the wealth of data generated in GWAS, along with small to moderate sample sizes, have led to epistasis being an afterthought, rather than a primary focus of GWAS analyses. In this review, we discuss some potential approaches to filter a GWAS dataset to a smaller, more manageable dataset where searching for epistasis is considerably more feasible. We describe a number of alternative approaches, but primarily focus on the use of prior biological knowledge from databases in the public domain to guide the search for epistasis. The manner in which prior knowledge is incorporated into a GWA study can be many and these data can be extracted from a variety of database sources. We discuss a number of these approaches and propose that a comprehensive approach will likely be most fruitful for searching for epistasis in large-scale genomic studies of the current state-of-the-art and into the future.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1469-1809.2010.00630.x

Using biological knowledge to uncover the mystery in the search for epistasis in genome-wide association studies.

Identifying robust, patient-specific, and predictive biomarkers presents a major obstacle in precision oncology To optimize patient-specific therapeutic strategies, here we couple pathway knowledge with large-scale drug sensitivity, RNAi, and CRISPR-Cas9 screening data from 460 cell lines Pathway activity levels are found to be strong predictive biomarkers for the essentiality of 15 proteins, including the essentiality of MAD2L1 in breast cancer patients with high BRCA-pathway activity We also find strong predictive biomarkers for the sensitivity to 31 compounds, including BCL2 and microtubule inhibitors (MTIs) Lastly, we show that Bcl-xL inhibition can modulate the activity of a predictive biomarker pathway and re-sensitize lung cancer cells and tumors to MTI therapy Overall, our results support the use of pathways in helping to achieve the goal of precision medicine by uncovering dozens of predictive biomarkers Predicting an individual's response to therapy is an important goal for precision medicine Here, the authors use an algorithm that takes into account the interaction type and directionality of signalling pathways in protein–protein interactions and find that their pathway analysis can predict essential genes, which may be a target for therapy

/pdf/predicting-and-affecting-response-to-cancer-therapy-based-on-3cpdu7k2nr.pdf

Predicting and affecting response to cancer therapy based on pathway-level biomarkers

Bipolar disorder (BD) is a serious mental illness with well-established, but poorly characterized genetic risk. Lithium is among the best proven mood stabilizer therapies for BD, but treatment responses vary considerably. Based upon these and other findings, it has been suggested that lithium-responsive BD may be a genetically distinct phenotype within the mood disorder spectrum. This assertion has practical implications both for the treatment of BD and for understanding the neurobiological basis of the illness: genetic variation within lithium-sensitive signaling pathways may confer preferential treatment response, and the involved genes may underlie BD in some individuals. Presently, the mechanism of lithium is reviewed with an emphasis on gene-expression changes in response to lithium. Within this context, findings from genetic-association studies designed to identify lithium response genes in BD patients are evaluated. Finally, a framework is proposed by which future pharmacogenetic studies can incorporate advances in genetics, molecular biology and bioinformatics in a pathway-based approach to predicting lithium treatment response.

Pharmacogenetics of lithium response in bipolar disorder.

The candidate gene approach to pharmacogenetics is hypothesis driven, and anchored in biological plausibility. Whole genome scanning is hypothesis generating, and it may lead to new biology. While both approaches are important, the scientific community is rapidly reallocating resources toward the latter. We propose a step-wise approach to large-scale pharmacogenetic association studies that begins with candidate genes, then uses a pathway-based intermediate step, to inform subsequent analyses of data generated through whole genome scanning. Novel computational strategies are explored in the context of two clinically relevant examples, cholesterol synthesis and lipid signaling.

The Pathway Less Traveled: Moving from Candidate Genes to Candidate Pathways in the Analysis of Genome-Wide Data from Large Scale Pharmacogenetic Association Studies.

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The Pathway Less Traveled: Moving from Candidate Genes to Candidate Pathways in the Analysis of Genome-Wide Data from Large Scale Pharmacogenetic Association Studies.