Genetic variants in association studies – review of strengths and weaknesses in study design and current knowledge of impact on cancer risk (original) (raw)
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Cancer heterogeneity: origins and implications for genetic association studies
Trends in genetics : TIG, 2012
Genetic association studies have become standard approaches to characterize the genetic and epigenetic variability associated with cancer development, including predispositions and mutations. However, the bewildering genetic and phenotypic heterogeneity inherent in cancer both magnifies the conceptual and methodological problems associated with these approaches and renders difficult the translation of available genetic information into a knowledge that is both biologically sound and clinically relevant. Here, we elaborate on the underlying causes of this complexity, illustrate why it represents a challenge for genetic association studies, and briefly discuss how it can be reconciled with the ultimate goals of identifying targetable disease pathways and successfully treating individual patients.
A Compendium of Genome-Wide Associations for Cancer: Critical Synopsis and Reappraisal
Journal of The National Cancer Institute, 2010
Since 2007, genome-wide association (GWA) studies have identified numerous well-supported, novel genetic risk loci for common cancers; however, there are concerns that this technology is reaching its limits. We provide an overview of GWAidentified genetic associations with solid tumors. We simulated the distribution of population risk alleles for colorectal, prostate, testicular, and thyroid cancers based on genetic variants identified in GWA studies. We also evaluated whether statistical power to detect typical genetic effects could be improved with studies performing GWA analyses of all available samples rather than multistage designs. Fifty-six eligible articles yielded 92 eligible associations between cancer phenotypes and genetic variants with a median per-allele odds ratio (OR) of 1.22 (interquartile range = 1.15-1.36). Half of the associations pertained to prostate, colorectal, or breast cancer. Individuals at the upper quartile of simulated risk had only 2.1-to 4.2-fold higher relative risk than those in the lower quartile. Comprehensive evaluation of currently available samples with GWA platforms would yield few additional variants with per-allele OR = 1.4, but many more variants with OR = 1.2 could be detected; statistical power to detect weak associations (OR = 1.07) would still be negligible. The GWA approach is effective in identifying common genetic variants with moderate effect; however, identifying loci with very small effects and rare variants will require major new efforts. At present, the utility of GWA-identified risk loci in risk stratification for cancer is limited. J Natl Cancer Inst 2010;102:846-858 jnci.oxfordjournals.org JNCI | Review 847
Computational Biology, 2009
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Human Heredity, 2011
Objectives: Genome-wide association (GWA) studies still rely on the common-disease common-variant hypothesis since the assumption is associated with increased power. In GWA studies, polymorphisms are genotyped and their association with disease is investigated. Most of the identified associations are indirect and reflect a shared inheritance of the genotyped markers and genetically linked causal variants. We have compared six statistics of genetic association regarding their ability to discriminate between markers and causal susceptibility variants, including a probability value (Pval) and a Bayes Factor (BF) based on logistic regression, and the attributable familial relative risk (FRR). Methods: We carried out a simulation-based sensitivity analysis to explore several conceivable scenarios. Theoretical results were illustrated by established causal associations with age-related macular degeneration and by using imputed data based on HapMap for a case-control study of breast cancer...
Genome-wide association studies: progress and potential for drug discovery and development
Nature Reviews Drug Discovery, 2008
Although genetic studies have been critically important for the identification of therapeutic targets in Mendelian disorders, genetic approaches aiming to identify targets for common, complex diseases have traditionally had much more limited success. However, during the past year, a novel genetic approach -genome-wide association (GWA) -has demonstrated its potential to identify common genetic variants associated with complex diseases such as diabetes, inflammatory bowel disease and cancer. Here, we highlight some of these recent successes, and discuss the potential for GWA studies to identify novel therapeutic targets and genetic biomarkers that will be useful for drug discovery, patient selection and stratification in common diseases.
In Search of Complex Disease Risk through Genome Wide Association Studies
Mathematics, 2021
The identification and characterisation of genomic changes (variants) that can lead to human diseases is one of the central aims of biomedical research. The generation of catalogues of genetic variants that have an impact on specific diseases is the basis of Personalised Medicine, where diagnoses and treatment protocols are selected according to each patient’s profile. In this context, the study of complex diseases, such as Type 2 diabetes or cardiovascular alterations, is fundamental. However, these diseases result from the combination of multiple genetic and environmental factors, which makes the discovery of causal variants particularly challenging at a statistical and computational level. Genome-Wide Association Studies (GWAS), which are based on the statistical analysis of genetic variant frequencies across non-diseased and diseased individuals, have been successful in finding genetic variants that are associated to specific diseases or phenotypic traits. But GWAS methodology i...