How next-generation sequencing is transforming complex disease genetics

Kilpinen, Helena; Barrett, Jeffrey C.

doi:10.1016/j.tig.2012.10.001

Cited by 74 publications

(38 citation statements)

References 67 publications

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“…32 Projects such as ENCODE are likely to provide new insights into GWAS associations in non-coding regions of the genome. 38 Together, these multiple approaches will help us identify additional genetic associations and understand their functional implications.…”

Section: Discussionmentioning

confidence: 99%

A systematic review of cancer GWAS and candidate gene meta-analyses reveals limited overlap but similar effect sizes

et al. 2013

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Candidate gene and genome-wide association studies (GWAS) represent two complementary approaches to uncovering genetic contributions to common diseases. We systematically reviewed the contributions of these approaches to our knowledge of genetic associations with cancer risk by analyzing the data in the Cancer Genome-wide Association and Meta Analyses database (Cancer GAMAdb). The database catalogs studies published since January 1, 2000, by study and cancer type. In all, we found that meta-analyses and pooled analyses of candidate genes reported 349 statistically significant associations and GWAS reported 269, for a total of 577 unique associations. Only 41 (7.1%) associations were reported in both candidate gene meta-analyses and GWAS, usually with similar effect sizes. When considering only noteworthy associations (defined as those with false-positive report probabilities r0.2) and accounting for indirect overlap, we found 202 associations, with 27 of those appearing in both meta-analyses and GWAS. Our findings suggest that meta-analyses of well-conducted candidate gene studies may continue to add to our understanding of the genetic associations in the post-GWAS era.

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Section: Discussionmentioning

confidence: 99%

A systematic review of cancer GWAS and candidate gene meta-analyses reveals limited overlap but similar effect sizes

et al. 2013

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“…MS is diagnosed in about 2.3 million people worldwide (Browne et al 2014), affects mostly young people leading to early disability, and reduces the lifespan by approximately 7 years as compared with the general population (Marrie et al 2015;Kaufman et al 2014;Leray et al 2015). MS is a complex disease: several causes underlie its development and are still incompletely understood (Marian 2012;Kilpinen and Barrett 2013).…”

Section: Introductionmentioning

confidence: 99%

A review of genome-wide association studies for multiple sclerosis: classical and hypothesis-driven approaches

et al. 2015

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Multiple sclerosis (MS) is a common complex neurodegenerative disease of the central nervous system. It develops with autoimmune inflammation and demyelination. Genome-wide association studies (GWASs) serve as a powerful tool for investigating the genetic architecture of MS and are generally used to identify the genetic factors of disease susceptibility, clinical phenotypes, and treatment response. This review considers the main achievements and challenges of using GWAS to identify the genes involved in MS. It also describes hypothesis-driven studies with extensive genome coverage of the selected regions, complementary to GWASs. To date, over 100 MS risk loci have been identified by the combination of both approaches; 40 of them were found in at least two GWASs and meet genome-wide significance threshold (p ≤ 5 × 10(-8)) in at least one GWAS, whereas the threshold for the rest of GWASs was set in our review at p < 1 × 10(-5). Yet, MS risk loci identified to date explain only a part of the total heritability, and the reasons of "missing heritability" are discussed. The functions of MS-associated genes are described briefly; the majority of them encode immune-response proteins involved in the main stages of MS pathogenesis.

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“…The efficacy of RNA-seq for detecting causal mutations was further underscored by the need for multiple DNAbased assays, including the sequencing of an uninformative (control) gene panel, to identify a causal variant [34]. By contrast, RNA-seq was employed to confirm the existence of truncating and splice-site mutations that led to rare disorders in three individuals [35].…”

Section: Interpretation Validation and Characterization Of Functionmentioning

confidence: 99%

RNA Sequencing and Genetic Disease

et al. 2016

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Purpose of Review Next-generation sequencing is a revolutionary approach for highly accurate identification of gene associations with specific human disease phenotypes. RNA sequencing (RNA-seq) holds great promise for identifying distinct gene expression ''signatures'' for the detection, prognosis, and chemosensitivity of human disease. However, this technique has yet to be adopted as a standard medical practice. Recent Findings The recent emergence of high-throughput, next-generation sequencing technology has facilitated significant advancements in understanding evolutionary diversity and disease. RNA-seq in particular is an invaluable tool for profiling transcription across the entire human genome (i.e., the ''transcriptome'') at high resolution, providing the means to quantitatively study links among genotypes, transcript abundance, and other transcript-based features and human phenotypes. RNA-seq technology provides an essential layer of molecular information providing investigators and clinicians a systems genetics prospective of human disease. Integrated analysis of DNA-and RNA-seq data allows elucidation of the causal and regulatory mechanisms of the complex traits of human disease. Summary Here, we review RNA-seq technology and present a summary of different methods and their application to the study of human genetic disease. We further illustrate the utility of RNA-seq technology as an important tool for identifying novel transcriptomic biomarkers, and how these link to the pathological phenotypes of human disease initiation and progression. Advanced machine learning techniques for RNA-seq data analysis are also discussed.

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How next-generation sequencing is transforming complex disease genetics

Cited by 74 publications

References 67 publications

A systematic review of cancer GWAS and candidate gene meta-analyses reveals limited overlap but similar effect sizes

A systematic review of cancer GWAS and candidate gene meta-analyses reveals limited overlap but similar effect sizes

A review of genome-wide association studies for multiple sclerosis: classical and hypothesis-driven approaches

RNA Sequencing and Genetic Disease

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