Interpreting noncoding genetic variation in complex traits and human disease
Abstract
Association studies provide genome-wide information about the genetic basis of complex disease, but medical research has focused primarily on protein-coding variants, owing to the difficulty of interpreting noncoding mutations. This picture has changed with advances in the systematic annotation of functional noncoding elements. Evolutionary conservation, functional genomics, chromatin state, sequence motifs and molecular quantitative trait loci all provide complementary information about the function of noncoding sequences. These functional maps can help with prioritizing variants on risk haplotypes, filtering mutations encountered in the clinic and performing systems-level analyses to reveal processes underlying disease associations. Advances in predictive modeling can enable data-set integration to reveal pathways shared across loci and alleles, and richer regulatory models can guide the search for epistatic interactions. Lastly, new massively parallel reporter experiments can systematically validate regulatory predictions. Ultimately, advances in regulatory and systems genomics can help unleash the value of whole-genome sequencing for personalized genomic risk assessment, diagnosis and treatment.
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Most disease-associated genetic variation lies in non-coding regions enriched for regulatory elements that control expression and splicing of target genes.
“The majority of disease-associated genetic variation lies in non-coding regions (7) enriched for non-coding RNAs and cis regulatory elements that regulate gene expression and splicing of their cognate coding gene targets (8, 9).”
From Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder (2018) · cites this paper as [8]
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“The majority of disease-associated genetic variation lies in noncoding regions (5) enriched for noncoding RNAs (ncRNAs) and cis-regulatory elements that regulate gene expression and splicing of their cognate coding gene targets (6, 7).”
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“Let d = b= − b> (5) The sampling variance of d can be written as V d = V b= + V b> − 2θ V b= V b> (6) where b= and b> are the estimated effect sizes of the top associated cis-eQTL for a gene in two tissues, V b= and V b> are the sampling variance for b= and b> , respectively, and θ is sampling correlation between b= a…”
BackgroundThe Dynamic Landscape of Open Chromatin during Human Cortical Neurogenesis
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“Characterizing these regions is of great importance because most human-specific genetic variation and risk loci for neuropsychiatric diseases are found in these likely regulatory, non-coding regions (Ward and Kellis, 2012).”
BackgroundInfluentialTissue-specific regulatory circuits reveal variable modular perturbations across complex diseases
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“[7] Ward & Kellis [8] review works demonstrating the influence of common alleles contributing small, but meaningful effects in traits or disease risk [9] – as in the present work, loci associated with complex traits at P-values far below those considered significant on a genome-wide level have clustered across the gen…”
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“Ward and Kellis (2012) have commented on works demonstrating the influence of common alleles contributing small but meaningful effects in traits or disease risk (Purcell et al. 2009); these loci, associated with complex traits at P-values far below those considered significant on a genome-wide…”
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“Most adaptive traits are affected by many segregating loci and are thus difficult to study (Ward & Kellis, 2012 ), explaining why relatively few studies have so far elucidated fine scale pat - terns of local adaptation.”
Genetic variation within transcriptional regulatory elements and its implications for human disease
Rada-Iglesias, A. (2014) · Biological Chemistry · doi:10.1515/hsz-2014-0109 · Cited by 7 · see citing works
“This means that, in many cases, the genetic variants reported in GWAS are not necessarily causative themselves, but instead, they can be in strong linkage disequilibrium (LD) with the true causative variants as part of large haplotypes (Ward and Kellis, 2012).”
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“mutations,[3,4] annotating the molecular basis of human diseases still remains a great challenge.”
BackgroundGhaffar, A. (2022) · Queensland University of Technology · doi:10.5204/thesis.eprints.236251
- Authors
- Ward, L. D., & Kellis, M.
- Affiliations
- MIT Computer Science and Artificial Intelligence Laboratory (US)
- Broad Institute (US)
- Corresponding author
- Ward, Lucas D
- Kellis, Manolis
- Venue
- Nature Biotechnology
- Published
- 2012-11
- Volume
- 30
- Issue
- 11
- Pages
- 1095-1106
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- OpenAlex
- W1595121045
- Cited by
- 6
- References
- 198
- Citations by year
- 2023: 2 · 2022: 1 · 2019: 1 · 2014: 2
- FWCI
- 0.08
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- PMC
- Funders
- National Human Genome Research Institute (RC1HG005334, R01HG004037)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Keywords
- complex traits, human disease, regulatory elements, evolutionary conservation, chromatin state, sequence motifs, quantitative trait loci, risk haplotype, variant prioritization, epistasis, massively parallel reporter assay, systems genomics
- MeSH terms
- Animals, Chromosome Mapping, Genetic Testing, Humans, Genetic Variation, Sequence Analysis, DNA, Genetic Predisposition to Disease, RNA, Untranslated, Quantitative Trait Loci
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