A noise-reduction GWAS analysis implicates altered regulation of neurite outgrowth and guidance in autism
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Background: Genome-wide Association Studies (GWAS) have proved invaluable for the identification of disease susceptibility genes. However, the prioritization of candidate genes and regions for follow-up studies often proves difficult due to false-positive associations caused by statistical noise and multiple-testing. In order to address this issue, we propose the novel GWAS noise reduction (GWAS-NR) method as a way to increase the power to detect true associations in GWAS, particularly in complex diseases such as autism. Methods: GWAS-NR utilizes a linear filter to identify genomic regions demonstrating correlation among association signals in multiple datasets. We used computer simulations to assess the ability of GWAS-NR to detect association against the commonly used joint analysis and Fisher's methods. Furthermore, we applied GWAS-NR to a family-based autism GWAS of 597 families and a second existing autism GWAS of 696 families from the Autism Genetic Resource Exchange (AGRE) to arrive at a compendium of autism candidate genes. These genes were manually annotated and classified by a literature review and functional grouping in order to reveal biological pathways which might contribute to autism aetiology. Results: Computer simulations indicate that GWAS-NR achieves a significantly higher classification rate for true positive association signals than either the joint analysis or Fisher's methods and that it can also achieve this when there is imperfect marker overlap across datasets or when the closest disease-related polymorphism is not directly typed. In two autism datasets, GWAS-NR analysis resulted in 1535 significant linkage disequilibrium (LD) blocks overlapping 431 unique reference sequencing (RefSeq) genes. Moreover, we identified the nearest RefSeq gene to the non-gene overlapping LD blocks, producing a final candidate set of 860 genes. Functional categorization of these implicated genes indicates that a significant proportion of them cooperate in a coherent pathway that regulates the directional protrusion of axons and dendrites to their appropriate synaptic targets. Conclusions: As statistical noise is likely to particularly affect studies of complex disorders, where genetic heterogeneity or interaction between genes may confound the ability to detect association, GWAS-NR offers a powerful method for prioritizing regions for follow-up studies. Applying this method to autism datasets, GWAS-NR analysis indicates that a large subset of genes involved in the outgrowth and guidance of axons and dendrites is implicated in the aetiology of autism.
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Gene-set enrichment and related analyses for AGP and other ASD data sets have found significant evidence for common variants affecting risk.
“gene-set enrichment and related analyses for AGP and other ASD data sets have found significant evidence for common variants affecting risk (54–57)”
From Individual common variants exert weak effects on the risk for autism spectrum disorders (2012) · cites this paper as [56]
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Kessler, K., Seymour, R. A., & Rippon, G. (2016) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.10.002 · Cited by 69 · see citing works
Lack of replication of previous autism spectrum disorder GWAS hits in European populations
Torrico, B., Chiocchetti, A. G., Bacchelli, E., Trabetti, E., Hervás, A., Franke, B., Buitelaar, J. K., Rommelse, N., Yousaf, A., Duketis, E., et al. (2017) · Autism Research · doi:10.1002/aur.1662 · Cited by 47 · see citing works
García-Cabezas, M. Á., Barbas, H., & Zikopoulos, B. (2018) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2018.00070 · Cited by 30 · see citing works
Vardarajan, B. N., Eran, A., Jung, J.-Y., Kunkel, L. M., & Wall, D. P. (2013) · Translational Psychiatry · doi:10.1038/tp.2013.38 · Cited by 26 · see citing works
- Authors
- Hussman, J. P., Chung, R.-H., Griswold, A. J., Jaworski, J. M., Salyakina, D., Ma, D., Konidari, I., Whitehead, P. L., Vance, J. M., Martin, E. R., Cuccaro, M. L., Gilbert, J. R., Haines, J. L., & Pericak-Vance, M. A.
- Affiliations
- University of Miami (US)
- Vanderbilt University (US)
- ORCID
- Venue
- Molecular Autism
- Published
- 2011-01-19
- Volume
- 2
- Issue
- 1
- Pages
- 1-1
- ISSN
- 2040-2392 (ISSN-L)
- PMC
- PMC3035032
- OpenAlex
- W2160038617
- Cited by
- 231
- References
- 78
- Citations by year
- 2026: 7 · 2025: 4 · 2024: 12 · 2023: 7 · 2022: 11 · 2021: 13 · 2020: 9 · 2019: 16 · 2018: 13 · 2017: 25 · 2016: 24 · 2015: 21 · 2014: 24 · 2013: 23 · 2012: 15
- FWCI
- 11.21
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Molecular Autism (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Autism Speaks
- Hussman Foundation
- National Institutes of Health (NS26630)
- Medical Research Council
- National Institute of Mental Health (R01MH080647)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Autism Spectrum Disorder Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetics and Neurodevelopmental Disorders
- Keywords
- autism spectrum disorder, candidate gene prioritization, multiple testing correction, linkage disequilibrium blocks, neurite outgrowth, autism candidate genes, autism etiology
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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