Journal
HUMAN MUTATION
Volume 40, Issue 9, Pages 1519-1529Publisher
WILEY
DOI: 10.1002/humu.23875
Keywords
CAGI; critical assessment; enzymatic activity; machine learning; Sanfilippo syndrome; variants of unknown significance; alpha-N-acetylglucosaminidase; NAGLU
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Funding
- Eesti Teadusagentuur [IUT34-12] Funding Source: Medline
- National Health and Medical Research Council of Australia [1083450, 1121629, 1059775] Funding Source: Medline
- NHGRI NIH HHS [U41 HG007346, R13 HG006650] Funding Source: Medline
- NIA NIH HHS [R01 AG061105] Funding Source: Medline
- NIGMS NIH HHS [U01 GM115486, R01 GM079656, R01 GM115486, R01 GM066099] Funding Source: Medline
- NIMH NIH HHS [R01 MH105524] Funding Source: Medline
- NLM NIH HHS [K99 LM012992, R01 LM009722] Funding Source: Medline
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The NAGLU challenge of the fourth edition of the Critical Assessment of Genome Interpretation experiment (CAGI4) in 2016, invited participants to predict the impact of variants of unknown significance (VUS) on the enzymatic activity of the lysosomal hydrolase alpha-N-acetylglucosaminidase (NAGLU). Deficiencies in NAGLU activity lead to a rare, monogenic, recessive lysosomal storage disorder, Sanfilippo syndrome type B (MPS type IIIB). This challenge attracted 17 submissions from 10 groups. We observed that top models were able to predict the impact of missense mutations on enzymatic activity with Pearson's correlation coefficients of up to .61. We also observed that top methods were significantly more correlated with each other than they were with observed enzymatic activity values, which we believe speaks to the importance of sequence conservation across the different methods. Improved functional predictions on the VUS will help population-scale analysis of disease epidemiology and rare variant association analysis.
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