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Autism-related dietary preferences mediate autism-gut microbiome associations.

Authors :
Yap, CX
Henders, AK
Alvares, GA
Wood, DLA
Krause, L
Tyson, GW
Restuadi, R
Wallace, L
McLaren, T
Hansell, NK
Cleary, D
Grove, R
Hafekost, C
Harun, A
Holdsworth, H
Jellett, R
Khan, F
Lawson, LP
Leslie, J
Frenk, ML
Masi, A
Mathew, NE
Muniandy, M
Nothard, M
Miller, JL
Nunn, L
Holtmann, G
Strike, LT
de Zubicaray, GI
Thompson, PM
McMahon, KL
Wright, MJ
Visscher, PM
Dawson, PA
Dissanayake, C
Eapen, V
Heussler, HS
McRae, AF
Whitehouse, AJO
Wray, NR
Gratten, J
Yap, CX
Henders, AK
Alvares, GA
Wood, DLA
Krause, L
Tyson, GW
Restuadi, R
Wallace, L
McLaren, T
Hansell, NK
Cleary, D
Grove, R
Hafekost, C
Harun, A
Holdsworth, H
Jellett, R
Khan, F
Lawson, LP
Leslie, J
Frenk, ML
Masi, A
Mathew, NE
Muniandy, M
Nothard, M
Miller, JL
Nunn, L
Holtmann, G
Strike, LT
de Zubicaray, GI
Thompson, PM
McMahon, KL
Wright, MJ
Visscher, PM
Dawson, PA
Dissanayake, C
Eapen, V
Heussler, HS
McRae, AF
Whitehouse, AJO
Wray, NR
Gratten, J
Publication Year :
2024

Abstract

(Cell 184, 5916–5931; November 24, 2021) Our paper reported evidence that autism-related dietary preferences mediate autism-microbiome associations. Since publication, we have become aware of an error in our paper that we are now correcting. Specifically, in the code we wrote and used to transform the microbiome count matrices in our variance component analysis, we inadvertently missed a matrix transposition, which affected their centered-log-ratio (clr) transformation and affected variance estimates in Figure 2 and Table S1 (listed in detail below). By missing the matrix transposition, we incorrectly calculated the geometric mean per-taxa rather than per-individual. However, the error does not affect the conclusions of the paper because the per-taxa and per-individual geometric means are similar, and so the resulting clr transformed matrices are similar as well (note that the clr transform should take the quotient of a microbiome/taxa quantity by the geometric mean of microbiome quantities across the sample/individual). To show that this is the case, we compared the correctly (geometric mean calculated per-individual) and incorrectly (geometric mean calculated per-taxa) clr transformed matrices by taking the nth column of both matrices (representing each of 247 individuals’ microbiome data) and calculating the Pearson's correlation coefficient between them. The median Pearson's correlation coefficient ranged from 0.90–0.94 for the common species, rare species, common genes, and rare genes matrices. As the correctly and incorrectly transformed matrices are highly correlated, this error has negligible impact on the variance component analysis results and does not change the overall conclusions of our work. The code error did not affect which microbiome features were identified as being differentially abundant, as the method used for this analysis (ANCOMv2.1) takes un-transformed count data as input. However, the data visualization for this analysis was affected with

Details

Database :
OAIster
Publication Type :
Electronic Resource
Accession number :
edsoai.on1439681823
Document Type :
Electronic Resource