A PheWAS Model of Autism Spectrum Disorder

Matta, John; Dobrino, Daniel; Howard, Swade; Yeboah, Dacosta; Kopel, Jonathan; EL-Manzalawy, Yasser; Obafemi-Ajayi, Tayo

doi:10.1109/embc46164.2021.9629533

Cited by 1 publication

(8 citation statements)

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“…In this study, we investigate a more holistic PheWAS-inspired method to identify meaningful associations between ASD phenotypes and genotypes. This work, as shown in Figure 1 , significantly extends the preliminary results presented in Matta et al ( 2021 ). We examine multiple methodologies for implementing a p-p network, creating both direct networks using only phenotype data as well as indirect networks that incorporate SNP data.…”

Section: Introductionsupporting

confidence: 90%

“…For the remainder of this paper, for brevity, we conduct the model interpretation analysis for the cosine fused graph (G α = 0.2 ), cosine indirect graph (G I ), and Euclidean direct graph (G D ) clustering results. We also compare the outcomes to our prior work (Matta et al, 2021).…”

Section: Resultsmentioning

confidence: 99%

“…The dysmorphic biomarker phenotype, quantified using the Autism Dysmorphology Measure (Miles et al, 2008 ), distinguishes complex autism (dysmorphic and/or microcephalic) (Miles, 2015 ) from essential autism (non-dysmorphic and not microcephalic). Given that the distinction between complex and essential autism is important in dissecting the ASD etiologic heterogeneity (Miles, 2015 ; Spencer et al, 2018 ), we restricted the sample analyzed in this work to only the 560 probands who underwent dysmorphology examinations (Zhao et al, 2019 ; Matta et al, 2021 ). All the 51 phenotype markers utilized in this work are summarized in Table 1 .…”

Section: Methodsmentioning

confidence: 99%

“…In addition, we extract a matrix of p -values measuring the probability of phenotype-SNP correlation, where a smaller p -value indicates a higher probability of correlation. Unlike in previous work (Matta et al, 2021 ), the matrix of p -values is not filtered based on any threshold. In contrast to Matta et al ( 2021 ), where the filtered matrix was fed directly into the clustering model, we convert this matrix to a kNN graph.…”

Section: Methodsmentioning

confidence: 99%

“…The PheWAS concept is very effective as even studies with small sample sizes have been able to validate previous studies and enhance single SNP results with pleiotropic relationships (Hebbring et al, 2013 ). In Matta et al ( 2021 ), we presented a simple PheWAS model to identify SNP associations among ASD phenotypes. Our hypothesis was that by viewing a heterogeneous disorder, such as ASD, as an aggregate of multiple subtypes, we could derive a PheWAS model to identify novel genetic and cross-phenotype associations.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Connecting phenotype to genotype: PheWAS-inspired analysis of autism spectrum disorder

Matta

Dobrino

Yeboah

et al. 2022

Front. Hum. Neurosci.

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Autism Spectrum Disorder (ASD) is extremely heterogeneous clinically and genetically. There is a pressing need for a better understanding of the heterogeneity of ASD based on scientifically rigorous approaches centered on systematic evaluation of the clinical and research utility of both phenotype and genotype markers. This paper presents a holistic PheWAS-inspired method to identify meaningful associations between ASD phenotypes and genotypes. We generate two types of phenotype-phenotype (p-p) graphs: a direct graph that utilizes only phenotype data, and an indirect graph that incorporates genotype as well as phenotype data. We introduce a novel methodology for fusing the direct and indirect p-p networks in which the genotype data is incorporated into the phenotype data in varying degrees. The hypothesis is that the heterogeneity of ASD can be distinguished by clustering the p-p graph. The obtained graphs are clustered using network-oriented clustering techniques, and results are evaluated. The most promising clusterings are subsequently analyzed for biological and domain-based relevance. Clusters obtained delineated different aspects of ASD, including differentiating ASD-specific symptoms, cognitive, adaptive, language and communication functions, and behavioral problems. Some of the important genes associated with the clusters have previous known associations to ASD. We found that clusters based on integrated genetic and phenotype data were more effective at identifying relevant genes than clusters constructed from phenotype information alone. These genes included five with suggestive evidence of ASD association and one known to be a strong candidate.

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Section: Introductionsupporting

confidence: 90%

Section: Resultsmentioning

confidence: 99%