A statistical method for image-mediated association studies discovers genes and pathways associated with four brain disorders

He, Jialin; Antonyan, Lilit; Zhu, Haobo; Li, Qing; Enoma, David; Zhang, William; Liu, Andy; Cao, Bo; MacDonald, Ethan; Arnold, Paul; Long, Quan

doi:10.1101/2023.06.16.545326

Cited by 3 publications

(2 citation statements)

References 104 publications

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“…First, our evaluation focused solely on the performance using the BSLMM method for GReX prediction. However, our AE-TWAS framework can also be adapted to other TWAS methods, such as ElasticNet implemented by PrediXcan and kernel methods articulated by ourselves (11,15,16,32). We chose BSLMM because our previous experiences show its supremacy to ElasticNet (16) and popularity in the field.…”

Section: Discussionmentioning

confidence: 99%

“…Then, in the second step, in the GWAS dataset which contains genotype and phenotype (but not expression), the above trained model is used to predict GReX which is subsequently associated with phenotypic traits. TWAS has since achieved popularity and success in identifying the genetic basis of complex traits (4)(5)(6)(7)(8)(9), inspiring similar protocols for other endophenotypes such as IWAS (10,11) for images and PWAS (12) for proteins. Despite of its success in many projects, due to the low heritability of gene expressions, out of around 20,000 genes, usually only 7,000-10,000 genes may be analyzed in a TWAS project (2,5,13).…”

Section: Introductionmentioning

confidence: 99%

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Autoencoder-transformed transcriptome improves genotype-phenotype association studies

Bian¹,

Li²,

Leung³

et al. 2023

Preprint

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Transcriptome-wide association study (TWAS) is an emerging model leveraging gene expressions to direct genotype-phenotype association mapping. A key component in TWAS is the prediction of gene expressions; and many statistical approaches have been developed along this line. However, a problem is that many genes have low expression heritability, limiting the performance of any predictive model. In this work, hypothesizing that appropriate denoising may improve the quality of expression data (including heritability), we propose AE-TWAS, which adds a transformation step before conducting standard TWAS. The transformation is composed of two steps by first splitting the whole transcriptome into co-expression networks (modules) and then using autoencoder (AE) to reconstruct the transcriptome data within each module. This transformation removes noise (including nonlinear ones) from the transcriptome data, paving the path for downstream TWAS. We applied AE-TWAS to the GTEx whole blood transcriptome data and GWAS data of five human diseases, showing two inspiring properties of AE-TWAS: (1) After transformation, the transcriptome data enjoy higher expression heritability at the low-heritability spectrum and possess higher connectivity within the modules. (2) The transferred transcriptome indeed enables better performance of TWAS; and moreover, the newly formed highly connected genes (i.e., hub genes) are more functionally relevant to diseases, evidenced by their functional annotations and overlap with TWAS hits. Taking together, we show that autoencoder transformation produces better transcriptome, which in turn enables improved expression-assisted genotype-phenotype association mapping. The impact of this work may be beyond the field of gene mapping: AE can be deemed as a nonlinear extension of principal component analysis (PCA) that is used for removing artifacts in expression data routinely. As such, this work may inspire more expression-based applications to be carried out after an appropriate AE-transformation, unlocking the use of AE-denoised transcriptome in many fields.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Autoencoder-transformed transcriptome improves genotype-phenotype association studies

Bian¹,

Li²,

Leung³

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

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IBAS: Interaction-bridged association studies discovering novel genes underlying complex traits

Kossinna,

Kumarapeli,

Zhang

2023

Preprint

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The contribution of genetic variants to a complex phenotype may be mediated by various forms of complicated interactions. Currently, the discovery of genetic variants underlying interaction is limited, partly due to that the real interaction patterns are diverse and unknown, whereas exhaustively examining all potential combinations confers the risk of overfitting and instability. We propose IBAS, Interaction-Bridged Association Study, a new model using statistical learning techniques to extract representations of interaction patterns in transcriptome data, which act as a mediator for the next genotype-phenotype association test. Using simulated perturbation experiments, it is demonstrated that IBAS is more robust to noise than similar mediation-based protocols replying on single-genes, i.e., transcriptome-wide association studies (TWAS). By applying IBAS to real genotype-phenotype and expression data, we reported additional genes underlying complex traits as well as their biological annotations. IBAS unlocks the power of integrating gene-gene interactions in association mapping without concerning overfitting and instability.

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OmeSim: a genetics-based nonlinear simulator for in-between-ome and phenotype

Long,

Zhang

2024

Preprint

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MotivationDeciphering genetic basis of complex traits via genotype-phenotype association studies is a long-standing theme in genetics. The availability of molecular omics data (such as transcriptome) has enabled researchers to utilize “in-between-omes” in association studies, for instance transcriptome-wide association study. Although many statistical tests and machine learning models integrating omics in genetic mapping are emerging, there is no standard way to simulate phenotype by genotype with the role of in-between-omes incorporated. Moreover, the involvement of in-between-omes usually bring substantial nonlinear architecture (e.g., co-expression network), that may be non-trivial to simulate. As such, rigorous power estimations, a critical step to test novel models, may not be conducted fairly.ResultsTo address the gap between emerging methods development and the unavailability of adequate simulators, we developed OmeSim, a phenotype simulator incorporating genetics, an in-between-ome (e.g., transcriptome), and their complex relationships including nonlinear architectures. OmeSim outputs detailed causality graphs together with original data, correlations, and associations structures between phenotypic traits and omes terms as comprehensive gold-standard datasets for the verifications of novel tools integrating an in-between-ome in genotype-phenotype association studies. We expect OmeSim to enable rigorous benchmarking for the future multi-omics integrations.Availabilityhttps://github.com/zhoulongcoding/OmeSimContactqingrun.zhang@ucalgary.ca

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A statistical method for image-mediated association studies discovers genes and pathways associated with four brain disorders

Cited by 3 publications

References 104 publications

Autoencoder-transformed transcriptome improves genotype-phenotype association studies

Autoencoder-transformed transcriptome improves genotype-phenotype association studies

IBAS: Interaction-bridged association studies discovering novel genes underlying complex traits

OmeSim: a genetics-based nonlinear simulator for in-between-ome and phenotype

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