DeepCOMBI: Explainable artificial intelligence for the analysis and discovery in genome-wide association studies

Mieth, Bettina; Rozier, Alexandre; Rodríguez, Juan Antonio; Höhne, Marina; Görnitz, Nico; Müller, Klaus‐Robert

doi:10.1101/2020.11.06.371542

Cited by 3 publications

(1 citation statement)

References 65 publications

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“…Such knowledge-primed models derive connections between nodes in the neural network from available biological network data (Kang, Ding et al 2017, Ma, Yu et al 2018, Snow, Noghabi et al 2019, Fortelny and Bock 2020, van Hilten, Kushner et al 2020, Bourgeais, Zehraoui et al 2021). In addition to potential benefits for prediction performance, these approaches can also be suitable for developing explainable models (Azodi, Tang et al 2020, Mieth, Rozier et al 2020, Novakovsky, Dexter et al 2022.…”

Section: ) Knowledge-guided Networkmentioning

confidence: 99%

Knowledge-driven approaches to improve genomic prediction in plants

Farooq

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Many studies have demonstrated the utility of machine learning (ML) methods for genomic prediction (GP) of various plant traits, but it is still largely unclear if and when ML should be chosen over conventionally used, often simpler parametric methods,. Predictive performance of GP models might depend on a plethora of factors including sample size, number of markers, population structure and genetic architecture. Here, we investigate which problem and dataset characteristics are related to good performance of ML methods for genomic prediction. We compare the predictive performance of two frequently used ensemble ML methods (Random Forest and Extreme Gradient Boosting) with parametric methods including genomic best linear unbiased prediction (GBLUP), reproducing kernel Hilbert space regression (RKHS), BayesA and BayesB. To explore problem characteristics, we use simulated and real plant traits under different genetic complexity levels determined by the number of Quantitative Trait Loci (QTLs), heritability (ℎ � and ℎ � � ), population structure and linkage disequilibrium between causal nucleotides and other SNPs. Decision tree based ensemble ML methods are a reasonable choice for phenotypes with allelic interactions and are comparable to Bayesian methods for additive phenotypes in the case of large effect Quantitative Trait Nucleotides (QTNs). Furthermore, we find that ML methods are susceptible to confounding due to population structure but less sensitive to low linkage disequilibrium than linear parametric methods. Overall, this provides insights into the usefulness of ML in GP as well as guidelines for practitioners.

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Section: ) Knowledge-guided Networkmentioning

confidence: 99%

Knowledge-driven approaches to improve genomic prediction in plants

Farooq

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Quantitative neurogenetics: applications in understanding disease

Afrasiabi

Keane

Heng

et al. 2021

Biochemical Society Transactions

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Neurodevelopmental and neurodegenerative disorders (NNDs) are a group of conditions with a broad range of core and co-morbidities, associated with dysfunction of the central nervous system. Improvements in high throughput sequencing have led to the detection of putative risk genetic loci for NNDs, however, quantitative neurogenetic approaches need to be further developed in order to establish causality and underlying molecular genetic mechanisms of pathogenesis. Here, we discuss an approach for prioritizing the contribution of genetic risk loci to complex-NND pathogenesis by estimating the possible impacts of these loci on gene regulation. Furthermore, we highlight the use of a tissue-specificity gene expression index and the application of artificial intelligence (AI) to improve the interpretation of the role of genetic risk elements in NND pathogenesis. Given that NND symptoms are associated with brain dysfunction, risk loci with direct, causative actions would comprise genes with essential functions in neural cells that are highly expressed in the brain. Indeed, NND risk genes implicated in brain dysfunction are disproportionately enriched in the brain compared with other tissues, which we refer to as brain-specific expressed genes. In addition, the tissue-specificity gene expression index can be used as a handle to identify non-brain contexts that are involved in NND pathogenesis. Lastly, we discuss how using an AI approach provides the opportunity to integrate the biological impacts of risk loci to identify those putative combinations of causative relationships through which genetic factors contribute to NND pathogenesis.

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Machine learning based disease prediction from genotype data

Katsaouni

Tashkandi

Wiese

et al. 2021

Biological Chemistry

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Using results from genome-wide association studies for understanding complex traits is a current challenge. Here we review how genotype data can be used with different machine learning (ML) methods to predict phenotype occurrence and severity from genotype data. We discuss common feature encoding schemes and how studies handle the often small number of samples compared to the huge number of variants. We compare which ML methods are being applied, including recent results using deep neural networks. Further, we review the application of methods for feature explanation and interpretation.

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DeepCOMBI: Explainable artificial intelligence for the analysis and discovery in genome-wide association studies

Cited by 3 publications

References 65 publications

Knowledge-driven approaches to improve genomic prediction in plants

Knowledge-driven approaches to improve genomic prediction in plants

Quantitative neurogenetics: applications in understanding disease

Machine learning based disease prediction from genotype data

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