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Article ; Online: Welch-weighted Egger regression reduces false positives due to correlated pleiotropy in Mendelian randomization.

Brown, Brielin C / Knowles, David A

2021 Volume 108, Issue 12, Page(s) 2319–2335

Abstract: Modern population-scale biobanks contain simultaneous measurements of many phenotypes, providing unprecedented opportunity to study the relationship between biomarkers and disease. However, inferring causal effects from observational data is notoriously ... ...

Abstract	Modern population-scale biobanks contain simultaneous measurements of many phenotypes, providing unprecedented opportunity to study the relationship between biomarkers and disease. However, inferring causal effects from observational data is notoriously challenging. Mendelian randomization (MR) has recently received increased attention as a class of methods for estimating causal effects using genetic associations. However, standard methods result in pervasive false positives when two traits share a heritable, unobserved common cause. This is the problem of correlated pleiotropy. Here, we introduce a flexible framework for simulating traits with a common genetic confounder that generalizes recently proposed models, as well as a simple approach we call Welch-weighted Egger regression (WWER) for estimating causal effects. We show in comprehensive simulations that our method substantially reduces false positives due to correlated pleiotropy while being fast enough to apply to hundreds of phenotypes. We apply our method first to a subset of the UK Biobank consisting of blood traits and inflammatory disease, and then to a broader set of 411 heritable phenotypes. We detect many effects with strong literature support, as well as numerous behavioral effects that appear to stem from physician advice given to people at high risk for disease. We conclude that WWER is a powerful tool for exploratory data analysis in ever-growing databases of genotypes and phenotypes.
MeSH term(s)	Computer Simulation ; False Positive Reactions ; Female ; Genetic Pleiotropy ; Humans ; Inflammation/blood ; Inflammation/genetics ; Male ; Mendelian Randomization Analysis/methods ; Mendelian Randomization Analysis/standards ; Models, Genetic ; Phenotype ; Polymorphism, Single Nucleotide ; Regression Analysis
Language	English
Publishing date	2021-12-02
Publishing country	United States
Document type	Comparative Study ; Evaluation Study ; Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	219384-x
ISSN	1537-6605 ; 0002-9297
ISSN (online)	1537-6605
ISSN	0002-9297
DOI	10.1016/j.ajhg.2021.10.006
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Article: Large-scale causal discovery using interventional data sheds light on the regulatory network architecture of blood traits.

Brown, Brielin C / Morris, John A / Lappalainen, Tuuli / Knowles, David A

bioRxiv : the preprint server for biology

2023

Abstract: Inference of directed biological networks is an important but notoriously challenging problem. We ... ...

Abstract	Inference of directed biological networks is an important but notoriously challenging problem. We introduce
Language	English
Publishing date	2023-10-17
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.10.13.562293
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Article ; Online: Using epigenomics to understand cellular responses to environmental influences in diseases.

Wattacheril, Julia J / Raj, Srilakshmi / Knowles, David A / Greally, John M

PLoS genetics

2023 Volume 19, Issue 1, Page(s) e1010567

Abstract: It is a generally accepted model that environmental influences can exert their effects, at least in part, by changing the molecular regulators of transcription that are described as epigenetic. As there is biochemical evidence that some epigenetic ... ...

Abstract	It is a generally accepted model that environmental influences can exert their effects, at least in part, by changing the molecular regulators of transcription that are described as epigenetic. As there is biochemical evidence that some epigenetic regulators of transcription can maintain their states long term and through cell division, an epigenetic model encompasses the idea of maintenance of the effect of an exposure long after it is no longer present. The evidence supporting this model is mostly from the observation of alterations of molecular regulators of transcription following exposures. With the understanding that the interpretation of these associations is more complex than originally recognised, this model may be oversimplistic; therefore, adopting novel perspectives and experimental approaches when examining how environmental exposures are linked to phenotypes may prove worthwhile. In this review, we have chosen to use the example of nonalcoholic fatty liver disease (NAFLD), a common, complex human disease with strong environmental and genetic influences. We describe how epigenomic approaches combined with emerging functional genetic and single-cell genomic techniques are poised to generate new insights into the pathogenesis of environmentally influenced human disease phenotypes exemplified by NAFLD.
MeSH term(s)	Humans ; Non-alcoholic Fatty Liver Disease/genetics ; Epigenesis, Genetic ; Epigenomics ; Environmental Exposure/adverse effects ; Phenotype
Language	English
Publishing date	2023-01-19
Publishing country	United States
Document type	Journal Article ; Review ; Research Support, N.I.H., Extramural
ZDB-ID	2186725-2
ISSN	1553-7404 ; 1553-7390
ISSN (online)	1553-7404
ISSN	1553-7390
DOI	10.1371/journal.pgen.1010567
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Article ; Online: LDmat: efficiently queryable compression of linkage disequilibrium matrices.

Weiner, Rockwell J / Lakhani, Chirag / Knowles, David A / Gürsoy, Gamze

Bioinformatics (Oxford, England)

2023 Volume 39, Issue 2

Abstract: Motivation: Linkage disequilibrium (LD) matrices derived from large populations are widely used in population genetics in fine-mapping, LD score regression, and linear mixed models for Genome-wide Association Studies (GWAS). However, these matrices can ... ...

Abstract	Motivation: Linkage disequilibrium (LD) matrices derived from large populations are widely used in population genetics in fine-mapping, LD score regression, and linear mixed models for Genome-wide Association Studies (GWAS). However, these matrices can reach large sizes when they are derived from millions of individuals; hence, moving, sharing and extracting granular information from this large amount of data can be cumbersome. Results: We sought to address the need for compressing and easily querying large LD matrices by developing LDmat. LDmat is a standalone tool to compress large LD matrices in an HDF5 file format and query these compressed matrices. It can extract submatrices corresponding to a sub-region of the genome, a list of select loci, and loci within a minor allele frequency range. LDmat can also rebuild the original file formats from the compressed files. Availability and implementation: LDmat is implemented in python, and can be installed on Unix systems with the command 'pip install ldmat'. It can also be accessed through https://github.com/G2Lab/ldmat and https://pypi.org/project/ldmat/. Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Humans ; Linkage Disequilibrium ; Software ; Genome-Wide Association Study ; Data Compression ; Genome
Language	English
Publishing date	2023-03-13
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btad092
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Article ; Online: Deep mendelian randomization: Investigating the causal knowledge of genomic deep learning models.

Malina, Stephen / Cizin, Daniel / Knowles, David A

PLoS computational biology

2022 Volume 18, Issue 10, Page(s) e1009880

Abstract: Multi-task deep learning (DL) models can accurately predict diverse genomic marks from sequence, but whether these models learn the causal relationships between genomic marks is unknown. Here, we describe Deep Mendelian Randomization (DeepMR), a method ... ...

Abstract	Multi-task deep learning (DL) models can accurately predict diverse genomic marks from sequence, but whether these models learn the causal relationships between genomic marks is unknown. Here, we describe Deep Mendelian Randomization (DeepMR), a method for estimating causal relationships between genomic marks learned by genomic DL models. By combining Mendelian randomization with in silico mutagenesis, DeepMR obtains local (locus specific) and global estimates of (an assumed) linear causal relationship between marks. In a simulation designed to test recovery of pairwise causal relations between transcription factors (TFs), DeepMR gives accurate and unbiased estimates of the 'true' global causal effect, but its coverage decays in the presence of sequence-dependent confounding. We then apply DeepMR to examine the global relationships learned by a state-of-the-art DL model, BPNet, between TFs involved in reprogramming. DeepMR's causal effect estimates validate previously hypothesized relationships between TFs and suggest new relationships for future investigation.
MeSH term(s)	Mendelian Randomization Analysis/methods ; Deep Learning ; Causality ; Research Design ; Genomics
Language	English
Publishing date	2022-10-20
Publishing country	United States
Document type	Journal Article
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1009880
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Book ; Online: System Identification for Continuous-time Linear Dynamical Systems

Halmos, Peter / Pillow, Jonathan / Knowles, David A.

2023

Abstract: The problem of system identification for the Kalman filter, relying on the expectation-maximization (EM) procedure to learn the underlying parameters of a dynamical system, has largely been studied assuming that observations are sampled at equally-spaced ...

Abstract	The problem of system identification for the Kalman filter, relying on the expectation-maximization (EM) procedure to learn the underlying parameters of a dynamical system, has largely been studied assuming that observations are sampled at equally-spaced time points. However, in many applications this is a restrictive and unrealistic assumption. This paper addresses system identification for the continuous-discrete filter, with the aim of generalizing learning for the Kalman filter by relying on a solution to a continuous-time It\^o stochastic differential equation (SDE) for the latent state and covariance dynamics. We introduce a novel two-filter, analytical form for the posterior with a Bayesian derivation, which yields analytical updates which do not require the forward-pass to be pre-computed. Using this analytical and efficient computation of the posterior, we provide an EM procedure which estimates the parameters of the SDE, naturally incorporating irregularly sampled measurements. Generalizing the learning of latent linear dynamical systems (LDS) to continuous-time may extend the use of the hybrid Kalman filter to data which is not regularly sampled or has intermittent missing values, and can extend the power of non-linear system identification methods such as switching LDS (SLDS), which rely on EM for the linear discrete-time Kalman filter as a sub-unit for learning locally linearized behavior of a non-linear system. We apply the method by learning the parameters of a latent, multivariate Fokker-Planck SDE representing a toggle-switch genetic circuit using biologically realistic parameters, and compare the efficacy of learning relative to the discrete-time Kalman filter as the step-size irregularity and spectral-radius of the dynamics-matrix increases. Comment: 31 pages, 3 figures. Only light changes and restructuring to previous version made
Keywords	Computer Science - Machine Learning ; Electrical Engineering and Systems Science - Systems and Control
Subject code	006
Publishing date	2023-08-23
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Variational Variance

Stirn, Andrew / Knowles, David A.

Simple, Reliable, Calibrated Heteroscedastic Noise Variance Parameterization

2020

Abstract: Brittle optimization has been observed to adversely impact model likelihoods for regression and VAEs when simultaneously fitting neural network mappings from a (random) variable onto the mean and variance of a dependent Gaussian variable. Previous works ... ...

Abstract	Brittle optimization has been observed to adversely impact model likelihoods for regression and VAEs when simultaneously fitting neural network mappings from a (random) variable onto the mean and variance of a dependent Gaussian variable. Previous works have bolstered optimization and improved likelihoods, but fail other basic posterior predictive checks (PPCs). Under the PPC framework, we propose critiques to test predictive mean and variance calibration and the predictive distribution's ability to generate sensible data. We find that our attractively simple solution, to treat heteroscedastic variance variationally, sufficiently regularizes variance to pass these PPCs. We consider a diverse gamut of existing and novel priors and find our methods preserve or outperform existing model likelihoods while significantly improving parameter calibration and sample quality for regression and VAEs. Comment: 17 pages, 6 figures, 10 tables
Keywords	Computer Science - Machine Learning ; Statistics - Machine Learning
Subject code	519
Publishing date	2020-06-08
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Active Learning in CNNs via Expected Improvement Maximization

Nagpal, Udai G. / Knowles, David A

2020

Abstract: Deep learning models such as Convolutional Neural Networks (CNNs) have demonstrated high levels of effectiveness in a variety of domains, including computer vision and more recently, computational biology. However, training effective models often ... ...

Abstract	Deep learning models such as Convolutional Neural Networks (CNNs) have demonstrated high levels of effectiveness in a variety of domains, including computer vision and more recently, computational biology. However, training effective models often requires assembling and/or labeling large datasets, which may be prohibitively time-consuming or costly. Pool-based active learning techniques have the potential to mitigate these issues, leveraging models trained on limited data to selectively query unlabeled data points from a pool in an attempt to expedite the learning process. Here we present "Dropout-based Expected IMprOvementS" (DEIMOS), a flexible and computationally-efficient approach to active learning that queries points that are expected to maximize the model's improvement across a representative sample of points. The proposed framework enables us to maintain a prediction covariance matrix capturing model uncertainty, and to dynamically update this matrix in order to generate diverse batches of points in the batch-mode setting. Our active learning results demonstrate that DEIMOS outperforms several existing baselines across multiple regression and classification tasks taken from computer vision and genomics.
Keywords	Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Computer Vision and Pattern Recognition ; I.2.6 ; I.5.4 ; I.4.9
Subject code	006 ; 004
Publishing date	2020-11-27
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Prediction of on-target and off-target activity of CRISPR-Cas13d guide RNAs using deep learning.

Wessels, Hans-Hermann / Stirn, Andrew / Méndez-Mancilla, Alejandro / Kim, Eric J / Hart, Sydney K / Knowles, David A / Sanjana, Neville E

Nature biotechnology

2023 Volume 42, Issue 4, Page(s) 628–637

Abstract: Transcriptome engineering applications in living cells with RNA-targeting CRISPR effectors depend on accurate prediction of on-target activity and off-target avoidance. Here we design and test ~200,000 RfxCas13d guide RNAs targeting essential genes in ... ...

Abstract	Transcriptome engineering applications in living cells with RNA-targeting CRISPR effectors depend on accurate prediction of on-target activity and off-target avoidance. Here we design and test ~200,000 RfxCas13d guide RNAs targeting essential genes in human cells with systematically designed mismatches and insertions and deletions (indels). We find that mismatches and indels have a position- and context-dependent impact on Cas13d activity, and mismatches that result in G-U wobble pairings are better tolerated than other single-base mismatches. Using this large-scale dataset, we train a convolutional neural network that we term targeted inhibition of gene expression via gRNA design (TIGER) to predict efficacy from guide sequence and context. TIGER outperforms the existing models at predicting on-target and off-target activity on our dataset and published datasets. We show that TIGER scoring combined with specific mismatches yields the first general framework to modulate transcript expression, enabling the use of RNA-targeting CRISPRs to precisely control gene dosage.
MeSH term(s)	Humans ; RNA, Guide, CRISPR-Cas Systems ; CRISPR-Cas Systems/genetics ; Clustered Regularly Interspaced Short Palindromic Repeats ; Deep Learning ; RNA ; Gene Editing
Chemical Substances	RNA, Guide, CRISPR-Cas Systems ; RNA (63231-63-0)
Language	English
Publishing date	2023-07-03
Publishing country	United States
Document type	Journal Article
ZDB-ID	1311932-1
ISSN	1546-1696 ; 1087-0156
ISSN (online)	1546-1696
ISSN	1087-0156
DOI	10.1038/s41587-023-01830-8
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Article ; Online: Computational models of dopamine release measured by fast scan cyclic voltammetry in vivo.

Shashaank, N / Somayaji, Mahalakshmi / Miotto, Mattia / Mosharov, Eugene V / Makowicz, Emily A / Knowles, David A / Ruocco, Giancarlo / Sulzer, David L

PNAS nexus

2023 Volume 2, Issue 3, Page(s) pgad044

Abstract: Dopamine neurotransmission in the striatum is central to many normal and disease functions. Ventral midbrain dopamine neurons exhibit ongoing tonic firing that produces low extrasynaptic levels of dopamine below the detection of conventional ... ...

Abstract	Dopamine neurotransmission in the striatum is central to many normal and disease functions. Ventral midbrain dopamine neurons exhibit ongoing tonic firing that produces low extrasynaptic levels of dopamine below the detection of conventional extrasynaptic cyclic voltammetry (∼10-20 nanomolar), with superimposed bursts that can saturate the dopamine uptake transporter and produce transient micromolar concentrations. The bursts are known to lead to marked presynaptic plasticity via multiple mechanisms, but analysis methods for these kinetic parameters are limited. To provide a deeper understanding of the mechanics of the modulation of dopamine neurotransmission by physiological, genetic, and pharmacological means, we present three computational models of dopamine release with different levels of spatiotemporal complexity to analyze in vivo fast-scan cyclic voltammetry recordings from the dorsal striatum of mice. The models accurately fit to cyclic voltammetry data and provide estimates of presynaptic dopamine facilitation/depression kinetics and dopamine transporter reuptake kinetics, and we used the models to analyze the role of synuclein proteins in neurotransmission. The models' results support recent findings linking the presynaptic protein α-synuclein to the short-term facilitation and long-term depression of dopamine release, as well as reveal a new role for β-synuclein and/or γ-synuclein in the long-term regulation of dopamine reuptake.
Language	English
Publishing date	2023-02-10
Publishing country	England
Document type	Journal Article
ISSN	2752-6542
ISSN (online)	2752-6542
DOI	10.1093/pnasnexus/pgad044
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