Biology subjects

Zietz, M.

Publications and source records attributed to Zietz, M..

7 recordsLinked to original sources

bioRxiv2024

Three Open Questions in Polygenic Score Portability

A major obstacle hindering the broad adoption of polygenic scores (PGS) is their lack of "portability" to people that differ--in genetic ancestry or other characteristics--from the GWAS samples in which genetic effects were estimated. Here, we use the UK Biobank to measure the change in PGS prediction accuracy as a continuous function of individuals genome-wide genetic dissimilarity to the GWAS sample ("genetic distance"). Our results highlight three gaps in our understanding of PGS portability. First, prediction accuracy is extremely noisy at the individual level and not well predicted by genetic distance. In fact, variance in prediction accuracy is explained comparably well by socioeconomic measures. Second, trends of portability vary across traits. For several immunity-related traits, prediction accuracy drops near zero quickly even at intermediate levels of genetic distance. This quick drop may reflect GWAS associations being more ancestry-specific in immunity-related traits than in other traits. Third, we show that even qualitative trends of portability can de pend on the measure of prediction accuracy used. For instance, for type 2 diabetes, precision stays roughly constant, while recall surprisingly increases, with genetic distance. Together, our results show that portability cannot be understood through global ancestry groupings alone. There are other, understudied factors influencing portability, such as the specifics of the evolution of the trait and its genetic architecture, social context, and the construction of the polygenic score. Addressing these gaps can aid in the development and application of PGS and inform more equitable genomic research.

genetics↗

bioRxiv2024

Optimized phenotype definitions boost GWAS power

AO_SCPLOWBSTRACTC_SCPLOWComplex diseases are among the central challenges facing the world, and genetics underlie a large fraction of the risk. Observational data, such as electronic health records (EHR), offer numerous advantages in the study of complex disease genetics. These include their large scale, cost-effectiveness, information on many different conditions, and future scalability with the widespread adoption of EHRs. Observational data, however, are challenging for research as they reflect various factors including the healthcare process and access to care, as well as broader societal effects like systemic biases. Here, we introduce MaxGCP, a novel phenotyping method designed to purify the genetic signal in observational data. Our approach optimizes a phenotype definition to maximize its coheritability with the complex trait of interest. We validated the method in simulations and applied it to real data analyses of stroke and Alzheimers disease. We found that MaxGCP improves genomewide association study (GWAS) power compared to conventional, single-code phenotype definitions. MaxGCP is a powerful tool for genetic discovery in observational data, and we anticipate that it will be broadly useful for studying complex diseases using observational data.

bioinformatics↗

bioRxiv2023

Phenotype projections accelerate biobank-scale GWAS

Understanding the genetic basis of complex disease is a critical research goal due to the immense, worldwide burden of these diseases. Pan-biobank genome-wide association studies (GWAS) provide a powerful resource in complex disease genetics, generating shareable summary statistics on thousands of phenotypes. Biobank-scale GWAS have two notable limitations: they are resource-intensive to compute and do not inform about hand-crafted phenotype definitions, which are often more relevant to study. Here we present Indirect GWAS, a summary-statistic-based method that addresses these limitations. Indirect GWAS computes GWAS statistics for any phenotype defined as a linear combination of other phenotypes. Our method can reduce runtime by an order of magnitude for large pan-biobank GWAS, and it enables ultra-rapid (roughly one minute) GWAS on hand-crafted phenotype definitions using only summary statistics. Overall, this method advances complex disease research by facilitating more accessible and cost-effective genetic studies using large observational data.

genetics↗

bioRxiv2023

Hetnet connectivity search provides rapid insights into how two biomedical entities are related

Hetnets, short for "heterogeneous networks", contain multiple node and relationship types and offer a way to encode biomedical knowledge. One such example, Hetionet connects 11 types of nodes -- including genes, diseases, drugs, pathways, and anatomical structures -- with over 2 million edges of 24 types. Previous work has demonstrated that supervised machine learning methods applied to such networks can identify drug repurposing opportunities. However, a training set of known relationships does not exist for many types of node pairs, even when it would be useful to examine how nodes of those types are meaningfully connected. For example, users may be curious not only how metformin is related to breast cancer, but also how the GJA1 gene might be involved in insomnia. We developed a new procedure, termed hetnet connectivity search, that proposes important paths between any two nodes without requiring a supervised gold standard. The algorithm behind connectivity search identifies types of paths that occur more frequently than would be expected by chance (based on node degree alone). We find that predictions are broadly similar to those from previously described supervised approaches for certain node type pairs. Scoring of individual paths is based on the most specific paths of a given type. Several optimizations were required to precompute significant instances of node connectivity at the scale of large knowledge graphs. We implemented the method on Hetionet and provide an online interface at https://het.io/search. We provide an open source implementation of these methods in our new Python package named hetmatpy.

bioinformatics↗

bioRxiv2023

The probability of edge existence due to node degree: a baseline for network-based predictions

Important tasks in biomedical discovery such as predicting gene functions, gene-disease associations, and drug repurposing opportunities are often framed as network edge prediction. The number of edges connecting to a node, termed degree, can vary greatly across nodes in real biomedical networks, and the distribution of degrees varies between networks. If degree strongly influences edge prediction, then imbalance or bias in the distribution of degrees could lead to nonspecific or misleading predictions. We introduce a network permutation framework to quantify the effects of node degree on edge prediction. Our framework decomposes performance into the proportions attributable to degree and the networks specific connections. We discover that performance attributable to factors other than degree is often only a small portion of overall performance. Degrees predictive performance diminishes when the networks used for training and testing--despite measuring the same biological relationships--were generated using distinct techniques and hence have large differences in degree distribution. We introduce the permutation-derived edge prior as the probability that an edge exists based only on degree. The edge prior shows excellent discrimination and calibration for 20 biomedical networks (16 bipartite, 3 undirected, 1 directed), with AUROCs frequently exceeding 0.85. Researchers seeking to predict new or missing edges in biological networks should use the edge prior as a baseline to identify the fraction of performance that is nonspecific because of degree. We released our methods as an open-source Python package (https://github.com/hetio/xswap/).

bioinformatics↗

bioRxiv2019

Sequential compression across latent space dimensions enhances gene expression signatures

BackgroundUnsupervised compression algorithms applied to gene expression data extract latent, or hidden, signals representing technical and biological sources of variation. However, these algorithms require a user to select a biologically-appropriate latent dimensionality. In practice, most researchers select a single algorithm and latent dimensionality. We sought to determine the extent by which using multiple dimensionalities across ensemble compression models improves biological representations.\n\nResultsWe compressed gene expression data from three large datasets consisting of adult normal tissue, adult cancer tissue, and pediatric cancer tissue. We compressed these data into many latent dimensionalities ranging from 2 to 200. We observed various tradeoffs across latent dimensionalities and compression models. For example, we observed high model stability between principal components analysis (PCA), independent components analysis (ICA), and non-negative matrix factorization (NMF). We identified more unique biological signatures in ensembles of denoising autoencoder (DAE) and variational autoencoder (VAE) models in intermediate latent dimensionalities. However, we captured the most pathway-associated features using all compressed features across algorithms and dimensionalities. Optimized at different latent dimensionalities, compression models detect generalizable gene expression signatures representing sex, neuroblastoma MYCN amplification, and cell types. In two supervised machine learning tasks, compressed features optimized predictions at different latent dimensionalities.\n\nConclusionsThere is no single best latent dimensionality or compression algorithm for analyzing gene expression data. Instead, using feature ensembles from different compression models across latent space dimensionalities optimizes biological representations.

bioinformatics↗

bioRxiv2018

Phosphorylation of the exocyst subunit Exo70B2 contributes to the regulation of its function

The exocyst is a conserved hetero-octameric complex that mediates early tethering of post-Golgi vesicles during exocytosis. Its Exo70 subunit functions as a spatiotemporal regulator by mediating numerous interactions with proteins and lipids. However, a molecular understanding of the exocyst functions remains challenging. Exo70B2 localized to dynamic foci at the plasma membrane and transited through Brefeldin A (BFA)-sensitive compartments, indicating that it participates in conventional secretion. Conversely, treatment with the immunogenic peptide flg22 or the salicylic acid (SA) defence hormone analogue Benzothiadiazole (BTH), induced Exo70B2 transport into the vacuole where it colocalized with autophagic markers AUTOPHAGY-RELATED PROTEIN 8 (ATG8) and NEIGHBOR OF BRCA1 GENE 1 (NBR1). According with its role in immunity, we discovered that Exo70B2 interacts with and is phosphorylated by the MITOGEN-ACTIVATED PROTEIN KINASE 3 (MPK3). Mimicking phosphorylation inhibited Exo70B2 localization at sites of active secretion. By contrast, lines expressing phosphonull variants displayed higher Effector-Triggered Immunity and were hypersensitive to BTH, conditions known to induce the secretory pathway. Our results suggest a molecular mechanism by which phosphorylation of Exo70B2 regulates interaction with the plasma membrane, and couples the secretory pathway with cellular signalling.

cell biology↗