Biology subjects

Greshake Tzovaras, B.

Publications and source records attributed to Greshake Tzovaras, B..

5 recordsLinked to original sources

bioRxiv2023

Hypothesis-free phenotype prediction within a genetics-first framework

Cohort-wide sequencing studies have revealed that the largest category of variants is those deemed rare, even for the subset located in coding regions (99% of known coding variants are seen in less than 1% of the population1-3). Our understanding of how rare genetic variants influence disease and organism-level phenotypes has achieved limited progress, partly explained by the intrinsic difficulty in statistically evaluating the biological significance of rare events. Here we show that discoveries can instead be made through a knowledge-based approach using protein domains and ontologies (function and phenotype) that considers all coding variants regardless of allele frequency. We describe an ab initio, genetics-first method making molecular knowledge-based interpretations for exome-wide non-synonymous variants for phenotypes at the organism and cellular level. By using this reverse approach, we identify plausible novel genetic causes for developmental disorders that have eluded other established methods and present novel molecular hypotheses for the causal genetics of 40 phenotypes generated from a direct-to-consumer genotype cohort. This system offers a chance to extract further discovery from genetic data after standard tools have been applied.

genomics↗

bioRxiv2020

Phenotype prediction from genome-wide genotyping data: a crowdsourcing experiment

BackgroundThe increasing statistical power of genome-wide association studies is fostering the development of precision medicine through genomic predictions of complex traits. Nevertheless, it has been shown that the results remain relatively modest. A reason might be the nature of the methods typically used to construct genomic predictions. Recent machine learning techniques have properties that could help to capture the architecture of complex traits better and improve genomic prediction accuracy. MethodsWe relied on crowd-sourcing to efficiently compare multiple genomic prediction methods. This represents an innovative approach in the genomic field because of the privacy concerns linked to human genetic data. There are two crowd-sourcing elements building our study. First, we constructed a dataset from openSNP (opensnp.org), an open repository where people voluntarily share their genotyping data and phenotypic information in an effort to participate in open science. To leverage this resource we release the openSNP Cohort Maker, a tool that builds a homogeneous and up-to-date cohort based on the data available on opensnp.org. Second, we organized an open online challenge on the CrowdAI platform (crowdai.org) aiming at predicting height from genome-wide genotyping data. ResultsThe openSNP Height Prediction challenge lasted for three months. A total of 138 challengers contributed to 1275 submissions. The winner computed a polygenic risk score using the publicly available summary statistics of the GIANT study to achieve the best result (r2 = 0.53 versus r2 = 0.49 for the second-best). ConclusionWe report here the first crowd-sourced challenge on publicly available genome-wide genotyping data. We also deliver the openSNP Cohort Maker that will allow people to make use of the data available on opensnp.org.

genomics↗

bioRxiv2019

What is in a lichen? A metagenomic approach to reconstruct the holo-genome of Umbilicaria pustulata

Lichens are valuable models in symbiosis research and promising sources of biosynthetic genes for biotechnological applications. Most lichenized fungi grow slowly, resist aposymbiotic cultivation, and are generally poor candidates for experimentation. Obtaining contiguous, high quality genomes for such symbiotic communities is technically challenging. Here we present the first assembly of a lichen holo-genome from metagenomic whole genome shotgun data comprising both PacBio long reads and Illumina short reads. The nuclear genomes of the two primary components of the lichen symbiosis - the fungus Umbilicaria pustulata (33 Mbp) and the green alga Trebouxia sp. (53 Mbp) - were assembled at contiguities comparable to single-species assemblies. The analysis of the read coverage pattern revealed a relative cellular abundance of approximately 20:1 (fungus:alga). Gap-free, circular sequences for all organellar genomes were obtained. The community of lichen-associated bacteria is dominated by Acidobacteriaceae, and the two largest bacterial contigs belong to the genus Acidobacterium. Gene set analyses showed no evidence of horizontal gene transfer from algae or bacteria into the fungal genome. Our data suggest a lineage-specific loss of a putative gibberellin-20-oxidase in the fungus, a gene fusion in the fungal mitochondrion, and a relocation of an algal chloroplast gene to the algal nucleus. Major technical obstacles during reconstruction of the holo-genome were coverage differences among individual genomes surpassing three orders of magnitude. Moreover, we show that G/C-rich inverted repeats paired with non-random sequencing error in PacBio data can result in missing gene predictions. This likely poses a general problem for genome assemblies based on long reads.

genomics↗

bioRxiv2018

Open Humans: A platform for participant-centered research and personal data exploration

BackgroundMany aspects of our lives are now digitized and connected to the internet. As a result, individuals are now creating and collecting more personal data than ever before. This offers an unprecedented chance for human-participant research ranging from the social sciences to precision medicine. With this potential wealth of data come practical problems (such as how to merge data streams from various sources), as well as ethical problems (such as how to best balance risks and benefits when enabling personal data sharing by individuals).\n\nResultsTo begin to address these problems in real time, we present Open Humans, a community-based platform that enables personal data collections across data streams, giving individuals more personal data access and control of sharing authorizations, and enabling academic research as well as patient-led projects. We showcase data streams that Open Humans combines (e.g. personal genetic data, wearable activity monitors, GPS location records and continuous glucose monitor data), along with use cases of how the data facilitates various projects.\n\nConclusionsOpen Humans highlights how a community-centric ecosystem can be used to aggregate personal data from various sources as well as how these data can be used by academic and citizen scientists through practical, iterative approaches to sharing that strive to balance considerations with participant autonomy, inclusion, and privacy.

bioinformatics↗

bioRxiv2018

Dopaminergic genes are associated with both directed and random exploration

In order to maximize long-term rewards, agents must balance exploitation (choosing the option with the highest payoff) and exploration (gathering information about options that might have higher payoffs). Although the optimal solution to this trade-off is intractable, humans make use of two effective strategies: selectively exploring options with high uncertainty (directed exploration), and increasing the randomness of their choices when they are more uncertain (random exploration). Using a task that independently manipulates these two forms of exploration, we show that single nucleotide polymorphisms related to dopamine are associated with individual differences in exploration strategies. Variation in a gene linked to prefrontal dopamine (COMT) predicted the degree of directed exploration, as well as the overall randomness of responding. Variation in a gene linked to striatal dopamine (DARPP-32) predicted the degree of both directed and random exploration. These findings suggest that dopamine makes multiple contributions to exploration, depending on its afferent target.

neuroscience↗