Biology subjects

Bochud, M.

Publications and source records attributed to Bochud, M..

3 recordsLinked to original sources

bioRxiv2022

Omics-informed CNV calls reduce false positive rate and improve power for CNV-trait associations

Copy number variations (CNV) are believed to play an important role in a wide range of complex traits but discovering such associations remains challenging. Whilst whole genome sequencing (WGS) is the gold standard approach for CNV detection, there are several orders of magnitude more samples with available genotyping microarray data. Such array data can be exploited for CNV detection using dedicated software (e.g., PennCNV), however these calls suffer from elevated false positive and negative rates. In this study, we developed a CNV quality score that weights PennCNV calls (pCNV) based on their likelihood of being true positive. First, we established a measure of pCNV reliability by leveraging evidence from multiple omics data (WGS, transcriptomics and methylomics) obtained from the same samples. Next, we built a predictor of omics-confirmed pCNVs, termed omics-informed quality score (OQS), using only PennCNV software output parameters. Promisingly, OQS assigned to pCNVs detected in close family members was up to 35% higher than the OQS of pCNVs not carried by other relatives (P < 3.0-10-90), outperforming other scores. Finally, in an association study of four anthropometric traits in 89,516 Estonian Biobank samples, the use of OQS led to a relative increase in the trait variance explained by CNVs of up to 34% compared to raw pCNVs or previous quality scores. Overall, we put forward a flexible framework to improve any CNV detection method leveraging multi-omics evidence, applied it to improve PennCNV calls and demonstrated its utility by improving the statistical power for downstream association analyses.

bioinformatics↗

bioRxiv2020

Visualization and normalization of drift effect across batches in metabolome-wide association studies

As a powerful phenotyping technology, metabolomics provides new opportunities in biomarker discovery through metabolome-wide association studies (MWAS) and identification of metabolites having regulatory effect in various biological processes. While MS-based metabolomics assays are endowed with high-throughput and sensitivity, large-scale MWAS are doomed to long-term data acquisition generating an overtime-analytical signal drift that can hinder the uncovering of true biologically relevant changes. We developed "dbnorm", a package in R environment, which allows visualization and removal of signal heterogeneity from large metabolomics datasets. "dbnorm" integrates advanced statistical tools to inspect dataset structure, at both macroscopic (sample batch) and microscopic (metabolic features) scales. To compare model performance on data correction, "dbnorm" assigns a score, which allows the straightforward identification of the best fitting model for each dataset. Herein, we show how "dbnorm" efficiently removes signal drift among batches to capture the true biological heterogeneity of data in two large-scale metabolomics studies.

bioinformatics↗

bioRxiv2019

Dietary intake and diet quality of Swiss adult survivors of childhood cancer compared to the general population

BackgroundChildhood cancer survivors (CCSs) are at increased risk of developing chronic health conditions, which may be reduced by following a healthy lifestyle including a good diet. ObjectivesThis study compared the dietary intake and quality of CCSs and the general population. DesignAs part of the Swiss Childhood Cancer Survivor Study, we sent a food frequency questionnaire (FFQ) to CCSs who had a median age of 34 years (IQR: 29-40 years) and a median of 26 years (20-31 years) postdiagnosis at the time of survey. We compared dietary intake and quality of CCSs and three comparison groups representing the general adult population using FFQ and 24h recall data (24HDR). We evaluated whether mean individual intake met national dietary recommendations and used the Alternative Healthy Eating Index (AHEI) to estimate diet quality. ResultsThe 774 CCSs in our study were compared to 8964 participants in the Bus Sante study, 1276 participants in the CoLaus study, and 1134 participants in the Swiss National Nutrition Survey. Dietary intake was equally poor in CCSs and the general Swiss population. CCSs consumed inadequate amounts of vitamin D, fiber, carbohydrates, iron, vitamin A, and calcium (12%, 41%, 72%, 72%, 79%, and 89% of the recommended intakes, respectively), and excessive amounts of saturated fat, protein, cholesterol, and total fat (137%, 126%, 114%, and 107% of the recommended intakes). The mean AHEI score in CCSs was low at 48.0 (men: 45.0, women: 50.9) out of a maximum score of 100. The general population, assessed by 24HDR, scored lower overall than CCSs (41.5; men: 38.7, women: 43.8). Clinical characteristics were not associated with diet quality in CCSs. ConclusionLong-term CCSs and the general adult population have similarly poor dietary intake and quality in Switzerland, which suggests population-based interventions for everyone.

epidemiology↗