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Vlaming, P.

Publications and source records attributed to Vlaming, P..

2 recordsLinked to original sources

The role of the gut microbiota in patients with Kleefstra syndrome

Kleefstra Syndrome (KS) is a rare monogenetic syndrome, caused by haploinsufficiency of the EHMT1 gene, an important regulator of neurodevelopment. The clinical features of KS include intellectual disability, autistic behavior and gastrointestinal problems. The gut microbiota may constitute a, yet unexplored, mechanism underlying clinical variation, as they are an important modifier of the gut-brain-axis. To test whether variation in the gut microbiota is part of KS, we investigated the gut microbiota composition of 23 individuals with KS (patients) and 40 of their family members. Both alpha and beta diversity of patients were different from their family members. Genus Coprococcus 3 was lower in abundance in patients compared to family members. Moreover, abundance of genus Merdibacter was lower in patients versus family members, but only in the participants reporting intestinal complaints. Within the patient group, behavioral problems explained 7% variance in the beta diversity. Also, within this group, we detected higher levels of Coprococcus 3 and Atopobiaceae - uncultured associated with higher symptoms severity. Our results show significant differences in the gut microbiota composition of patients with KS compared to their family members, suggesting that these differences are part of the KS phenotype.

microbiology↗

Quantitative Genetic Scoring, or how to put a number on an arbitrary genetic region

MotivationWith the increasing availability of genome-wide genetic data, methods to combine genetic variables with other sources of data in statistical models are required. This paper introduces quantitative genetic scoring (QGS), a dimensionality reduction method to create quantitative genetic variables representing arbitrary genetic regions. MethodsQGS is defined as the sum of absolute differences in the genetic sequence between a subject and a reference population. QGS properties such as distribution and sensitivity to region size were examined, and QGS was tested in six different existing genomic data sets of various sizes and various phenotypes. ResultsQGS can reduce genetic information by >98% yet explain phenotypic variance at low, medium, and high level of granularity. Associations based on QGS are independent of both size and linkage disequilibrium structure of the underlying region. In combination with stability selection, QGS finds significant results where a traditional genome-wide association approaches struggle. In conclusion, QGS preserves phenotypically significant genetic variance while reducing dimensionality, allowing researchers to include quantitative genetic information in any type of statistical analysis. Availabilityhttps://github.com/machine2learn/QGS Contactgido.schoenmacker@radboudumc.nl Supplemental informationSupplemental data are available online.

bioinformatics↗