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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

Developing the "next generation" of genetic association databases for complex diseases.

Christina M Lill¹, Lars Bertram

¹Neurospsychiatric Genetics Group, Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics, Berlin, Germany.

|July 4, 2012

Summary

Genetic association studies are complex. This work presents online databases for neuropsychiatric disorders, integrating genome-wide association studies (GWAS) to improve understanding of common human conditions.

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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

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Published on: August 21, 2016

Area of Science:

Genetics
Bioinformatics
Neuroscience

Background:

Thousands of genetic association studies, including genome-wide association studies (GWAS), have been published, creating a complex data landscape.
Interpreting the vast amount of genetic data for common complex diseases is challenging.
Next-generation sequencing technologies will further increase data complexity.

Purpose of the Study:

To present online genetic association databases for neuropsychiatric disorders.
To demonstrate a model for concatenating genetic data within publicly available field synopses.
To improve the interpretation of genetic influences on common human conditions.

Main Methods:

Development of online genetic association databases.
Systematic inclusion of data from large-scale genotyping projects, such as GWAS.
Ensuring privacy of data contributors.

Main Results:

Examples of online genetic association databases for neuropsychiatric disorders are presented.
The database model integrates data from large-scale genotyping projects like GWAS.
The approach respects data contributor privacy.

Conclusions:

The developed database approach offers a viable model for managing and interpreting complex genetic data.
This model can be applied to other research fields.
Improved understanding of genetic factors in common human diseases is facilitated.