Genetic analysis of structural birth defects and neurodevelopmental disorders
In this study, we aim to advance our understanding of the genetic causes of birth defects and neurodevelopmental disorders. We can achieve this goal by improving the ability to identify real difference of genetic variations between patients (cases) and the general population (controls) using data sets as large as possible. UK Biobank have generated genetic variation data of half a million human subjects. Excluding the subjects with known birth defects or neurodevelopmental disorders, it will be the largest control data set available for in-depth genetic analysis.
Recent large-scale sequencing studies of autism and congenital heart disease (CHD) suggest that carrying genetic mutations or variations in one of the 500-1000 risk genes can cause these early onset diseases. However, the scientific community have established robust association with each condition in only about 100 genes. In other words, we do not know the identify of the vast majority of risk genes. As a result, we have incomplete understanding of the biological mechanisms of the diseases, limiting the ability to diagnose the genetic causes in individual patients. It is imperative to identify new risk genes to improve our understanding and the utility of genetic testing.
A critical bottleneck in identification of new risk genes is the lack of large number of controls with genetic data, as all of the autism and CHD studies only include modest number of controls. In this study, we propose to use UK Biobank data as controls to improve the ability of new risk gene identification. We have access to large genetic data sets of autism or congenital heart disease cases through close collaborations with consortia, including SPARK for autism and Pediatric Cardiac Genomics Consortium. From the proposed study, we expect to identify new risk genes of autism and congenital heart disease with various degree of statistical confidence. The high-confidence genes will be further examined by research community to be included in clinical genetic testing and improve the yield in genetic diagnosis. Both high- and medium- confidence genes will lead to improve our understanding of the biological mechanisms of the diseases.