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Abstract:
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Over the past few years, substantial effort has been put into the functional annotation of variation in human genome sequence. Such annotations can play a critical role in identifying putatively causal variants among the abundant natural variation that occurs at a locus of interest. The main challenges in using these various annotations include their large numbers, and their diversity. I will discuss an unsupervised approach to derive an integrative score of these diverse annotations. I will show that the resulting meta-score has good discriminatory ability using disease associated and putatively benign variants from published studies (for both Mendelian and complex diseases), and is more strongly associated with the disease association status of such variants compared with the recently proposed CADD score. Furthermore, I will show how the meta-score is particularly useful in prioritizing likely causal variants in a region of interest when it is combined with sequencing data in the framework of a hierarchical model.
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