Abstract
Although the genome-wide association studies, which are based on common disease-common variants (CDCV) hypothesis, have great success in dissecting the genetic architecture of human diseases, their limitation of explaining the missing heritability motivated researchers to test the hypothesis that rare variants contribute to the variation of common diseases, that is, common disease/rare variant (CDRV) hypothesis. The fast developed high-throughput next generation of sequencing technologies has made the studies of rare variants practicable. Statistical approaches to test associations between a phenotype and rare variants are rapidly developing. Overall, the key idea of these methods is to test a set of rare variants in a defined region or regions by collapsing or aggregating rare variants. To improve the statistical power, several weighting strategies to the rare variants and/or adding the informative covariates in the model have been published. In this chapter, some of these methods which can use unrelated individuals and family members are introduced.
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Feng, T., Zhu, X. (2018). Rare Variant Analysis in Unrelated Individuals. In: Yao, Y. (eds) Applied Computational Genomics. Translational Bioinformatics, vol 13. Springer, Singapore. https://doi.org/10.1007/978-981-13-1071-3_4
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DOI: https://doi.org/10.1007/978-981-13-1071-3_4
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