We propose to develop novel statistical methods and software tools for disease association testing with rare variants, with particular application to autism. Although genome-wide association studies have led to the discovery of many common variants reproducibly associated with various complex traits, these variants have small effect sizes and overall explain only a small fraction of the total estimated trait heritability. Recent advances in next-generation sequencing technologies allow for the first time an objective assessment of the importance of rare variants in complex diseases. Over the past few years it has become clear from numerous empirical studies that rare variants are an important contributor to disease risk. This is especially compelling for psychiatric diseases, such as schizophrenia and autism, where common disease susceptibility variants have been more difficult to identify. Traditional association testing strategies that have worked well for common variants have low power for the analysis of rare variants, mostly due to the large number of such variants in any genetic region and their low frequency counts in datasets of realistic sizes. Therefore development of powerful methods for rare variant analysis is greatly needed in order to efficiently extract information from the many sequencing datasets currently being generated. In this application we propose novel methods for both population- and family-based designs to identify rare genetic variants that influence risk to complex diseases, with particular application to autism. In particular, we focus on methods development in the following areas: family-based testing strategies for rare variants, unified testing strategies to efficiently combine family-base and population-based studies, and refinement strategies to identify causal rare variants once an overall association at a gene- or region-level has been established. We will implement the new methods in a comprehensive software package to be made available to the scientific community. Furthermore we will apply these methods to whole-exome data from 1000 autism cases, 1000 matched controls, and 500 autism trios. We believe the proposed research is very timely and has the potential to be of great public health importance through direct application to autism, and more broadly to other complex diseases.
Autism and other psychiatric diseases are major public health problems. The proposed statistical methodology with direct application to autism will help in the identification of genetic variants influencing autism risk, with important implications for public health.
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|Ionita-Laza, Iuliana; McCallum, Kenneth; Xu, Bin et al. (2016) A spectral approach integrating functional genomic annotations for coding and noncoding variants. Nat Genet 48:214-20|
|Song, Xiaoyu; Ionita-Laza, Iuliana; Liu, Mengling et al. (2016) A General and Robust Framework for Secondary Traits Analysis. Genetics 202:1329-43|
|McCallum, Kenneth J; Ionita-Laza, Iuliana (2015) Empirical Bayes scan statistics for detecting clusters of disease risk variants in genetic studies. Biometrics 71:1111-20|
|Ionita-Laza, Iuliana; Capanu, Marinela; De Rubeis, Silvia et al. (2014) Identification of rare causal variants in sequence-based studies: methods and applications to VPS13B, a gene involved in Cohen syndrome and autism. PLoS Genet 10:e1004729|
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|De Rubeis, Silvia; He, Xin; Goldberg, Arthur P et al. (2014) Synaptic, transcriptional and chromatin genes disrupted in autism. Nature 515:209-15|
|Swartz, Michael D; Kim, Taebeom; Niu, Jiangong et al. (2014) Small sample properties of rare variant analysis methods. BMC Proc 8:S13|
|Ionita-Laza, Iuliana; Xu, Bin; Makarov, Vlad et al. (2014) Scan statistic-based analysis of exome sequencing data identifies FAN1 at 15q13.3 as a susceptibility gene for schizophrenia and autism. Proc Natl Acad Sci U S A 111:343-8|
|Takata, Atsushi; Xu, Bin; Ionita-Laza, Iuliana et al. (2014) Loss-of-function variants in schizophrenia risk and SETD1A as a candidate susceptibility gene. Neuron 82:773-80|
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