Academic journal article Genetics

Powerful and Adaptive Testing for Multi-Trait and Multi-SNP Associations with GWAS and Sequencing Data

Academic journal article Genetics

Powerful and Adaptive Testing for Multi-Trait and Multi-SNP Associations with GWAS and Sequencing Data

Article excerpt

(ProQuest: ... denotes formulae omitted.)

ALZHEIMER'S disease (AD) (MIM 104300) is the most common neurodegenerative disease, and every 67 sec, someone in the United States develops AD (Alzheimer's Association 2015a). Currently there is no cure for AD, and most cases are diagnosed in the late stage of the disease. It is projected that the number of Americans of age 65 years and older with AD will increase from 5.1 million in 2015 to 13.5 million in 2050, a growth from an estimated 11% of the U.S. senior population in 2015 to 16% in 2050, costing.$1.1 trillion in 2050 (Alzheimer's Association 2015b). To advance our understanding of the initiation, progression, and etiology of AD, the Alzheimer's Disease Neuroimaging Initiative (ADNI) was started in 2004 and continues to the present, collecting extensive clinical, genomic, and multimodal imaging data (Shen et al. 2014). Many other genetic studies have been conducted, identifying multiple common and rare variants, shedding light on pathogenic mechanisms of AD (Marei et al. 2015; Saykin et al. 2015). In particular, the APOEe4 allele has been consistently shown to be associated with AD. However, only 50% of AD patients carry an APOEe4 allele, suggesting the existence of other genetic variants contributing to risk for the disease (Karch et al. 2014). A recent study indicates that 33%of total AD phenotypic variance is explained by common variants; APOE alone explains 6% and other known markers 2%, meaning .25% of phenotypic variance remains unexplained by known common variants (Ridge et al. 2013). Hence, as for other common and complex diseases and traits, many more genetic factors underlying late-onset AD are yet to be discovered. One obvious but costly approach is to have a larger sample size. Alternatively,more powerful analysismethods are urgently needed. For example, in contrast to the popular single single-nucleotide polymorphism (SNP)-based analysis, novel gene- and pathway-based analyses may be more powerful in discovering additional causal variants. As demonstrated by Jones et al. (2010), jointly analyzing functionally related SNPs sheds new light on the relatedness of immune regulation, energy metabolism, and protein degradation to the etiology of AD. The reason is due to the well-known genetic heterogeneity and small effect sizes of individual common variants, as observed from published genome-wide association study (GWAS) results (Manolio et al. 2009). To boost power in identifying aggregate effects of multiple SNPs, itmay be promising to conduct association analysis at the SNP-set (or gene) level, rather than at the individual SNP level.

Another strategy is to use multiple endophenotypes, intermediate between genetics and the disease, for their potential to have stronger associations with genetic variants. In addition to boosting power, the use of intermediate phenotypes may provide important clues about causal pathways to the disease (Maity et al. 2012; Schifano et al. 2013). A recent GWAS demonstrated the effectiveness of the strategy: some risk genes such as FRMD6 were first identified to be associated with some neuroimaging intermediate phenotypes (e.g., hippocampal atrophy) (Shen et al. 2014) and then were later validated to be associated with AD (Hong et al. 2012; Sherva et al. 2014). A possibly useful but underutilized intermediate phenotype is the brain default mode network (DMN), consisting of several brain regions of interest (ROIs) remaining active in the resting state. Brain activity in the DMN may explain the etiology of AD (Metin et al. 2015) and is a plausible indicator for incipient AD (Damoiseaux et al. 2012; Greicius et al. 2004; He et al. 2009; Jones et al. 2011; Balthazar et al. 2014). Since there is growing evidence that genetic factors play a role in aberrant default mode connectivity (Glahn et al. 2010), it may be substantially more powerful to detect genetic variants associated with the DMN, a set of multiple intermediate phenotypes, than with AD. …

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