@article {55,
	title = {ACAT: A Fast and Powerful p Value Combination Method for Rare-Variant Analysis in Sequencing Studies.},
	journal = {Am J Hum Genet},
	volume = {104},
	year = {2019},
	month = {2019 Mar 07},
	pages = {410-421},
	abstract = {<p>Set-based analysis that jointly tests the association of variants in a group has emerged as a popular tool for analyzing rare and low-frequency variants in sequencing studies. The existing set-based tests can suffer significant power loss when only a small proportion of variants are causal, and their powers can be sensitive to the number, effect sizes, and effect directions of the causal variants and the choices of weights. Here we propose an aggregated Cauchy association test (ACAT), a general, powerful, and computationally efficient p value combination method for boosting power in sequencing studies. First, by combining variant-level p values, we use ACAT to construct a set-based test (ACAT-V) that is particularly powerful in the presence of only a small number of causal variants in a variant set. Second, by combining different variant-set-level p values, we use ACAT to construct an omnibus test (ACAT-O) that combines the strength of multiple complimentary set-based tests, including the burden test, sequence kernel association test (SKAT), and ACAT-V. Through analysis of extensively simulated data and the whole-genome sequencing data from the Atherosclerosis Risk in Communities (ARIC) study, we demonstrate that ACAT-V complements the SKAT and the burden test, and that ACAT-O has a substantially more robust and higher power than those of the alternative tests.</p>},
	issn = {1537-6605},
	doi = {10.1016/j.ajhg.2019.01.002},
	author = {Liu, Yaowu and Chen, Sixing and Li, Zilin and Morrison, Alanna C and Boerwinkle, Eric and Lin, Xihong}
}
@article {7,
	title = {Practical Approaches for Whole-Genome Sequence Analysis of Heart- and Blood-Related Traits.},
	journal = {Am J Hum Genet},
	volume = {100},
	year = {2017},
	month = {2017 Feb 02},
	pages = {205-215},
	abstract = {<p>Whole-genome sequencing (WGS) allows for a comprehensive view of the sequence of the human genome. We present and apply integrated methodologic steps for interrogating WGS data to characterize the genetic architecture of 10 heart- and blood-related traits in a sample of 1,860 African Americans. In order to evaluate the contribution of regulatory and non-protein coding regions of the genome, we conducted aggregate tests of rare variation across the entire genomic landscape using a sliding window, complemented by an annotation-based assessment of the genome using predefined regulatory elements and within the first intron of all genes. These tests were performed treating all variants equally as well as with individual variants weighted by a measure of predicted functional consequence. Significant findings were assessed in 1,705 individuals of European ancestry. After these steps, we identified and replicated components of the genomic landscape significantly associated with heart- and blood-related traits. For two traits, lipoprotein(a) levels and neutrophil count, aggregate tests of low-frequency and rare variation were significantly associated across multiple motifs. For a third trait, cardiac troponin T, investigation of regulatory domains identified a locus on chromosome 9. These practical approaches for WGS analysis led to the identification of informative genomic regions and also showed that defined non-coding regions, such as first introns of genes and regulatory domains, are associated with important risk factor phenotypes. This study illustrates the tractable nature of WGS data and outlines an approach for characterizing the genetic architecture of complex traits.</p>},
	keywords = {Black or African American, C-Reactive Protein, Cholesterol, HDL, Cholesterol, LDL, Chromosomes, Human, Pair 9, Gene Frequency, Genome, Human, Genome-Wide Association Study, Genomics, Hemoglobins, Humans, Introns, Leukocyte Count, Lipoprotein(a), Magnesium, Natriuretic Peptide, Brain, Neutrophils, Peptide Fragments, Phosphorus, Platelet Count, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Troponin T, White People},
	issn = {1537-6605},
	doi = {10.1016/j.ajhg.2016.12.009},
	author = {Morrison, Alanna C and Huang, Zhuoyi and Yu, Bing and Metcalf, Ginger and Liu, Xiaoming and Ballantyne, Christie and Coresh, Josef and Yu, Fuli and Muzny, Donna and Feofanova, Elena and Rustagi, Navin and Gibbs, Richard and Boerwinkle, Eric}
}
