@article {892,
	title = {Efficient phasing and imputation of low-coverage sequencing data using large reference panels.},
	journal = {Nat Genet},
	volume = {53},
	year = {2021},
	month = {2021 01},
	pages = {120-126},
	abstract = {<p>Low-coverage whole-genome sequencing followed by imputation has been proposed as a cost-effective genotyping approach for disease and population genetics studies. However, its competitiveness against SNP arrays is undermined because current imputation methods are computationally expensive and unable to leverage large reference panels. Here, we describe a method, GLIMPSE, for phasing and imputation of low-coverage sequencing datasets from modern reference panels. We demonstrate its remarkable performance across different coverages and human populations. GLIMPSE achieves imputation of a genome for less than US$1 in computational cost, considerably outperforming other methods and improving imputation accuracy over the full allele frequency range. As a proof of concept, we show that 1{\texttimes} coverage enables effective gene expression association studies and outperforms dense SNP arrays in rare variant burden tests. Overall, this study illustrates the promising potential of low-coverage imputation and suggests a paradigm shift in the design of future genomic studies.</p>},
	keywords = {Genome, Human, Genotype, Humans, Likelihood Functions, Polymorphism, Single Nucleotide, Reference Standards, Sequence Analysis, DNA},
	issn = {1546-1718},
	doi = {10.1038/s41588-020-00756-0},
	author = {Rubinacci, Simone and Ribeiro, Diogo M and Hofmeister, Robin J and Delaneau, Olivier}
}
@article {47,
	title = {Family-Based Rare Variant Association Analysis: A Fast and Efficient Method of Multivariate Phenotype Association Analysis.},
	journal = {Genet Epidemiol},
	volume = {40},
	year = {2016},
	month = {2016 Sep},
	pages = {502-11},
	abstract = {<p>Family-based designs have been repeatedly shown to be powerful in detecting the significant rare variants associated with human diseases. Furthermore, human diseases are often defined by the outcomes of multiple phenotypes, and thus we expect multivariate family-based analyses may be very efficient in detecting associations with rare variants. However, few statistical methods implementing this strategy have been developed for family-based designs. In this report, we describe one such implementation: the multivariate family-based rare variant association tool (mFARVAT). mFARVAT is a quasi-likelihood-based score test for rare variant association analysis with multiple phenotypes, and tests both homogeneous and heterogeneous effects of each variant on multiple phenotypes. Simulation results show that the proposed method is generally robust and efficient for various disease models, and we identify some promising candidate genes associated with chronic obstructive pulmonary disease. The software of mFARVAT is freely available at http://healthstat.snu.ac.kr/software/mfarvat/, implemented in C++ and supported on Linux and MS Windows.</p>},
	keywords = {Computer Simulation, Genetic Association Studies, Genetic Variation, Humans, Likelihood Functions, Models, Genetic, Phenotype},
	issn = {1098-2272},
	doi = {10.1002/gepi.21985},
	author = {Wang, Longfei and Lee, Sungyoung and Gim, Jungsoo and Qiao, Dandi and Cho, Michael and Elston, Robert C and Silverman, Edwin K and Won, Sungho}
}
