@article {38,
	title = {Analysis of predicted loss-of-function variants in UK Biobank identifies variants protective for disease.},
	journal = {Nat Commun},
	volume = {9},
	year = {2018},
	month = {2018 Apr 24},
	pages = {1613},
	abstract = {<p>Less than 3\% of protein-coding genetic variants are predicted to result in loss of protein function through the introduction of a stop codon, frameshift, or the disruption of an essential splice site; however, such predicted loss-of-function (pLOF) variants provide insight into effector transcript and direction of biological effect. In >400,000 UK Biobank participants, we conduct association analyses of 3759 pLOF variants with six metabolic traits, six cardiometabolic diseases, and twelve additional diseases. We identified 18 new low-frequency or rare (allele frequency < 5\%) pLOF variant-phenotype associations. pLOF variants in the gene GPR151 protect against obesity and type 2 diabetes, in the gene IL33 against asthma and allergic disease, and in the gene IFIH1 against hypothyroidism. In the gene PDE3B, pLOF variants associate with elevated height, improved body fat distribution and protection from coronary artery disease. Our findings prioritize genes for which pharmacologic mimics of pLOF variants may lower risk for disease.</p>},
	keywords = {Databases, Genetic, Diabetes Mellitus, Type 2, Disease, Gene Frequency, Genetic Testing, Genetic Variation, Humans, Obesity, Phenotype, Proteins, Respiratory Hypersensitivity, United Kingdom},
	issn = {2041-1723},
	doi = {10.1038/s41467-018-03911-8},
	author = {Emdin, Connor A and Khera, Amit V and Chaffin, Mark and Klarin, Derek and Natarajan, Pradeep and Aragam, Krishna and Haas, Mary and Bick, Alexander and Zekavat, Seyedeh M and Nomura, Akihiro and Ardissino, Diego and Wilson, James G and Schunkert, Heribert and McPherson, Ruth and Watkins, Hugh and Elosua, Roberto and Bown, Matthew J and Samani, Nilesh J and Baber, Usman and Erdmann, Jeanette and Gupta, Namrata and Danesh, John and Chasman, Daniel and Ridker, Paul and Denny, Joshua and Bastarache, Lisa and Lichtman, Judith H and D{\textquoteright}Onofrio, Gail and Mattera, Jennifer and Spertus, John A and Sheu, Wayne H-H and Taylor, Kent D and Psaty, Bruce M and Rich, Stephen S and Post, Wendy and Rotter, Jerome I and Chen, Yii-Der Ida and Krumholz, Harlan and Saleheen, Danish and Gabriel, Stacey and Kathiresan, Sekar}
}
@article {30,
	title = {Quantifying the regulatory effect size of -acting genetic variation using allelic fold change.},
	journal = {Genome Res},
	volume = {27},
	year = {2017},
	month = {2017 Nov},
	pages = {1872-1884},
	abstract = {<p>Mapping -acting expression quantitative trait loci (-eQTL) has become a popular approach for characterizing proximal genetic regulatory variants. In this paper, we describe and characterize log allelic fold change (aFC), the magnitude of expression change associated with a given genetic variant, as a biologically interpretable unit for quantifying the effect size of -eQTLs and a mathematically convenient approach for systematic modeling of -regulation. This measure is mathematically independent from expression level and allele frequency, additive, applicable to multiallelic variants, and generalizable to multiple independent variants. We provide efficient tools and guidelines for estimating aFC from both eQTL and allelic expression data sets and apply it to Genotype Tissue Expression (GTEx) data. We show that aFC estimates independently derived from eQTL and allelic expression data are highly consistent, and identify technical and biological correlates of eQTL effect size. We generalize aFC to analyze genes with two eQTLs in GTEx and show that in nearly all cases the two eQTLs act independently in regulating gene expression. In summary, aFC is a solid measure of -regulatory effect size that allows quantitative interpretation of cellular regulatory events from population data, and it is a valuable approach for investigating novel aspects of eQTL data sets.</p>},
	keywords = {Alleles, Databases, Genetic, Gene Expression, Gene Expression Profiling, Gene Regulatory Networks, Genetic Variation, Humans, Models, Theoretical, Quantitative Trait Loci},
	issn = {1549-5469},
	doi = {10.1101/gr.216747.116},
	author = {Mohammadi, Pejman and Castel, Stephane E and Brown, Andrew A and Lappalainen, Tuuli}
}
