@article {266,
	title = {A general framework for estimating the relative pathogenicity of human genetic variants.},
	journal = {Nat Genet},
	volume = {46},
	year = {2014},
	month = {2014 Mar},
	pages = {310-5},
	abstract = {<p>Current methods for annotating and interpreting human genetic variation tend to exploit a single information type (for example, conservation) and/or are restricted in scope (for example, to missense changes). Here we describe Combined Annotation-Dependent Depletion (CADD), a method for objectively integrating many diverse annotations into a single measure (C score) for each variant. We implement CADD as a support vector machine trained to differentiate 14.7 million high-frequency human-derived alleles from 14.7 million simulated variants. We precompute C scores for all 8.6 billion possible human single-nucleotide variants and enable scoring of short insertions-deletions. C scores correlate with allelic diversity, annotations of functionality, pathogenicity, disease severity, experimentally measured regulatory effects and complex trait associations, and they highly rank known pathogenic variants within individual genomes. The ability of CADD to prioritize functional, deleterious and pathogenic variants across many functional categories, effect sizes and genetic architectures is unmatched by any current single-annotation method. </p>},
	keywords = {Genetic Variation, Genome, Human, Genome-Wide Association Study, Humans, INDEL Mutation, Models, Genetic, Molecular Sequence Annotation, Polymorphism, Single Nucleotide, Selection, Genetic, Support Vector Machine},
	issn = {1546-1718},
	doi = {10.1038/ng.2892},
	author = {Kircher, Martin and Witten, Daniela M and Jain, Preti and O{\textquoteright}Roak, Brian J and Cooper, Gregory M and Shendure, Jay}
}
@article {279,
	title = {Rare-variant extensions of the transmission disequilibrium test: application to autism exome sequence data.},
	journal = {Am J Hum Genet},
	volume = {94},
	year = {2014},
	month = {2014 Jan 02},
	pages = {33-46},
	abstract = {<p>Many population-based rare-variant (RV) association tests, which aggregate variants across a region, have been developed to analyze sequence data. A drawback of analyzing population-based data is that it is difficult to adequately control for population substructure and admixture, and spurious associations can occur. For RVs, this problem can be substantial, because the spectrum of rare variation can differ greatly between populations. A solution is to analyze parent-child trio data, by using the transmission disequilibrium test (TDT), which is robust to population substructure and admixture. We extended the TDT to test for RV associations using four commonly used methods. We demonstrate that for all RV-TDT methods, using proper analysis strategies, type I error is well-controlled even when there are high levels of population substructure or admixture. For trio data, unlike for population-based data, RV allele-counting association methods will lead to inflated type I errors. However type I errors can be properly controlled by obtaining p values empirically through haplotype permutation. The power of the RV-TDT methods was evaluated and compared to the analysis of case-control data with a number of genetic and disease models. The RV-TDT was also used to analyze exome data from 199 Simons Simplex Collection autism trios and an association was observed with variants in ABCA7. Given the problem of adequately controlling for population substructure and admixture in RV association studies and the growing number of sequence-based trio studies, the RV-TDT is extremely beneficial to elucidate the involvement of RVs in the etiology of complex traits. </p>},
	keywords = {Alleles, Autistic Disorder, Computer Simulation, Exome, Gene Frequency, Genetic Association Studies, Genetic Predisposition to Disease, Genetic Variation, Haplotypes, Humans, Linkage Disequilibrium, Models, Genetic, Phenotype, Sequence Analysis, DNA},
	issn = {1537-6605},
	doi = {10.1016/j.ajhg.2013.11.021},
	author = {He, Zongxiao and O{\textquoteright}Roak, Brian J and Smith, Joshua D and Wang, Gao and Hooker, Stanley and Santos-Cortez, Regie Lyn P and Li, Biao and Kan, Mengyuan and Krumm, Nik and Nickerson, Deborah A and Shendure, Jay and Eichler, Evan E and Leal, Suzanne M}
}
