@article {168,
	title = {Exome sequencing reveals pathogenic mutations in 91 strains of mice with Mendelian disorders.},
	journal = {Genome Res},
	volume = {25},
	year = {2015},
	month = {2015 Jul},
	pages = {948-57},
	abstract = {<p>Spontaneously arising mouse mutations have served as the foundation for understanding gene function for more than 100 years. We have used exome sequencing in an effort to identify the causative mutations for 172 distinct, spontaneously arising mouse models of Mendelian disorders, including a broad range of clinically relevant phenotypes. To analyze the resulting data, we developed an analytics pipeline that is optimized for mouse exome data and a variation database that allows for reproducible, user-defined data mining as well as nomination of mutation candidates through knowledge-based integration of sample and variant data. Using these new tools, putative pathogenic mutations were identified for 91 (53\%) of the strains in our study. Despite the increased power offered by potentially unlimited pedigrees and controlled breeding, about half of our exome cases remained unsolved. Using a combination of manual analyses of exome alignments and whole-genome sequencing, we provide evidence that a large fraction of unsolved exome cases have underlying structural mutations. This result directly informs efforts to investigate the similar proportion of apparently Mendelian human phenotypes that are recalcitrant to exome sequencing. </p>},
	keywords = {Animals, Exome, Female, Genetic Diseases, Inborn, Genetic Linkage, Genetic Variation, Genome-Wide Association Study, Genomics, High-Throughput Nucleotide Sequencing, Male, Mice, Mutation, Phenotype, Reproducibility of Results},
	issn = {1549-5469},
	doi = {10.1101/gr.186882.114},
	author = {Fairfield, Heather and Srivastava, Anuj and Ananda, Guruprasad and Liu, Rangjiao and Kircher, Martin and Lakshminarayana, Anuradha and Harris, Belinda S and Karst, Son Yong and Dionne, Louise A and Kane, Coleen C and Curtain, Michelle and Berry, Melissa L and Ward-Bailey, Patricia F and Greenstein, Ian and Byers, Candice and Czechanski, Anne and Sharp, Jocelyn and Palmer, Kristina and Gudis, Polyxeni and Martin, Whitney and Tadenev, Abby and Bogdanik, Laurent and Pratt, C Herbert and Chang, Bo and Schroeder, David G and Cox, Gregory A and Cliften, Paul and Milbrandt, Jeffrey and Murray, Stephen and Burgess, Robert and Bergstrom, David E and Donahue, Leah Rae and Hamamy, Hanan and Masri, Amira and Santoni, Federico A and Makrythanasis, Periklis and Antonarakis, Stylianos E and Shendure, Jay and Reinholdt, Laura G}
}
@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}
}
