Needles in stacks of needles: finding disease-causal variants in a wealth of genomic data

By Gregory M. Cooper, Jay Shendure. 2011

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Citations in the corpus

CADD: predicting the deleteriousness of variants throughout the human genome (2018, neutral): “However, the resolution of purely genetic strategies is limited by statistical power and other factors (2).”
A general framework for estimating the relative pathogenicity of human genetic variants (2014, background): “Second, it is well established that annotations like PolyPhen and conservation are valuable in the sequencing-based identification of disease-causal genes by virtue of their ability to highly rank pathogenic variants 1,2,38 .”
Biallelic and De Novo Variants in DONSON Reveal a Clinical Spectrum of Cell Cycle-opathies with Microcephaly, Dwarfism and Skeletal Abnormalities (2019, methodology): “Identified variants are listed in Table 2 with ExAC and gnomAD database minor allele frequencies (Lek et al., 2016), conservation score using phyloP (Cooper & Shendure, 2011) and GERP (Davydov et al., 2010), and functional prediction algorithms that include CADD (Kircher et al., 2014), SIFT (Ng &…”
Beyond GWASs: illuminating the dark road from association to function. (2013, background): “Contrary to early expectations, few GWAS-identified variants are predicted to disrupt protein-coding regions, and approaches to determining their functional consequences are reviewed elsewhere.(3) The vast majority of GWAS tag SNPs lie in intergenic or intronic regions (approximately 88%(1)) and the”
GAVIN: Gene-Aware Variant INterpretation for medical sequencing (2017, background): “Interpreting the thousands of variations observed in DNA and determining which are pathogenic and which are benign is still difficult and time-consuming, even when variants are prioritized by state-of-the-art in silico prediction tools and heuristic filters [2].”
Computational tools for prioritizing candidate genes: boosting disease gene discovery (2012, methodology): “Although there has been considerable progress in filtering variants (see the recent Review in this journal 118 ), current methods mainly focus on how variants affect sequence properties (in particular, evolutionary conservation) and protein structure, rather than being based on phenotypic…”
Rare-variant association analysis: study designs and statistical tests. (2014, background): “Reviews of this subject can be found elsewhere.(115,116) Such studies are often the logical next”
A Landscape of Driver Mutations in Melanoma (2012, methodology): “, 2010) in this study (as well as LoF mutation burden), other mutation scoring algorithms (Cooper and Shendure, 2011) may too prove useful.”
The Post-GWAS Era: From Association to Function. (2018, background): “In addition, a GWAS causal variant might alter the amino acid sequence of a protein, thereby affecting protein function rather than abundance.(53) These possibilities can usually be excluded, however, if there are no daVs in exonic regions.”
Identification of cis-suppression of human disease mutations by comparative genomics (2015, neutral): “(2) k compensatory changes fix at rate 1/h1, followed by the now-neutral CPD at an independent rate 1/h2.”
The Evaluation of Tools Used to Predict the Impact of Missense Variants Is Hindered by Two Types of Circularity (2015, background): “…LRT [Chun and Fay, 2009], PP2 [Adzhubei et al., 2010], SIFT [Ng and Henikoff, 2003], MASS [Reva et al., 2011], FatHMM-W and FatHMM-U [Shihab et al., 2013], and CADD [Kircher et al., 2014] as well as two conservation scores: phyloP [Cooper and Shendure, 2011] and GERP ++ [Davydov et al., 2010].”