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Available from: Ian G Mills, Aug 09, 2015
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    • "A variety of methods for incorporating genomic context, functional , or pathway annotation data have been discussed in GWAS contexts (reviewed by Cantor et al., 2010; Thompson et al., 2013). Examples of prior information might include loci previously reported, pathway or genomic annotation, expression QTL or other functional assays, etc. (Rebbeck et al., 2004; Bush et al., 2009; Karchin, 2009; Nicolae et al., 2010; Wang et al., 2010; Freedman et al., 2011; San Lucas et al., 2012; Minelli et al., 2013). "
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    ABSTRACT: The cost of next-generation sequencing is now approaching that of early GWAS panels, but is still out of reach for large epidemiologic studies and the millions of rare variants expected poses challenges for distinguishing causal from non-causal variants. We review two types of designs for sequencing studies: two-phase designs for targeted follow-up of genomewide association studies using unrelated individuals; and family-based designs exploiting co-segregation for prioritizing variants and genes. Two-phase designs subsample subjects for sequencing from a larger case-control study jointly on the basis of their disease and carrier status; the discovered variants are then tested for association in the parent study. The analysis combines the full sequence data from the substudy with the more limited SNP data from the main study. We discuss various methods for selecting this subset of variants and describe the expected yield of true positive associations in the context of an on-going study of second breast cancers following radiotherapy. While the sharing of variants within families means that family-based designs are less efficient for discovery than sequencing unrelated individuals, the ability to exploit co-segregation of variants with disease within families helps distinguish causal from non-causal ones. Furthermore, by enriching for family history, the yield of causal variants can be improved and use of identity-by-descent information improves imputation of genotypes for other family members. We compare the relative efficiency of these designs with those using unrelated individuals for discovering and prioritizing variants or genes for testing association in larger studies. While associations can be tested with single variants, power is low for rare ones. Recent generalizations of burden or kernel tests for gene-level associations to family-based data are appealing. These approaches are illustrated in the context of a family-based study of colorectal cancer.
    Frontiers in Genetics 12/2013; 4:276. DOI:10.3389/fgene.2013.00276
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    • "In most cases, this is a call to action for a research scientist to experimentally further follow up on the findings. Freedman et al. [144] had suggested an extensive number of functional assays necessary to validate and explain variation-to-cancer relationships that can be identified via GWAS. A similar effort needs to be undertaken for all suspect variation identified via any genome-to-phenome mapping pipeline; that is, variome-to-disease mappings need to be followed up by functional assays to elucidate pathogenicity pathways. "
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    ABSTRACT: Reduced costs and increased speed and accuracy of sequencing can bring the genome-based evaluation of individual disease risk to the bedside. While past efforts have identified a number of actionable mutations, the bulk of genetic risk remains hidden in sequence data. The biggest challenge facing genomic medicine today is the development of new techniques to predict the specifics of a given human phenome (set of all expressed phenotypes) encoded by each individual variome (full set of genome variants) in the context of the given environment. Numerous tools exist for the computational identification of the functional effects of a single variant. However, the pipelines taking advantage of full genomic, exomic, trascriptomic (and other) sequences have only recently become a reality. This review looks at the building of methodologies for predicting "variome"-defined disease risk. It also discusses some of the challenges for incorporating such a pipeline into everyday medical practice.
    Journal of Molecular Biology 08/2013; 431(21). DOI:10.1016/j.jmb.2013.07.038 · 4.33 Impact Factor
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    • "GWAS-identified SNPs may point to functional variants but are unlikely themselves to be the causative variants, since there will often be several variants in strong linkage disequilibrium (LD) that show more or less equivalent evidence of association for any given signal of association. Extensive sequencing of the identified region followed by a well-designed finemapping study in multiple populations may be helpful to narrow an association signal to potentially causative variants [15] . Furthermore, much additional work is needed to determine the functional basis for the observed associations. "
    05/2013; 27(3):167-169. DOI:10.7555/JBR.27.20130040
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