Genome-wide strategies for detecting multiple loci that influence complex diseases

By Jonathan Marchini, Peter Donnelly, Lon R. Cardon. Nature Genetics, 2005

978 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/0841f59d0191acabf0cc28d5d69dc12640bea1ab

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Citations in the corpus

Genetic Mapping in Human Disease (2008, methodology): “Although many claims of associations were published, the statistical support tended to be weak and few were subsequently replicated ( 25 ).”
Data and Theory Point to Mainly Additive Genetic Variance for Complex Traits (2008, background): “Increasing knowledge about biological pathways and gene networks implies, however, that gene-gene interactions (epistasis) are important, and some have argued recently that much genetic variance in populations is due to such interactions [5,6,7,8].”
A tutorial on statistical methods for population association studies (2006, background): “Despite these problems, there is evidence that a direct search for epistatic effects can pay dividend”
Genomics to systems biology in animal and veterinary sciences: Progress, lessons and opportunities☆ (2014, background): “There is growing evidence that gene-gene and gene-environment interactions contribute to complex diseases and traits rather than single genes (Marchini et al., 2005; Kadarmideen et al., 2006a; Shao et al., 2008).”
SNPHarvester: a filtering-based approach for detecting epistatic interactions in genome-wide association studies (2009, methodology): “We use the three models in Marchini et al. (2005) for comparison.”
A new multipoint method for genome-wide association studies by imputation of genotypes (2007, background): “The standard paradigm for association analysis is based on detecting disease variants based on their marginal effects, but this may not be the most powerful strategy if signiﬁcant allelic heterogeneity or gene-gene interactions have a role in the genetic architecture of complex traits 26 .”
Variance component model to account for sample structure in genome-wide association studies (2010, neutral): “Epistatic loci can be incorporated by including additional interaction terms in equation (1) to model a diverse set of possible types of interaction”
A genome-wide association study identifies new psoriasis susceptibility loci and an interaction between HLA-C and ERAP1 (2010, neutral): “There are many different ways to model both the marginal effects at each SNP and their joint effec”
Collective judgment predicts disease-associated single nucleotide variants (2013, background): “Any one of these SNVs is unlikely to be significantly visible in the overarching background of human variation [9,10].”
BOOST: A fast approach to detecting gene-gene interactions in genome-wide case-control studies (2010, background): “Marchini et al.(11) highlighted the importance of testing associations allowing for interactions in a genome-wide scale and successfully demonstrated its feasibility.”
Replication of putative candidate-gene associations with rheumatoid arthritis in >4,000 samples from North America and Sweden: association of susceptibility with PTPN22, CTLA4, and PADI4. (2005, methodology): “Epistatic interactions between pairs of alleles were tested among PTPN22, CTLA4, PADI4, and HLADRB1 SE alleles, as recently advocated (Marchini et al. 2005), by use of logistic regression to determine whether genotype distributions were statistically different between cases and controls.”
A random forest approach to the detection of epistatic interactions in case-control studies (2009, methodology): “The above approach, named epi Forest (detection of epi - static interactions using random Forest), was compared with three existing methods (BEAM [17], the stepwise logistic regression [11], and the classical χ 2 test) on three simulated disease models [11].”
A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility. (2006, background): “This will be important as we move to genome-wide association studies (Moore and Ritchie, 2004; Marchini et al., 2005).”
Asymptotic optimality of the Westfall--Young permutation procedure for multiple testing under dependence (2011, background): “Most genomewide association studies, for example, aim to discover just a few locations on the genome that are associated with prevalence of a certain disease [20, 23].”
Extended Bayesian information criteria for model selection with large model spaces (2008, background): “…in genome-wide association studies, geneticists type tens or hundreds of thousands of single nucleotide polymorphisms spreading over the whole genome to identify a handful of them that are responsible for the genetic variation of a quantitative trait or a disease status; see Marchini et al. (2005).”