A tutorial on statistical methods for population association studies

By David J. Balding. Nature reviews genetics, 2006

1,493 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/4f86ac6903c26df7b3a03f11da0c715b87fb1f4f

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In Genetics

Citations in the corpus

STrengthening the REporting of Genetic Association Studies (STREGA)— An Extension of the STROBE Statement (2009, neutral): “Study design (case-family control studies) and statistical methods [84] have been proposed to address population stratification, but so far few studies have used these suggestions [49].”
An Examination of Polygenic Score Risk Prediction in Individuals With First-Episode Psychosis. (2017, background): “SNPs are genetic markers in LD with the unrecognized causal variants (38).”
Improved Differentially Private Analysis of Variance (2019, background): “Hypothesis testing is a common tool in population association studies, where the goal is to identify whether genetic variation is associated with disease risk [1].”
Rapid transcriptome characterization for a nonmodel organism using 454 pyrosequencing (2008, methodology): “The ability to accomplish transcriptome-wide SNP discovery using 454 sequence data is an important development because gene-associated SNPs are difficult and expensive to obtain by other methods but provide valuable data and markers for population biology, functional genomics, and conservation (Rose”
Genome-wide genetic data on ~500,000 UK Biobank participants (2017, methodology): “Accounting for the ancestral background of participants is an essential component of analysis of the UK Biobank resource, both for epidemiological studies [33], and genetic analyses, such as GWAS [27, 34].”
Genotype Imputation (2009, methodology): “Since multimarker association analyses are much more convenient in the absence of missing genotype data (5), we used the program PHASE (97, 98) and an early version of our MACH software (59) to fill in missing genotypes in our sample.”
Progress and Promise of Genome-Wide Association Studies for Human Complex Trait Genetics (2011, methodology): “(For recent reviews of GWAS methods, see Cardon and Bell 2001; Balding 2006; Hardy and Singleton 2009; Smith et al. 2009; for a recent example of a GWAS for rheumatoid arthritis risk, refer to box, case study of a GWAS: meta-analysis of six genome-wide association studies identifies seven new…”
FaST linear mixed models for genome-wide association studies (2011, methodology): “The problem of confounding by population structure, family structure, and cryptic relatedness in genome-wide association studies (GWAS) is widely appreciated [1, 2, 3, 4, 5, 6, 7].”
Whole genome association study of brain-wide imaging phenotypes for identifying quantitative trait loci in MCI and AD: A study of the ADNI cohort (2010, background): “Genome-wide association studies (GWAS) are increasingly performed (Balding, 2006; Hirschhorn and Daly, 2005; Purcell et al., 2007; Zondervan and Cardon, 2007), but effectively relating high throughput SNP data to large scale image data remains a challenging task.”
Stacks: an analysis tool set for population genomics (2013, background): “…Mapping (Barchi et al. 2011, 2012; Chutimanitsakun et al. 2011; Pfender et al. 2011; Houston et al. 2012; King et al. 2012), and a promising avenue for the use of GBS studies may be genome-wide association studies (GWAS) in natural populations (Rosenberg et al. 2010; Balding 2006; Luo et al. 2011).”
A general framework for updating belief distributions (2013, methodology): “As is standard practice, e.g. Balding (2006), we initially investigate the evidence of genetic association by testing each of the 15,608 markers in turn using a univariate model with loss, , for each of the j = 1 , . . . 15 , 608 genetic makers.”
Genome-wide association scan of tag SNPs identifies a susceptibility locus for lung cancer at 15q25.1 (2008, methodology): “We also carried out association analysis using the Armitage-Doll trend tes”
Naïve Bayesian Classifier and Genetic Risk Score for Genetic Risk Prediction of a Categorical Trait: Not so Different after all! (2012, methodology): “The coefficients of the logistic score are typically estimated by maximum likelihood (McCullagh and Nelder, 1989), or Bayesian methods using large sample approximations or Gibbs sampling (Balding, 2006).”