Balding DJ. A tutorial on statistical methods for population association studies. Nat Rev Genet 7: 781-791

Department of Epidemiology and Public Health, Imperial College, St Marys Campus, Norfolk Place, London W2 1PG, UK.
Nature Reviews Genetics (Impact Factor: 36.98). 11/2006; 7(10):781-91. DOI: 10.1038/nrg1916
Source: PubMed


Although genetic association studies have been with us for many years, even for the simplest analyses there is little consensus on the most appropriate statistical procedures. Here I give an overview of statistical approaches to population association studies, including preliminary analyses (Hardy-Weinberg equilibrium testing, inference of phase and missing data, and SNP tagging), and single-SNP and multipoint tests for association. My goal is to outline the key methods with a brief discussion of problems (population structure and multiple testing), avenues for solutions and some ongoing developments.

272 Reads
  • Source
    • "All statistical analyses were performed using STATA software, version 11.0. Testing for deviations from Hardy–Weinberg equilibrium (HWE) was carried out using a fast exact test [19], and the significance level of departure from HWE among controls was set at a = 10 À3 [20]. The PLINK software v1.07 was used for haplotypes construction and analyses. "
    [Show abstract] [Hide abstract]
    ABSTRACT: The aim of the present study is to explore the association between the APOA5 polymorphisms and haplotypes with obesity in Moroccan patients. The study was performed in 459 subjects, Obese (n=164) and non-obese (n=295). All subjects were genotyped for the APOA5 -1131T>C (rs662799) and c.56C>G (rs3135506) polymorphisms. The contribution of APOA5 polymorphisms and haplotypes in the increased risk of obesity were explored using logistic regression analyses. The -1131T>C and c.56C>G polymorphisms were significantly associated with obesity. Both polymorphisms were strongly associated with increased BMI. Analysis of constructed haplotypes showed a significant association between CG haplotype and susceptibility to obesity (OR [95%CI]=3.09 [1.93-4.97]; P<0.001). These results support a potential role for APOA5 common variants and related haplotypes as risk factors for obesity.
    Pathologie Biologie 11/2015; DOI:10.1016/j.patbio.2015.09.002 · 1.20 Impact Factor
  • Source
    • "Genetic methods like association mapping (see Balding [2] for a review), aiming at finding correlations between genetic markers and phenotypic traits in germplasm, may allow to complete the knowledge of both reblooming genetic control and rose breeding history. "
    [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, we develop a statistical methodology applied to the characterization of flowering curves using Gaussian mixture models. Our study relies on a set of rosebushes flowering data, and Gaussian mixture models are mainly used to quantify the reblooming behavior of each one. In this regard, we also suggest our own selection criterion to take into account the lack of symmetry of most of the flowering curves. Three classes are created on the basis of the reblooming indicators, and a subclassification is made using a longitudinal $k$--means algorithm which highlights the role also played by the precocity of the flowering. A principal component analysis is finally conducted on a set of indicators derived from our statistical approach to get an overview of the correlations between the features that we have decided to retain on each curve. Results suggest the lack of correlation between reblooming and flowering precocity. The pertinent indicators obtained in this study will be a first step towards the comprehension of the environmental and genetic control of these biological processes.
  • Source
    • "For example, if m 5 100,000, it is expected that about 5,000 false positive associations are observed by chance even none of SNPs is diseaserelated . Thus, multiple comparison is an important consideration in GWAS analysis, and must be handled appropriately [18] . "
    [Show abstract] [Hide abstract]
    ABSTRACT: In the past few years, genome-wide association study (GWAS) has made great successes in identifying genetic susceptibility loci underlying many complex diseases and traits. The findings provide important genetic insights into understanding pathogenesis of diseases. In this paper, we present an overview of widely used approaches and strategies for analysis of GWAS, offered a general consideration to deal with GWAS data. The issues regarding data quality control, population structure, association analysis, multiple comparison and visual presentation of GWAS results are discussed; other advanced topics including the issue of missing heritability, meta-analysis, set-based association analysis, copy number variation analysis and GWAS cohort analysis are also briefly introduced. © 2015 the Journal of Biomedical Research. All rights reserved.
    07/2015; 29(4):285-97. DOI:10.7555/JBR.29.20140007
Show more

Similar Publications

Preview (2 Sources)

272 Reads
Available from