Topic | Bibliography | Software |
---|---|---|
New methods for new data types | Satten GA, Johnson HR, Allen AS et al. Testing association without calling genotypes allows for systematic differences in read depth between cases and controls. In: Abstracts from the 22nd Annual Meeting of the International Genetic Epidemiology Society, Chicago IL, USA. ISBN: 978-1-940377-00-1, 2012, 9. Original proposal to use the proportion of calls for the minor allele instead of called genotypes Karazsia BT and Dulmen MHM: Regression models for count data: illustrations using longitudinal predictors of childhood injury. Journal of Pediatric Psychology 2008;33:1076–1084. Intuitive examples of widely used models for count data | R-packages stats and pscl to fit negative binomial/linear and zero-inflated/Hurdle-negative regression models, respectively |
Handling rare variants | Conomos MP, Miller MB, and Thornton TA. Robust inference of population structure for ancestry prediction and correction of stratification in the presence of relatedness. Genetic Epidemiology 2015;39(4):276–293. Reviews the complications of population structure and kinship estimation Kang HM, Sul JH, Service SK, et al. Variance component model to account for sample structure in genome-wide association studies. Nature Genetics 2010;42:348–354. Description of EMMAX, an association testing tool for dependent observations | PC-AiR is implemented in R and is available from http://www.bioconductor.org/packages/devel/bioc/html/GENESIS.html |
SNPRelate is an R package, available from http://www.bioconductor.org/packages/release/bioc/html/SNPRelate.html. PC-AiR and SNPRelate were used for kinship estimation. | ||
EMMAX for genome wide association testing is available from http://genetics.cs.ucla.edu/emmax/ | ||
RFMiX for local ancestry mapping is available from https://sites.google.com/site/rfmixlocalancestryinference/ | ||
R-package pmlr to conduct penalized logistic regression likelihood ratio tests (http://cran.r-project.org/web/packages/pmlr) | ||
SKAT to perform single-variant score tests, and 3 variant-collapsing tests: burden, nonburden sequence kernel association test, and optimal unified test (http://cran.r-project.org/web/packages/SKAT/) | ||
Blossoc to estimate phylogenetic trees | ||
Maples BK, Gravel S, Kenny EE, and Bustamante CD. RFMix: a discriminative modeling approach for rapid and robust local-ancestry inference. American Journal of Human Genetics 2013;93:278–288. Description of RFMix, which can be used for local ancestry mapping | R packages ape and geiger to manipulate phylogenetic trees | |
Bull SB, Mak C, and Greenwood CMT: A modified score function estimator for multinomial logistic regression in small samples. Computational Statistics & Data Analysis 2002;39:57–64 | ||
Firth D. Bias reduction of maximum likelihood estimates. Biometrika 1993;80:27–38 | ||
Lee S, Emond MJ, Bamshad MJ, et al. Optimal unified approach for rare-variant association testing with application to small-sample case–control whole-exome sequencing studies. American Journal of Human Genetics 2012;91:224–237 | ||
Thompson K, Kubatko L. Using ancestral information to detect and localize quantitative trait loci in genome-wide association studies. BMC Bioinformatics 2013;14:200 | ||
Mailund T, Besenbacher S, and Schierup MH: Whole genome association mapping by incompatibilities and local phylogenies. BMC Bioinformatics 2006;7:454 | ||
Rare variant behavior | Tabangin ME, Woo JG, and Martin LJ. The effect of minor allele frequency on the likelihood of obtaining false positives. BMC Proceedings 2003;3 Suppl 7:S41 | MMAP to fit linear mixed model in a family-based sample, estimate either model-based or robust standard errors, and conduct a 1 df test of gene–environment interactions in an “interaction model” using the estimates gene–environment interaction term |
METAL to estimate 1 df and 2 df tests of gene–environment interactions using a model with a gene–environment interaction term (“interaction model”) | ||
Goh L and Yap VB. Effects of normalization on quantitative traits in association test. BMC Bioinformatics 2009;10:415. | R-package EasyStrata to estimate 1 df and 2 df tests of gene–environment interactions by comparing the genetic effects across environmental strata (“med-diff” approach) | |
Manning AK, LaValley M, Liu CT, et al. Meta-analysis of gene-environment interaction: joint estimation of SNP and SNP × environment regression coefficients. Genetic Epidemiology 2011;35:11–8. Application of 1° of freedom (df) and 2 df tests of gene–environment interactions using a model with a gene–environment interaction term | ||
Randall JC, Winkler TW, Kutalik Z, et al. Sex-stratified genome-wide association studies including 270,000 individuals show sexual dimorphism in genetic loci for anthropometric traits. PLoS Genetics 2013;9:e1003500. Application of a 1 df test of gene–environment interactions by comparing the genetic effects across environmental strata | ||
Aschard H, Hancock DB, London SJ, and Kraft P. Genome-wide meta-analysis of joint tests for genetic and gene-environment interaction effects. Human Heredity 2010;70:292–300. Application of a joint 2 df test of gene–environment interactions and genetic main effects by comparing the genetic effects across environmental strata | ||
Follow up of association signals | Wang C, Parmigiani G, and Dominici F. Bayesian effect estimation accounting for adjustment uncertainty. Biometrics 2012;68:661–671 | Codes that implement Bayesian adjustment for confounding are available at http://sweb.uky.edu/~cwa236/ |
Wang C, Dominici F, Parmigiani G, Zigler CM. Accounting for uncertainty in confounder and effect modifier selection when estimating average causal effects in generalized linear models. Biometrics 2015, in press. | R-packages hapassoc, haplo.stats, LBL to implement the haplotype association methods | |
These two papers proposed the Bayesian adjustment for confounding (BAC) method | ||
Biswas S and Lin S: Logistic Bayesian LASSO for identifying association with rare haplotypes and application to age-related macular degeneration. Biometrics 2012;68:587–597 | ||
Biswas S, Xia S, and Lin S: Detecting rare haplotype-environment interaction with logistic Bayesian LASSO. Genetic Epidemiology 2014;38:31-41 |