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Table 3 Relevant bibliography and software used by authors of accepted papers in the Population-Based Association group

From: Above and beyond state-of-the-art approaches to investigate sequence data: summary of methods and results from the population-based association group at the Genetic Analysis Workshop 19

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