5 Methods for Association Studies
119
Pierce BL, Burgess S (2013) Efficient design for Mendelian randomization studies: subsample and
2-sample instrumental variable estimators. Am J Epidemiol 178:1177–1184
Pirinen M, Donnelly P, Spencer CC (2012) Including known covariates can reduce power to detect
genetic effects in case-control studies. Nat Genet 44:848–851
Piro RM, Di Cunto F (2012) Computational approaches to disease-gene prediction: rationale,
classification and successes: computational disease-gene prediction. FEBS J 279:678–696
Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D (2006) Principal
components analysis corrects for stratification in genome-wide association studies. Nat Genet
38:904–909
Price AL, Zaitlen NA, Reich D, Patterson N (2010) New approaches to population stratification in
genome-wide association studies. Nat Rev Genet 11:459–463
Pritchard JK, Rosenberg NA (1999) Use of unlinked genetic markers to detect population
stratification in association studies. Am J Hum Genet 65:220–228
Purcell S, Neale B, Todd-Brown K et al (2007) PLINK: a tool set for whole-genome association
and population-based linkage analyses. Am J Hum Genet 81:559–575
Rava M, Ahmed I, Demenais F, Sanchez M, Tubert-Bitter P, Nadif R (2013) Selection of genes for
gene-environment interaction studies: a candidate pathway-based strategy using asthma as an
example. Environ Health 12:56
Risch N, Merikangas K (1996) The future of genetic studies of complex human diseases. Science
273:1516–1517
Ritchie MD, Hahn LW, Roodi N et al (2001) Multifactor-dimensionality reduction reveals highorder interactions among estrogen-metabolism genes in sporadic breast cancer. Am J Hum
Genet 69:138–147
Ritchie MD, Hahn LW, Moore JH (2003) Power of multifactor dimensionality reduction for
detecting gene-gene interactions in the presence of genotyping error, missing data, phenocopy,
and genetic heterogeneity. Genet Epidemiol 24:150–157
Roeder K, Wasserman L (2009) Genome-wide significance levels and weighted hypothesis testing.
Stat Sci 24:398–413
Roeder K, Bacanu SA, Wasserman L, Devlin B (2006) Using linkage genome scans to improve
power of association in genome scans. Am J Hum Genet 78:243–252
Roshan U, Chikkagoudar S, Wei Z, Wang K, Hakonarson H (2011) Ranking causal variants
and associated regions in genome-wide association studies by the support vector machine and
random forest. Nucleic Acids Res 39:e62–e62
Santorico SA, Hendricks AE (2016) Progress in methods for rare variant association. BMC Genet
17(Suppl 2):6
Servin B, Stephens M (2007) Imputation-based analysis of association studies: candidate regions
and quantitative traits. PLoS Genet 3:e114
Sinnott JA, Kraft P (2012) Artifact due to differential error when cases and controls are imputed
from different platforms. Hum Genet 131:111–119
Skol AD, Scott LJ, Abecasis GR, Boehnke M (2006) Joint analysis is more efficient than
replication-based analysis for two-stage genome-wide association studies. Nat Genet 38:209–
213
Skol AD, Scott LJ, Abecasis GR, Boehnke M (2007) Optimal designs for two-stage genome-wide
association studies. Genet Epidemiol 31:776–788
Smith GD, Ebrahim S (2003) ‘Mendelian randomization’: can genetic epidemiology contribute to
understanding environmental determinants of disease? Int J Epidemiol 32:1–22
Stephens M, Balding DJ (2009) Bayesian statistical methods for genetic association studies. Nat
Rev Genet 10:681–690
Sun R, Hui S, Bader GD, Lin X, Kraft P (2019) Powerful gene set analysis in GWAS with the
generalized Berk-Jones statistic. PLoS Genet 15:e1007530
Teer JK, Mullikin JC (2010) Exome sequencing: the sweet spot before whole genomes. Hum Mol
Genet 19:R145–R151
Thomas D (2010) Methods for investigating gene-environment interactions in candidate pathway
and genome-wide association studies. Annu Rev Public Health 31:21–36
119
Pierce BL, Burgess S (2013) Efficient design for Mendelian randomization studies: subsample and
2-sample instrumental variable estimators. Am J Epidemiol 178:1177–1184
Pirinen M, Donnelly P, Spencer CC (2012) Including known covariates can reduce power to detect
genetic effects in case-control studies. Nat Genet 44:848–851
Piro RM, Di Cunto F (2012) Computational approaches to disease-gene prediction: rationale,
classification and successes: computational disease-gene prediction. FEBS J 279:678–696
Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D (2006) Principal
components analysis corrects for stratification in genome-wide association studies. Nat Genet
38:904–909
Price AL, Zaitlen NA, Reich D, Patterson N (2010) New approaches to population stratification in
genome-wide association studies. Nat Rev Genet 11:459–463
Pritchard JK, Rosenberg NA (1999) Use of unlinked genetic markers to detect population
stratification in association studies. Am J Hum Genet 65:220–228
Purcell S, Neale B, Todd-Brown K et al (2007) PLINK: a tool set for whole-genome association
and population-based linkage analyses. Am J Hum Genet 81:559–575
Rava M, Ahmed I, Demenais F, Sanchez M, Tubert-Bitter P, Nadif R (2013) Selection of genes for
gene-environment interaction studies: a candidate pathway-based strategy using asthma as an
example. Environ Health 12:56
Risch N, Merikangas K (1996) The future of genetic studies of complex human diseases. Science
273:1516–1517
Ritchie MD, Hahn LW, Roodi N et al (2001) Multifactor-dimensionality reduction reveals highorder interactions among estrogen-metabolism genes in sporadic breast cancer. Am J Hum
Genet 69:138–147
Ritchie MD, Hahn LW, Moore JH (2003) Power of multifactor dimensionality reduction for
detecting gene-gene interactions in the presence of genotyping error, missing data, phenocopy,
and genetic heterogeneity. Genet Epidemiol 24:150–157
Roeder K, Wasserman L (2009) Genome-wide significance levels and weighted hypothesis testing.
Stat Sci 24:398–413
Roeder K, Bacanu SA, Wasserman L, Devlin B (2006) Using linkage genome scans to improve
power of association in genome scans. Am J Hum Genet 78:243–252
Roshan U, Chikkagoudar S, Wei Z, Wang K, Hakonarson H (2011) Ranking causal variants
and associated regions in genome-wide association studies by the support vector machine and
random forest. Nucleic Acids Res 39:e62–e62
Santorico SA, Hendricks AE (2016) Progress in methods for rare variant association. BMC Genet
17(Suppl 2):6
Servin B, Stephens M (2007) Imputation-based analysis of association studies: candidate regions
and quantitative traits. PLoS Genet 3:e114
Sinnott JA, Kraft P (2012) Artifact due to differential error when cases and controls are imputed
from different platforms. Hum Genet 131:111–119
Skol AD, Scott LJ, Abecasis GR, Boehnke M (2006) Joint analysis is more efficient than
replication-based analysis for two-stage genome-wide association studies. Nat Genet 38:209–
213
Skol AD, Scott LJ, Abecasis GR, Boehnke M (2007) Optimal designs for two-stage genome-wide
association studies. Genet Epidemiol 31:776–788
Smith GD, Ebrahim S (2003) ‘Mendelian randomization’: can genetic epidemiology contribute to
understanding environmental determinants of disease? Int J Epidemiol 32:1–22
Stephens M, Balding DJ (2009) Bayesian statistical methods for genetic association studies. Nat
Rev Genet 10:681–690
Sun R, Hui S, Bader GD, Lin X, Kraft P (2019) Powerful gene set analysis in GWAS with the
generalized Berk-Jones statistic. PLoS Genet 15:e1007530
Teer JK, Mullikin JC (2010) Exome sequencing: the sweet spot before whole genomes. Hum Mol
Genet 19:R145–R151
Thomas D (2010) Methods for investigating gene-environment interactions in candidate pathway
and genome-wide association studies. Annu Rev Public Health 31:21–36