5 Methods for Association Studies
117
Huedo-Medina TB, Sanchez-Meca J, Marin-Martinez F, Botella J (2006) Assessing heterogeneity
in meta-analysis: Q statistic or I2 index? Psychol Methods 11:193–206
International HapMap Consortium (2003) The International HapMap Project. Nature 426:789–796
International HapMap Consortium (2005) A haplotype map of the human genome. Nature
437:1299–1320
Ioannidis JP (2005) Why most published research findings are false. PLoS Med 2:e124
Ioannidis JP (2006) Common genetic variants for breast cancer: 32 largely refuted candidates and
larger prospects. J Natl Cancer Inst 98:1350–1353
Ioannidis JP, Ntzani EE, Trikalinos TA, Contopoulos-Ioannidis DG (2001) Replication validity of
genetic association studies. Nat Genet 29:306–309
Ionita-Laza I, Lee S, Makarov V, Buxbaum JD, Lin X (2013) Sequence kernel association tests for
the combined effect of rare and common variants. Am J Hum Genet 92:841–853
Jorgenson E, Witte JS (2006) Coverage and power in genomewide association studies. Am J Hum
Genet 78:884–888
Karlsson Linner R, Biroli P, Kong E et al (2019) Genome-wide association analyses of risk
tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared
genetic influences. Nat Genet 51:245–257
Katan MB (1986) Apolipoprotein E isoforms, serum cholesterol, and cancer. Lancet 1:507–508
Kraft P, Wacholder S, Cornelis MC et al (2009) Beyond odds ratios—communicating disease risk
based on genetic profiles. Nat Rev Genet 10:264–269
Lander ES, Linton LM, Birren B et al (2001) Initial sequencing and analysis of the human genome.
Nature 409:860–921
Larson NB, McDonnell S, Cannon Albright L et al (2017) gsSKAT: rapid gene set analysis and
multiple testing correction for rare-variant association studies using weighted linear kernels.
Genet Epidemiol 41:297–308
Lee S, Emond MJ, Bamshad MJ et al (2012) Optimal unified approach for rare-variant association
testing with application to small-sample case-control whole-exome sequencing studies. Am J
Hum Genet 91:224–237
Lettre G, Lange C, Hirschhorn JN (2007) Genetic model testing and statistical power in populationbased association studies of quantitative traits. Genet Epidemiol 31:358–362
Li B, Leal SM (2008) Methods for detecting associations with rare variants for common diseases:
application to analysis of sequence data. Am J Hum Genet 83:311–321
Li Y, Willer C, Sanna S, Abecasis G (2009) Genotype imputation. Annu Rev Genomics Hum Genet
10:387–406
Li R, Conti DV, Diaz-Sanchez D, Gilliland F, Thomas DC (2012) Joint analysis for integrating two
related studies of different data types and different study designs using hierarchical modeling
approaches. Hum Hered 74:83–96
Li Z, Li X, Liu Y et al (2019) Dynamic scan procedure for detecting rare-variant association
regions in whole-genome sequencing studies. Am J Hum Genet 104:802–814
Lindquist KJ, Jorgenson E, Hoffmann TJ, Witte JS (2013) The impact of improved microarray
coverage and larger sample sizes on future genome-wide association studies. Genet Epidemiol
37:383–392
Liu M, Jiang Y, Wedow R et al (2019) Association studies of up to 1.2 million individuals yield
new insights into the genetic etiology of tobacco and alcohol use. Nat Genet 51:237–244
Lloyd-Jones LR, Robinson MR, Yang J, Visscher PM (2018) Transformation of summary statistics
from linear mixed model association on all-or-none traits to odds ratio. Genetics 208:1397–1408
Loh PR, Tucker G, Bulik-Sullivan BK et al (2015) Efficient Bayesian mixed-model analysis
increases association power in large cohorts. Nat Genet 47:284–290
Lohmueller KE, Pearce CL, Pike M, Lander ES, Hirschhorn JN (2003) Meta-analysis of genetic
association studies supports a contribution of common variants to susceptibility to common
disease. Nat Genet 33:177–182
Luca D, Ringquist S, Klei L et al (2008) On the use of general control samples for genome-wide
association studies: genetic matching highlights causal variants. Am J Hum Genet 82:453–463
117
Huedo-Medina TB, Sanchez-Meca J, Marin-Martinez F, Botella J (2006) Assessing heterogeneity
in meta-analysis: Q statistic or I2 index? Psychol Methods 11:193–206
International HapMap Consortium (2003) The International HapMap Project. Nature 426:789–796
International HapMap Consortium (2005) A haplotype map of the human genome. Nature
437:1299–1320
Ioannidis JP (2005) Why most published research findings are false. PLoS Med 2:e124
Ioannidis JP (2006) Common genetic variants for breast cancer: 32 largely refuted candidates and
larger prospects. J Natl Cancer Inst 98:1350–1353
Ioannidis JP, Ntzani EE, Trikalinos TA, Contopoulos-Ioannidis DG (2001) Replication validity of
genetic association studies. Nat Genet 29:306–309
Ionita-Laza I, Lee S, Makarov V, Buxbaum JD, Lin X (2013) Sequence kernel association tests for
the combined effect of rare and common variants. Am J Hum Genet 92:841–853
Jorgenson E, Witte JS (2006) Coverage and power in genomewide association studies. Am J Hum
Genet 78:884–888
Karlsson Linner R, Biroli P, Kong E et al (2019) Genome-wide association analyses of risk
tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared
genetic influences. Nat Genet 51:245–257
Katan MB (1986) Apolipoprotein E isoforms, serum cholesterol, and cancer. Lancet 1:507–508
Kraft P, Wacholder S, Cornelis MC et al (2009) Beyond odds ratios—communicating disease risk
based on genetic profiles. Nat Rev Genet 10:264–269
Lander ES, Linton LM, Birren B et al (2001) Initial sequencing and analysis of the human genome.
Nature 409:860–921
Larson NB, McDonnell S, Cannon Albright L et al (2017) gsSKAT: rapid gene set analysis and
multiple testing correction for rare-variant association studies using weighted linear kernels.
Genet Epidemiol 41:297–308
Lee S, Emond MJ, Bamshad MJ et al (2012) Optimal unified approach for rare-variant association
testing with application to small-sample case-control whole-exome sequencing studies. Am J
Hum Genet 91:224–237
Lettre G, Lange C, Hirschhorn JN (2007) Genetic model testing and statistical power in populationbased association studies of quantitative traits. Genet Epidemiol 31:358–362
Li B, Leal SM (2008) Methods for detecting associations with rare variants for common diseases:
application to analysis of sequence data. Am J Hum Genet 83:311–321
Li Y, Willer C, Sanna S, Abecasis G (2009) Genotype imputation. Annu Rev Genomics Hum Genet
10:387–406
Li R, Conti DV, Diaz-Sanchez D, Gilliland F, Thomas DC (2012) Joint analysis for integrating two
related studies of different data types and different study designs using hierarchical modeling
approaches. Hum Hered 74:83–96
Li Z, Li X, Liu Y et al (2019) Dynamic scan procedure for detecting rare-variant association
regions in whole-genome sequencing studies. Am J Hum Genet 104:802–814
Lindquist KJ, Jorgenson E, Hoffmann TJ, Witte JS (2013) The impact of improved microarray
coverage and larger sample sizes on future genome-wide association studies. Genet Epidemiol
37:383–392
Liu M, Jiang Y, Wedow R et al (2019) Association studies of up to 1.2 million individuals yield
new insights into the genetic etiology of tobacco and alcohol use. Nat Genet 51:237–244
Lloyd-Jones LR, Robinson MR, Yang J, Visscher PM (2018) Transformation of summary statistics
from linear mixed model association on all-or-none traits to odds ratio. Genetics 208:1397–1408
Loh PR, Tucker G, Bulik-Sullivan BK et al (2015) Efficient Bayesian mixed-model analysis
increases association power in large cohorts. Nat Genet 47:284–290
Lohmueller KE, Pearce CL, Pike M, Lander ES, Hirschhorn JN (2003) Meta-analysis of genetic
association studies supports a contribution of common variants to susceptibility to common
disease. Nat Genet 33:177–182
Luca D, Ringquist S, Klei L et al (2008) On the use of general control samples for genome-wide
association studies: genetic matching highlights causal variants. Am J Hum Genet 82:453–463