Cited by 287Genetic Associations and EpidemiologyGenetic and phenotypic traits in livestockGenetic Mapping and Diversity in Plants and Animals

The DOI matches its Crossref record; 38 of the 40 references it cites were found in a registry or an index.

Adjusting for Heritable Covariates Can Bias Effect Estimates in Genome-Wide Association Studies

Open access

Aschard, Hugues, Vilhjálmsson, Bjarni J., Joshi, Amit D., Price, Alkes L., Kraft, Peter·2015·The American Journal of Human Genetics

DOI 10.1016/j.ajhg.2014.12.021PMC PMC4320269

Open this paper in CoChat — free account Open PDF

Abstract

In recent years, a number of large-scale genome-wide association studies have been published for human traits adjusted for other correlated traits with a genetic basis. In most studies, the motivation for such an adjustment is to discover genetic variants associated with the primary outcome independently of the correlated trait. In this report, we contend that this objective is fulfilled when the tested variants have no effect on the covariate or when the correlation between the covariate and the outcome is fully explained by a direct effect of the covariate on the outcome. For all other scenarios, an unintended bias is introduced with respect to the primary outcome as a result of the adjustment, and this bias might lead to false positives. Here, we illustrate this point by providing examples from published genome-wide association studies, including large meta-analysis of waist-to-hip ratio and waist circumference adjusted for body mass index (BMI), where genetic effects might be biased as a result of adjustment for body mass index. Using both theory and simulations, we explore this phenomenon in detail and discuss the ramifications for future genome-wide association studies of correlated traits and diseases.

Integrity signals

Registry

Crossref · 10.1016/j.ajhg.2014.12.021

Cites

38 of 38 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Principal components analysis corrects for stratification in genome-wide association studies
Price, A. L., Patterson, N. J., Plenge, R. M., Weinblatt, M. E., Shadick, N. A., & Reich, D. (2006) · Nature Genetics · doi:10.1038/ng1847 · Cited by 10,248
Self-citation
An integrated map of genetic variation from 1,092 human genomes
The 1000 Genomes Project Consortium (2012) · Nature · doi:10.1038/nature11632 · Cited by 7,711
Causal Diagrams for Epidemiologic Research
Greenland, S., Pearl, J., & Robins, J. M. (1999) · Epidemiology · doi:10.1097/00001648-199901000-00008 · Cited by 3,728
Air Pollution and Hospital Admissions for Heart Disease in Eight U.S. Counties
Schwartz, J. D. (1999) · Epidemiology · doi:10.1097/00001648-199901000-00005 · Cited by 3,045
Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs
Cross-Disorder Group of the Psychiatric Genomics Consortium (2013) · Nature Genetics · doi:10.1038/ng.2711 · Cited by 2,263
Overadjustment bias and unnecessary adjustment in epidemiologic studies.
Schisterman, E. F., Cole, S. R., & Platt, R. W. (2009) · Epidemiology · doi:10.1097/ede.0b013e3181a819a1 · Cited by 2,096 · cited 1×
“Here, classical epidemiologists advise against adjustment for C, since C is in the causal pathway and hence not a confounder.(12,13) In contrast, instances can be found where genetic epidemiologists seek the controlled direct causal effect of G on Y by noting their intent to identify variants associated with the outco…”
Background
Maternal Pesticide Exposure from Multiple Sources and Selected Congenital Anomalies
Shaw, G. M., Wasserman, C. R., O’Malley, C. D., Nelson, V. E., & Jackson, R. J. (1999) · Epidemiology · doi:10.1097/00001648-199901000-00011 · Cited by 1,783
Causal knowledge as a prerequisite for confounding evaluation: an application to birth defects epidemiology
Hernan, M. A. (2002) · American Journal of Epidemiology · doi:10.1093/aje/155.2.176 · Cited by 1,445
Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity
Thorleifsson, G., Walters, G. B., Gudbjartsson, D. F., Steinthorsdottir, V., Sulem, P., Helgadottir, A., Styrkarsdottir, U., Gretarsdottir, S., Thorlacius, S., Jonsdottir, I., et al. (2009) · Nature Genetics · doi:10.1038/ng.274 · Cited by 1,375
Understanding mechanisms underlying human gene expression variation with RNA sequencing
Pickrell, J. K., Marioni, J. C., Pai, A. A., Degner, J. F., Engelhardt, B. E., Nkadori, E., Veyrieras, J.-B., Stephens, M., Gilad, Y., & Pritchard, J. K. (2010) · Nature · doi:10.1038/nature08872 · Cited by 1,256

Show all 40 references

Sorted by citations

An atlas of genetic correlations across human diseases and traits
ReproGen Consortium, Bulik-Sullivan, B., Psychiatric Genomics Consortium, Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Case Control Consortium 3, Finucane, H. K., Anttila, V., Gusev, A., Day, F. R., Loh, P.-R., Duncan, L., et al. (2015) · Nature Genetics · doi:10.1038/ng.3406 · Cited by 4,255
Self-citation
Causal associations between risk factors and common diseases inferred from GWAS summary data
Zhu, Z., Zheng, Z., Zhang, F., Wu, Y., Trzaskowski, M., Maier, R., Robinson, M. R., McGrath, J. J., Visscher, P. M., Wray, N. R., et al. (2018) · Nature Communications · doi:10.1038/s41467-017-02317-2 · Cited by 951
The genetic architecture of the human cerebral cortex
Grasby, K. L., Jahanshad, N., Painter, J. N., Colodro-Conde, L., Bralten, J., Hibar, D. P., Lind, P. A., Pizzagalli, F., Ching, C. R. K., McMahon, M. A. B., et al. (2020) · Science · doi:10.1126/science.aay6690 · Cited by 876
Commentary: Two-sample Mendelian randomization: opportunities and challenges
Lawlor, D. A. (2016) · International Journal of Epidemiology · doi:10.1093/ije/dyw127 · Cited by 810
The trans-ancestral genomic architecture of glycemic traits
Chen, J., Spracklen, C. N., Marenne, G., Varshney, A., Corbin, L. J., Luan, J., Willems, S. M., Wu, Y., Zhang, X., Horikoshi, M., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00852-9 · Cited by 776
Mendelian randomization in cardiometabolic disease: challenges in evaluating causality
Holmes, M. V., Ala-Korpela, M., & Smith, G. D. (2017) · Nature Reviews Cardiology · doi:10.1038/nrcardio.2017.78 · Cited by 679
Sex-dependent dominance at a single locus maintains variation in age at maturity in salmon
Barson, N. J., Aykanat, T., Hindar, K., Baranski, M., Bolstad, G. H., Fiske, P., Jacq, C., Jensen, A. J., Johnston, S. E., Karlsson, S., et al. (2015) · Nature · doi:10.1038/nature16062 · Cited by 663
Integrative genomic analysis implicates limited peripheral adipose storage capacity in the pathogenesis of human insulin resistance
Lotta, L. A., Gulati, P., Day, F. R., Payne, F., Ongen, H., van de Bunt, M., Gaulton, K. J., Eicher, J. D., Sharp, S. J., Luan, J., et al. (2016) · Nature Genetics · doi:10.1038/ng.3714 · Cited by 599
Contrasting genetic architectures of schizophrenia and other complex diseases using fast variance-components analysis
Schizophrenia Working Group of the Psychiatric Genomics Consortium, Loh, P.-R., Bhatia, G., Gusev, A., Finucane, H. K., Bulik-Sullivan, B. K., Pollack, S. J., de Candia, T. R., Lee, S. H., Wray, N. R., et al. (2015) · Nature Genetics · doi:10.1038/ng.3431 · Cited by 449
Self-citation
Genome-wide associations for birth weight and correlations with adult disease
Horikoshi, M., Beaumont, R. N., Day, F. R., Warrington, N. M., Kooijman, M. N., Fernandez-Tajes, J. J., Feenstra, B., van Zuydam, N. R., Gaulton, K. J., Grarup, N., et al. (2016) · Nature · doi:10.1038/nature19806 · Cited by 508
Bi-ancestral depression GWAS in the Million Veteran Program and meta-analysis in >1.2 million individuals highlight new therapeutic directions
Levey, D. F., Stein, M. B., Wendt, F. R., Pathak, G. A., Zhou, H., Aslan, M., Quaden, R., Harrington, K. M., Nuñez, Y. Z., Overstreet, C., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00860-2 · Cited by 422
Genome Analyses of >200,000 Individuals Identify 58 Loci for Chronic Inflammation and Highlight Pathways that Link Inflammation and Complex Disorders
Ligthart, S., Vaez, A., Võsa, U., Stathopoulou, M. G., de Vries, P. S., Prins, B. P., van der Most, P. J., Tanaka, T., Naderi, E., Rose, L. M., et al. (2018) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2018.09.009 · Cited by 421
Genome-wide association study of habitual physical activity in over 377,000 UK Biobank participants identifies multiple variants including CADM2 and APOE
Klimentidis, Y. C., Raichlen, D. A., Bea, J., Garcia, D. O., Wineinger, N. E., Mandarino, L. J., Alexander, G. E., Chen, Z., & Going, S. B. (2018) · International Journal of Obesity · doi:10.1038/s41366-018-0120-3 · Cited by 403
Distinguishing genetic correlation from causation across 52 diseases and complex traits
O’Connor, L. J., & Price, A. L. (2018) · Nature Genetics · doi:10.1038/s41588-018-0255-0 · Cited by 394
Self-citation
Causal Associations of Adiposity and Body Fat Distribution With Coronary Heart Disease, Stroke Subtypes, and Type 2 Diabetes Mellitus: A Mendelian Randomization Analysis
Dale, C. E., Fatemifar, G., Palmer, T. M., White, J., Prieto-Merino, D., Zabaneh, D., Engmann, J. E. L., Shah, T., Wong, A., Warren, H. R., et al. (2017) · Circulation · doi:10.1161/circulationaha.116.026560 · Cited by 345
Shared genetic and experimental links between obesity-related traits and asthma subtypes in UK Biobank
Zhu, Z., Guo, Y., Shi, H., Liu, C.-L., Panganiban, R. A. M., Chung, W., O’Connor, L. J., Himes, B. E., Gazal, S., Hasegawa, K., et al. (2019) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2019.09.035 · Cited by 355
Local Genetic Correlation Gives Insights into the Shared Genetic Architecture of Complex Traits
Shi, H., Mancuso, N., Spendlove, S. J., & Paşaniuc, B. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.09.022 · Cited by 351
Principles and methods for transferring polygenic risk scores across global populations
Kachuri, L., Chatterjee, N., Hirbo, J. B., Schaid, D. J., Martin, I. K., Kullo, I. J., Kenny, E. E., Paşaniuc, B., Auer, P. L., Conomos, M. P., et al. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00637-2 · Cited by 366
Association of Genetic Variants Related to Gluteofemoral vs Abdominal Fat Distribution With Type 2 Diabetes, Coronary Disease, and Cardiovascular Risk Factors
Lotta, L. A., Wittemans, L. B. L., Zuber, V., Stewart, I. D., Sharp, S. J., Luan, J., Day, F. R., Li, C., Bowker, N., Cai, L., et al. (2018) · JAMA · doi:10.1001/jama.2018.19329 · Cited by 229
Locus for severity implicates CNS resilience in progression of multiple sclerosis
International Multiple Sclerosis Genetics Consortium, Harroud, A., Stridh, P., McCauley, J. L., Saarela, J., van den Bosch, A. M. R., Engelenburg, H. J., Beecham, A. H., Alfredsson, L., Alikhani, K., et al. (2023) · Nature · doi:10.1038/s41586-023-06250-x · Cited by 243
Genetic association of major depression with atypical features and obesity-related immunometabolic dysregulations
Milaneschi, Y., Lamers, F., Peyrot, W. J., Baune, B. T., Breen, G., Dehghan, A., Forstner, A. J., Grabe, H. J., Homuth, G., Kan, C., et al. (2017) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2017.3016 · Cited by 233
Genome-wide association study of MRI markers of cerebral small vessel disease in 42,310 participants
Persyn, E., Hanscombe, K. B., Howson, J. M. M., Lewis, C. M., Traylor, M., & Markus, H. S. (2020) · Nature Communications · doi:10.1038/s41467-020-15932-3 · Cited by 208
Genome-wide meta-analysis of 241,258 adults accounting for smoking behaviour identifies novel loci for obesity traits
Justice, A. E., Winkler, T. W., Feitosa, M. F., Graff, M., Fisher, V. A., Young, K. L., Barata, L., Deng, X., Czajkowski, J., Hadley, D., et al. (2017) · Nature Communications · doi:10.1038/ncomms14977 · Cited by 204
The importance of gene–environment interactions in human obesity
Reddon, H., Guéant, J., & Meyre, D. (2016) · Clinical Science · doi:10.1042/cs20160221 · Cited by 210
Whole-Genome Sequencing Coupled to Imputation Discovers Genetic Signals for Anthropometric Traits
Tachmazidou, I., Süveges, D., Min, J. L., Ritchie, G. R. S., Steinberg, J., Walter, K., Iotchkova, V., Schwartzentruber, J., Huang, J., Memari, Y., et al. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.04.014 · Cited by 192
A genome-wide association study of bitter and sweet beverage consumption
Zhong, V. W., Kuang, A., Danning, R., Kraft, P. L., van Dam, R. M., Chasman, D. I., & Cornelis, M. C. (2019) · Human Molecular Genetics · doi:10.1093/hmg/ddz061 · Cited by 172
Self-citation
Assessing the Causal Relationships Between Insulin Resistance and Hyperuricemia and Gout Using Bidirectional Mendelian Randomization
McCormick, N., O’Connor, M. J., Yokose, C., Merriman, T. R., Mount, D. B., Leong, A., & Choi, H. K. (2021) · Arthritis & Rheumatology · doi:10.1002/art.41779 · Cited by 174
Adiposity, metabolites, and colorectal cancer risk: Mendelian randomization study
Bull, C. J., Bell, J. A., Murphy, N., Sanderson, E. C. M., Smith, G. D., Timpson, N. J., Banbury, B. L., Albanes, D., Berndt, S. I., Bezieau, S., et al. (2020) · BMC Medicine · doi:10.1186/s12916-020-01855-9 · Cited by 174
Inherited basis of visceral, abdominal subcutaneous and gluteofemoral fat depots
Agrawal, S., Wang, M., Klarqvist, M. D. R., Smith, K., Shin, J. H., Dashti, H., Diamant, N., Choi, S. H., Jurgens, S. J., Ellinor, P. T., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-30931-2 · Cited by 154
Genetic loci associated with heart rate variability and their effects on cardiac disease risk
Nolte, I. M., Muñoz, M. L., Tragante, V., Amare, A. T., Jansen, R., Vaez, A., von der Heyde, B., Avery, C. L., Bis, J. C., Dierckx, B., et al. (2017) · Nature Communications · doi:10.1038/ncomms15805 · Cited by 157
The genetic architecture of structural left–right asymmetry of the human brain
Sha, Z., Schijven, D., Carrión-Castillo, A., Joliot, M., Mazoyer, B. M., Fisher, S. E., Crivello, F., & Francks, C. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01069-w · Cited by 138
Human Genetics of Addiction: New Insights and Future Directions
Hancock, D. B., Markunas, C. A., Bierut, L. J., & Johnson, E. O. (2018) · Current Psychiatry Reports · doi:10.1007/s11920-018-0873-3 · Cited by 145
Insights into the aetiology of snoring from observational and genetic investigations in the UK Biobank
Campos, A. I., García‐Marín, L. M., Byrne, E. M., Martin, N. G., Cuéllar-Partida, G., & Rentería, M. E. (2020) · Nature Communications · doi:10.1038/s41467-020-14625-1 · Cited by 137
Genome-wide association study of delay discounting in 23,217 adult research participants of European ancestry
the 23andMe Research Team, Sanchez-Roige, S., Fontanillas, P., Elson, S. L., Pandit, A., Schmidt, E. M., Foerster, J. R., Abecasis, G. R., Gray, J. C., de Wit, H., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-017-0032-x · Cited by 128
Bias in two-sample Mendelian randomization when using heritable covariable-adjusted summary associations
Hartwig, F. P., Tilling, K. M., Smith, G. D., Lawlor, D. A., & Borges, M. C. (2021) · International Journal of Epidemiology · doi:10.1093/ije/dyaa266 · Cited by 116
Adjustment for index event bias in genome-wide association studies of subsequent events.
Dudbridge, F., Allen, R. J., Sheehan, N. A., Schmidt, A. F., Lee, J. C., Jenkins, R. G., Wain, L. V., Hingorani, A. D., & Patel, R. S. (2019) · Nature Communications · doi:10.1038/s41467-019-09381-w · Cited by 115
A Robust Example of Collider Bias in a Genetic Association Study
Day, F. R., Loh, P., Scott, R. A., Ong, K. K., & Perry, J. R. B. (2016) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2015.12.019 · Cited by 104
Identifying loci with different allele frequencies among cases of eight psychiatric disorders using CC-GWAS
Peyrot, W. J., & Price, A. L. (2021) · Nature Genetics · doi:10.1038/s41588-021-00787-1 · Cited by 107
Self-citation
Evaluating the contribution of genetics and familial shared environment to common disease using the UK Biobank
Muñoz, M., Pong‐Wong, R., Canela‐Xandri, O., Rawlik, K., Haley, C., & Tenesa, A. (2016) · Nature Genetics · doi:10.1038/ng.3618 · Cited by 106
Comprehensive genetic analysis of the human lipidome identifies loci associated with lipid homeostasis with links to coronary artery disease
Cadby, G., Giles, C., Melton, P. E., Huynh, K., Mellett, N. A., Duong, T., Nguyen, T. A., Cinel, M. A., Smith, A., Olshansky, G., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-30875-7 · Cited by 108
Protein-coding variants implicate novel genes related to lipid homeostasis contributing to body-fat distribution
Justice, A. E., Karaderi, T., Highland, H. M., Young, K. L., Graff, M., Lu, Y., Turcot, V., Auer, P. L., Fine, R. S., Guo, X., et al. (2019) · Nature Genetics · doi:10.1038/s41588-018-0334-2 · Cited by 101
Genome-wide association studies suggest sex-specific loci associated with abdominal and visceral fat
Sung, Y. J., Përusse, L., Sarzynski, M. A., Fornage, M., Sidney, S., Sternfeld, B., Rice, T., Terry, J. G., Jacobs, D. R., Katzmarzyk, P. T., et al. (2015) · International Journal of Obesity · doi:10.1038/ijo.2015.217 · Cited by 98
Adiposity impacts cognitive function in Asian populations: an epidemiological and Mendelian Randomization study
Mina, T. H., Yew, Y. W., Ng, H. K., Sadhu, N., Wansaicheong, G. K. L., Dalan, R., Low, D. Y., Lam, B. C. C., Ríboli, E., Lee, E. S., et al. (2023) · The Lancet Regional Health - Western Pacific · doi:10.1016/j.lanwpc.2023.100710 · Cited by 100
Nature vs nurture in knee osteoarthritis – the importance of age, sex and body mass index
Magnusson, K., Turkiewicz, A., & Englund, M. (2019) · Osteoarthritis and Cartilage · doi:10.1016/j.joca.2018.12.018 · Cited by 92
Genome-wide Analysis of Body Proportion Classifies Height-Associated Variants by Mechanism of Action and Implicates Genes Important for Skeletal Development
Chan, Y., Salem, R. M., Hsu, Y.-H. H., McMahon, G., Pers, T. H., Vedantam, S., Esko, T., Guo, M. H., Lim, E. T., Franke, L., et al. (2015) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2015.02.018 · Cited by 82
Clinical and Genome-Wide Analysis of Cisplatin-Induced Peripheral Neuropathy in Survivors of Adult-Onset Cancer
Dolan, M. E., Charif, O. E., Wheeler, H. E., Gamazon, E. R., Ardeshir‐Rouhani‐Fard, S., Monahan, P. O., Feldman, D. R., Hamilton, R. J., Vaughn, D. J., Beard, C. J., et al. (2017) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-16-3224 · Cited by 86
Lung function and cardiovascular disease: a two-sample Mendelian randomisation study
Higbee, D., Granell, R., Sanderson, E. C. M., Smith, G. D., & Dodd, J. W. (2021) · European Respiratory Journal · doi:10.1183/13993003.03196-2020 · Cited by 86
Circulating levels of micronutrients and risk of infections: a Mendelian randomization study
Flatby, H. M., Ravi, A., Damås, J. K., Solligård, E., & Rogne, T. (2023) · BMC Medicine · doi:10.1186/s12916-023-02780-3 · Cited by 88
Assessing the Causal Role of Body Mass Index on Cardiovascular Health in Young Adults
Wade, K. H., Chiesa, S. T., Hughes, A. D., Chaturvedi, N., Charakida, M., Rapala, A., Muthurangu, V., Khan, T. A., Finer, N., Sattar, N. A., et al. (2018) · Circulation · doi:10.1161/circulationaha.117.033278 · Cited by 86
A comprehensive study of metabolite genetics reveals strong pleiotropy and heterogeneity across time and context
Gallois, A., Mefford, J., Ko, A., Vaysse, A., Julienne, H., Ala‐Korpela, M., Laakso, M., Zaitlen, N., Pajukanta, P., & Aschard, H. (2019) · Nature Communications · doi:10.1038/s41467-019-12703-7 · Cited by 78
Self-citation
Genetic insights into human cortical organization and development through genome-wide analyses of 2,347 neuroimaging phenotypes
Warrier, V., Stauffer, E.-M., Huang, Q. Q., Wigdor, E. M., Slob, E. A. W., Seidlitz, J., Ronan, L., Valk, S. L., Mallard, T. T., Grotzinger, A. D., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01475-y · Cited by 91
The genetic architecture and evolution of the human skeletal form
Kun, E., Javan, E. M., Smith, O. S., Gulamali, F. F., de la Fuente, J., Flynn, B. I., Vajrala, K., Trutner, Z. D., Jayakumar, P., Tucker–Drob, E. M., et al. (2023) · Science · doi:10.1126/science.adf8009 · Cited by 80
Genome-wide association studies in East Asians identify new loci for waist-hip ratio and waist circumference
Wen, W., Kato, N., Hwang, J., Guo, X., Tabara, Y., Li, H., Dorajoo, R., Yang, X., Tsai, F., Li, S., et al. (2016) · Scientific Reports · doi:10.1038/srep17958 · Cited by 76
Genome-wide association study using family-based cohorts identifies the WLS and CCDC170/ESR1 loci as associated with bone mineral density
Mullin, B. H., Walsh, J. P., Zheng, H., Brown, S. J., Surdulescu, G. L., Curtis, C. J. C., Breen, G. D., Dudbridge, F., Richards, J. B., Spector, T. D., et al. (2016) · BMC Genomics · doi:10.1186/s12864-016-2481-0 · Cited by 73
Phenome-wide Mendelian-randomization study of genetically determined vitamin D on multiple health outcomes using the UK Biobank study
Meng, X., Li, X., Timofeeva, M. N., He, Y., Spiliopoulou, A., Wei, W., Gifford, A., Wu, H., Varley, T., Joshi, P. K., et al. (2019) · International Journal of Epidemiology · doi:10.1093/ije/dyz182 · Cited by 75
Genetic study of multimodal imaging Alzheimer’s disease progression score implicates novel loci
Scelsi, M. A., Khan, R. R., Lorenzi, M., Christopher, L., Greicius, M. D., Schott, J. M., Ourselin, S., & Altmann, A. (2018) · Brain · doi:10.1093/brain/awy141 · Cited by 73
Formalising recall by genotype as an efficient approach to detailed phenotyping and causal inference
Corbin, L. J., Tan, V. Y., Hughes, D. A., Wade, K. H., Paul, D. S., Tansey, K. E., Butcher, F. C., Dudbridge, F., Howson, J. M. M., Jallow, M. W., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03109-y · Cited by 72
Genome-wide association study identifies 18 novel loci associated with left atrial volume and function
Ahlberg, G., Andreasen, L., Ghouse, J., Bertelsen, L., Bundgaard, H., Haunsø, S., Svendsen, J. H., & OLESEN, M. S. (2021) · European Heart Journal · doi:10.1093/eurheartj/ehab466 · Cited by 68
Community-wide genome sequencing reveals 30 years of Darwin’s finch evolution
Enbody, E. D., Sendell-Price, A. T., Sprehn, C. G., Rubin, C.-J., Visscher, P. M., Grant, B. R., Grant, P. R., & Andersson, L. (2023) · Science · doi:10.1126/science.adf6218 · Cited by 75
A large-scale genome-wide association analysis of lung function in the Chinese population identifies novel loci and highlights shared genetic aetiology with obesity
Zhu, Z., Li, J., Si, J., Ma, B., Shi, H., Lv, J., Cao, W., Guo, Y., Millwood, I. Y., Walters, R. G., et al. (2021) · European Respiratory Journal · doi:10.1183/13993003.00199-2021 · Cited by 62
Phenotype integration improves power and preserves specificity in biobank-based genetic studies of major depressive disorder
Dahl, A., Thompson, M., An, U., Krebs, M. D., Appadurai, V., Border, R., Bacanu, S., Werge, T. M., Flint, J., Schork, A. J., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01559-9 · Cited by 63
Using Mendelian randomization to investigate a possible causal relationship between adiposity and increased bone mineral density at different skeletal sites in children
Kemp, J. P., Sayers, A. E., Smith, G. D., Tobias, J. H., & Evans, D. M. (2016) · International Journal of Epidemiology · doi:10.1093/ije/dyw079 · Cited by 58
Genetic loci and prioritization of genes for kidney function decline derived from a meta-analysis of 62 longitudinal genome-wide association studies
Gorski, M., Rasheed, H., Teumer, A., Thomas, L. F., Graham, S. E., Sveinbjörnsson, G., Winkler, T. W., Günther, F., Stark, K. J., Chai, J. F., et al. (2022) · Kidney International · doi:10.1016/j.kint.2022.05.021 · Cited by 56
Multiancestry exome sequencing reveals INHBE mutations associated with favorable fat distribution and protection from diabetes
Akbari, P., Sosina, O. A., Bovijn, J., Landheer, K., Nielsen, J. B., Kim, M., Aykul, S., De, T., Haas, M. E., Hindy, G., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-32398-7 · Cited by 58
Genetic effects on the commensal microbiota in inflammatory bowel disease patients
Aschard, H., Laville, V., Tchetgen, E. T., Knights, D., Imhann, F., Seksik, P., Zaitlen, N., Silverberg, M. S., Cosnes, J., Weersma, R. K., et al. (2019) · PLoS Genetics · doi:10.1371/journal.pgen.1008018 · Cited by 55
Self-citation
The genetic case for cardiorespiratory fitness as a clinical vital sign and the routine prescription of physical activity in healthcare
Hanscombe, K. B., Persyn, E., Traylor, M., Glanville, K. P., Hamer, M., Coleman, J. R. I., & Lewis, C. M. (2021) · Genome Medicine · doi:10.1186/s13073-021-00994-9 · Cited by 55
A phenome-wide comparative analysis of genetic discordance between obesity and type 2 diabetes
Coral, D., Fernandez-Tajes, J. J., Tsereteli, N., Pomares‐Millan, H., Fitipaldi, H., Mutie, P. M., Atabaki‐Pasdar, N., Kalamajski, S., Poveda, A., Miller‐Fleming, T. W., et al. (2023) · Nature Metabolism · doi:10.1038/s42255-022-00731-5 · Cited by 53
Educational attainment reduces the risk of suicide attempt among individuals with and without psychiatric disorders independent of cognition: a bidirectional and multivariable Mendelian randomization study with more than 815,000 participants
Rosoff, D. B., Kaminsky, Z. A., McIntosh, A. M., Smith, G. D., & Lohoff, F. W. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-01047-2 · Cited by 62
Dietary Factors and Endometrial Cancer Risk: A Mendelian Randomization Study
Wang, X. M., Glubb, D. M., & O’Mara, T. A. (2023) · Nutrients · doi:10.3390/nu15030603 · Cited by 57
A Mendelian Randomization dictionary: Useful definitions and descriptions for undertaking, understanding and interpreting Mendelian Randomization studiesPreprint
Lawlor, D. A., Wade, K. H., Borges, M. C., Palmer, T., Hartwig, F. P., Hemani, G., & Bowden, J. (2019) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/6yzs7 · Cited by 52
Discovery of genomic loci associated with sleep apnea risk through multi-trait GWAS analysis with snoring
Campos, A. I., Ingold, N., Huang, Y., Mitchell, B. L., Kho, P. F., Han, X., García‐Marín, L. M., Ong, J., Agee, M., Aslibekyan, S., et al. (2022) · SLEEP · doi:10.1093/sleep/zsac308 · Cited by 49
A joint view on genetic variants for adiposity differentiates subtypes with distinct metabolic implications
Winkler, T. W., Günther, F., Höllerer, S., Zimmermann, M. E., Loos, R. J. F., Kutalik, Z., & Heid, I. M. (2018) · Nature Communications · doi:10.1038/s41467-018-04124-9 · Cited by 52
Genetic Studies of Leptin Concentrations Implicate Leptin in the Regulation of Early Adiposity
Yaghootkar, H., Zhang, Y., Spracklen, C. N., Karaderi, T., Huang, L. O., Bradfield, J. P., Schurmann, C., Fine, R. S., Preuss, M. H., Kutalik, Z., et al. (2020) · Diabetes · doi:10.2337/db20-0070 · Cited by 51
Deep learning of left atrial structure and function provides link to atrial fibrillation risk
Pirruccello, J. P., Achille, P. D., Choi, S. H., Rämö, J. T., Khurshid, S. S., Nekoui, M., Jurgens, S. J., Nauffal, V., Kany, S., FinnGen, et al. (2024) · Nature Communications · doi:10.1038/s41467-024-48229-w · Cited by 52
Genetically predicted circulating concentrations of micronutrients and risk of colorectal cancer among individuals of European descent: a Mendelian randomization study
Tsilidis, K. K., Papadimitriou, N., Dimou, N. L., Gill, D., Lewis, S. J., Martin, R. M., Murphy, N., Markozannes, G., Zuber, V., Cross, A. J., et al. (2021) · American Journal of Clinical Nutrition · doi:10.1093/ajcn/nqab003 · Cited by 47
Genome-wide association study of dietary intake in the UK biobank study and its associations with schizophrenia and other traits
Niarchou, M., Byrne, E. M., Trzaskowski, M., Sidorenko, J., Kemper, K. E., McGrath, J. J., O’Donovan, M. C., Owen, M. J., & Wray, N. R. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-0688-y · Cited by 46
Causal link between lipid profile and bone mineral density: A Mendelian randomization study
Yang, X., Cui, Z., Zhang, H., Wei, X., Feng, G.-J., Liu, L., Liu, Y., Pei, Y., & Zhang, L. (2019) · Bone · doi:10.1016/j.bone.2019.05.037 · Cited by 44
Quantifying the extent to which index event biases influence large genetic association studies
Yaghootkar, H., Bancks, M. P., Jones, S. E., McDaid, A. F., Beaumont, R. N., Donnelly, L. A., Wood, A. R., Campbell, A., Tyrrell, J., Hocking, L. J., et al. (2016) · Human Molecular Genetics · doi:10.1093/hmg/ddw433 · Cited by 41
Meta-Analysis of Genomewide Association Studies Reveals Genetic Variants for Hip Bone Geometry
Hsu, Y., Estrada, K., Εvangelou, Ε., Ackert‐Bicknell, C. L., Åkesson, K., Beck, T., Brown, S. J., Capellini, T. D., Carbone, L. D., Cauley, J. A., et al. (2019) · Journal of Bone and Mineral Research · doi:10.1002/jbmr.3698 · Cited by 40
Genetic Pathways Underlying Individual Differences in Regular Physical Activity
de Geus, E. J. C. (2022) · Exercise and Sport Sciences Reviews · doi:10.1249/jes.0000000000000305 · Cited by 39
FTO Gene Polymorphism Is Associated with Type 2 Diabetes through Its Effect on Increasing the Maximum BMI in Japanese Men
Kamura, Y., Iwata, M., Maeda, S., Shinmura, S., Koshimizu, Y., Honoki, H., Fukuda, K., Ishiki, M., Usui, I., Fukushima, Y., et al. (2016) · PLoS ONE · doi:10.1371/journal.pone.0165523 · Cited by 41
Relationships between estimated autozygosity and complex traits in the UK Biobank
Johnson, E. C., Evans, L. M., & Keller, M. C. (2018) · PLoS Genetics · doi:10.1371/journal.pgen.1007556 · Cited by 38
Multivariable MR Can Mitigate Bias in Two‐Sample MR Using Covariable‐Adjusted Summary Associations
Gilbody, J., Borges, M. C., Smith, G. D., & Sanderson, E. C. M. (2025) · Genetic Epidemiology · doi:10.1002/gepi.22606 · Cited by 34
Genetic effects on planum temporale asymmetry and their limited relevance to neurodevelopmental disorders, intelligence or educational attainment
Carrión-Castillo, A., Pepe, A., Kong, X., Fisher, S. E., Mazoyer, B. M., Tzourio‐Mazoyer, N., Crivello, F., & Francks, C. (2019) · Cortex · doi:10.1016/j.cortex.2019.11.006 · Cited by 36
Comparison of Single-Trait and Multi-Trait Genome-Wide Association Models and Inclusion of Correlated Traits in the Dissection of the Genetic Architecture of a Complex Trait in a Breeding Program
Merrick, L. F., Burke, A. B., Zhang, Z., & Carter, A. H. (2022) · Frontiers in Plant Science · doi:10.3389/fpls.2021.772907 · Cited by 36
Addendum: A joint view on genetic variants for adiposity differentiates subtypes with distinct metabolic implications
Winkler, T. W., Günther, F., Höllerer, S., Zimmermann, M. E., Loos, R. J. F., Kutalik, Z., & Heid, I. M. (2018) · Nature Communications · doi:10.1038/s41467-018-05088-6 · Cited by 35
Kidney function and obstructive lung disease: a bidirectional Mendelian randomisation study
Park, S., Lee, S., Kim, Y., Cho, S., Kim, K., Kim, Y. C., Han, S. S., Lee, H., Lee, J. P., Joo, K. W., et al. (2021) · European Respiratory Journal · doi:10.1183/13993003.00848-2021 · Cited by 34
Multivariate genomic architecture of cortical thickness and surface area at multiple levels of analysis
Grotzinger, A. D., Mallard, T. T., Liu, Z., Seidlitz, J., Ge, T., & Smoller, J. W. (2023) · Nature Communications · doi:10.1038/s41467-023-36605-x · Cited by 35
Using genetics to assess the association of commonly used antihypertensive drugs with diabetes, glycaemic traits and lipids: a trans-ancestry Mendelian randomisation study
Zhao, J. V., Liu, F., Schooling, C. M., Li, J., Gu, D., & Lu, X. (2022) · Diabetologia · doi:10.1007/s00125-021-05645-7 · Cited by 34
Adjusting for principal components can induce collider bias in genome-wide association studies
Grinde, K. E., Browning, B. L., Reiner, A. P., Thornton, T. A., & Browning, S. R. (2024) · PLoS Genetics · doi:10.1371/journal.pgen.1011242 · Cited by 34
Genome-wide association study of school grades identifies genetic overlap between language ability, psychopathology and creativity
Rajagopal, V. M., Ganna, A., Coleman, J. R. I., Allegrini, A. G., Voloudakis, G., Grove, J., Als, T. D., Horsdal, H. T., Petersen, L. V., Appadurai, V., et al. (2023) · Scientific Reports · doi:10.1038/s41598-022-26845-0 · Cited by 36
Heritability of Thoracic Spine Curvature and Genetic Correlations With Other Spine Traits: The Framingham Study
Yau, M. S., Demissie, S., Zhou, Y., Anderson, D., Lorbergs, A. L., Kiel, D. P., Allaire, B., Yang, L., Cupples, L. A., Travison, T. G., et al. (2016) · Journal of Bone and Mineral Research · doi:10.1002/jbmr.2925 · Cited by 31
Somatic mutational profiles and germline polygenic risk scores in human cancer
Liu, Y., Gusev, A., Heng, Y. J., Alexandrov, L. B., & Kraft, P. L. (2022) · Genome Medicine · doi:10.1186/s13073-022-01016-y · Cited by 28
Self-citation
Genomic Prediction of Autogamous and Allogamous Plants by SNPs and Haplotypes
Matias, F. I., Galli, G., Granato, Í. S. C., & Fritsche‐Neto, R. (2017) · Crop Science · doi:10.2135/cropsci2017.01.0022 · Cited by 26
Adjusting for collider bias in genetic association studies using instrumental variable methods
Cai, S., Hartley, A. E., Mahmoud, O., Tilling, K. M., & Dudbridge, F. (2022) · Genetic Epidemiology · doi:10.1002/gepi.22455 · Cited by 30
Covariate selection for association screening in multiphenotype genetic studies
Aschard, H., Guillemot, V., Vilhjálmsson, B. J., Patel, C. J., Skurnik, D., Ye, C., Wolpin, B. M., Kraft, P. L., & Zaitlen, N. (2017) · Nature Genetics · doi:10.1038/ng.3975 · Cited by 28
Self-citation
Variants at the APOE/C1/C2/C4 Locus Modulate Cholesterol Efflux Capacity Independently of High‐Density Lipoprotein Cholesterol
Low‐Kam, C., Rhainds, D., Lo, K. S., Barhdadi, A., Boulé, M., Alem, S., Pedneault-Gagnon, V., Rhéaume, É., Dubé, M., Busseuil, D., et al. (2018) · Journal of the American Heart Association · doi:10.1161/jaha.118.009545 · Cited by 28
Causal association between obesity and hypothyroidism: a two-sample bidirectional Mendelian randomization study
Qiu, Y., Liu, Q., Luo, Y., Chen, J., Zheng, Q., Xie, Y., & Cao, Y. (2024) · Frontiers in Endocrinology · doi:10.3389/fendo.2023.1287463 · Cited by 31
Disentangling polygenic associations between attention-deficit/hyperactivity disorder, educational attainment, literacy and language
Verhoef, E., Demontis, D., Burgess, S., Shapland, C. Y., Dale, P. S., Okbay, A., Neale, B. M., Faraone, S. V., Agerbo, E., Als, T. D., et al. (2019) · Translational Psychiatry · doi:10.1038/s41398-018-0324-2 · Cited by 32
Mendelian randomization highlights the causal association of obesity with periodontal diseases
Dong, J., Gong, Y., Chu, T., Wu, L., Li, S., Deng, H., Hu, R., & Wang, Y. (2022) · Journal Of Clinical Periodontology · doi:10.1111/jcpe.13640 · Cited by 27

Authors: Aschard, H., Vilhjálmsson, B. J., Joshi, A. D., Price, A. L., & Kraft, P.
Affiliations: Harvard University (US)
Corresponding author: Aschard, Hugues
ORCID: Aschard, Hugues
Vilhjálmsson, Bjarni J.
Joshi, Amit D.
Price, Alkes L.
Kraft, Peter
Venue: The American Journal of Human Genetics
Publisher: Elsevier BV
Published: 2015-02
Volume: 96
Issue: 2
Pages: 329-339
ISSN: 0002-9297 (ISSN-L)
DOI: 10.1016/j.ajhg.2014.12.021
PMC: PMC4320269
OpenAlex: W1996682563
Cited by: 287
References: 40
Citations by year: 2026: 16 · 2025: 25 · 2024: 30 · 2023: 27 · 2022: 30 · 2021: 33 · 2020: 26 · 2019: 24 · 2018: 26 · 2017: 20 · 2016: 22 · 2015: 8
FWCI: 17.13
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (vor, 185 d)
Locations: The American Journal of Human Genetics (publishedVersion)
Europe PMC (PubMed Central) (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
HAL (submittedVersion)
Funders: National Human Genome Research Institute (R03HG006720)
National Cancer Institute (U19CA148065)
National Institute of Environmental Health Sciences (R21ES020754)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
Keywords: genome-wide association study, correlated traits, false positives, waist-to-hip ratio, waist circumference, body mass index, confounding
MeSH terms: Computer Simulation, Humans, Models, Genetic, Genetic Variation, Body Mass Index, Quantitative Trait, Heritable, Waist-Hip Ratio, Waist Circumference, Genome-Wide Association Study

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Adjusting for Heritable Covariates Can Bias Effect Estimates in Genome-Wide Association Studies

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks