The effects of human population structure on large genetic association studies

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Statistical significance for genomewide studies

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The International HapMap Project

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The Future of Genetic Studies of Complex Human Diseases

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The History and Geography of Human Genes

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Genomic Control for Association Studies

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Association mapping in structured populations.

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Searching for genetic determinants in the new millennium

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Use of Unlinked Genetic Markers to Detect Population Stratification in Association Studies

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From Genotypes to Genes: Doubling the Sample Size

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A Bayesian non-parametric mixed-effects model of microbial growth curves

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Systematic meta-analyses and field synopsis of genetic association studies in colorectal adenomas

Montazeri, Z., Τheodoratou, E., Nyiraneza, C., Timofeeva, M. N., Chen, W., Svinti, V., Sivakumaran, S., Gresham, G. K., Cubitt, L., Carvajal‐Carmona, L. G., et al. (2015) · International Journal of Epidemiology · doi:10.1093/ije/dyv185 · Cited by 24

Effective Sample Size: Quick Estimation of the Effect of Related Samples in Genetic Case-Control Association Analyses

Yang, Y., Remmers, E. F., Ogunwole, C. B., Kastner, D. L., Gregersen, P. K., & Li, W. (2007) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2010.12.006 · Cited by 21

A Unified Approach for Quantifying, Testing and Correcting Population Stratification in Case-Control Association Studies

Gorroochurn, P., Hodge, S. E., Heiman, G. A., & Greenberg, D. A. (2007) · Human Heredity · doi:10.1159/000102988 · Cited by 24

Rigor and reproducibility in genetic research on eating disorders

Hübel, C., Leppä, V., Breen, G. D., & Bulik, C. M. (2018) · International Journal of Eating Disorders · doi:10.1002/eat.22896 · Cited by 24

Racial disparities in risk factors for thrombosis

Itakura, H. (2005) · Current Opinion in Hematology · doi:10.1097/01.moh.0000167746.26145.53 · Cited by 24

Subarachnoid hemorrhage: tests of association with apolipoprotein E and elastin genes

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Planning and Executing a Genome Wide Association Study (GWAS)

Sale, M. M., Mychaleckyj, J. C., & Chen, W. (2009) · Methods in molecular biology · doi:10.1007/978-1-60327-378-7_25 · Cited by 23

Correcting for relatedness in Bayesian models for genomic data association analysis

Pikkuhookana, P., & Sillanpää, M. J. (2009) · Heredity · doi:10.1038/hdy.2009.56 · Cited by 22

Natural selection on cork oak: allele frequency reveals divergent selection in cork oak populations along a temperature cline

Ramírez‐Valiente, J. A., Lorenzo, Z., Soto, Á., Valladares, F., Gil, L., & Aranda, I. (2010) · Evolutionary Ecology · doi:10.1007/s10682-010-9365-6 · Cited by 22

- Applications of Autosomal SNPs and Indels in Forensic Analysis

Phillips, C. (2013) · CRC Press eBooks · doi:10.1201/b15361-17 · Cited by 23

Evaluation of the Precision of Ancestry Inferences in South American Admixed Populations

Pereira, V., Santangelo, R., Børsting, C., Tvedebrink, T., Almeida, A. P. F., de Carvalho, E. F., Morling, N., & Gusmão, L. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00966 · Cited by 22

Impact of the AHI1 Gene on the Vulnerability to Schizophrenia: A Case-Control Association Study

Rivero, O., Reif, A., Sanjuán, J., Moltó, M. D., Kittel‐Schneider, S., Nájera, C., Töpner, T., & Lesch, K. (2010) · PLoS ONE · doi:10.1371/journal.pone.0012254 · Cited by 23

Inferring the demographic history of European Ficedula flycatcher populations

Backström, N., Sætre, G., & Ellegren, H. (2013) · BMC Evolutionary Biology · doi:10.1186/1471-2148-13-2 · Cited by 27

Oxytocin receptor gene (OXTR) in relation to loneliness in adolescence

van Roekel, E., Verhagen, M., Engels, R. C. M. E., Goossens, L., & Scholte, R. H. J. (2013) · Psychiatric Genetics · doi:10.1097/ypg.0b013e328363f631 · Cited by 24

Effects of population structure on genetic association studies

Xu, H., & Shete, S. S. (2005) · BMC Genetics · doi:10.1186/1471-2156-6-s1-s109 · Cited by 24

Physiogenomic Analysis of Localized fMRI Brain Activity in Schizophrenia

Windemuth, A., Calhoun, V. D., Pearlson, G. D., Kocherla, M., Jagannathan, K., & Ruaño, G. (2008) · Annals of Biomedical Engineering · doi:10.1007/s10439-008-9475-2 · Cited by 22

Designing pharmacogenetic projects in industry: practical design perspectives from the Industry Pharmacogenomics Working Group

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Issues with Polymorphism Analysis in Sepsis

Sutherland, A. M., & Russell, J. A. (2005) · Clinical Infectious Diseases · doi:10.1086/431989 · Cited by 22

Genome-wide association studies of brain structure and function in the UK BiobankPreprint

Elliott, L. T., Sharp, K., Alfaro‐Almagro, F., Shi, S., Miller, K. L., Douaud, G., Marchini, J., & Smith, S. M. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/178806 · Cited by 23

Posterior predictive checks to quantify lack-of-fit in admixture models of latent population structure

Mimno, D., Blei, D. M., & Engelhardt, B. E. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1412301112 · Cited by 22

Genome-Wide Analysis of Blood Pressure Variability and Ischemic Stroke

Yadav, S., Cotlarciuc, I., Munroe, P. B., Khan, M. S., Nalls, M. A., Bevan, S., Cheng, Y.-C., Chen, W., Malik, R., McCarthy, N. S., et al. (2013) · Stroke · doi:10.1161/strokeaha.113.002186 · Cited by 21

Choice of population structure informative principal components for adjustment in a case-control study

Peloso, G. M., & Lunetta, K. L. (2011) · BMC Genetics · doi:10.1186/1471-2156-12-64 · Cited by 21

Genome-wide association studies of cancer: principles and potential utility.

Stadler, Z. K., Gallagher, D. J., Thom, P., & Offit, K. (2010) · PubMed · Cited by 21

Regional surnames and genetic structure in Great Britain

Kandt, J., Cheshire, J. A., & Longley, P. (2016) · Transactions of the Institute of British Geographers · doi:10.1111/tran.12131 · Cited by 21

Genetic Component of Type 2 Diabetes in a Mexican Population

Sanchez-Pozos, K. A., & Menjívar, M. (2016) · Archives of Medical Research · doi:10.1016/j.arcmed.2016.12.007 · Cited by 22

Confounding in Genetic Association Studies and Its Solutions

Hu, D., & Ziv, E. (2008) · Methods in molecular biology · doi:10.1007/978-1-59745-205-2_3 · Cited by 21

Case-Control Association Studies in Mixed Populations: Correcting Using Genomic Control

Shmulewitz, D., Zhang, J., & Greenberg, D. A. (2004) · Human Heredity · doi:10.1159/000083541 · Cited by 21

Unveiling the genetic architecture for lodging resistance in rice (Oryza sativa. L) by genome-wide association analyses

Rashid, M. A. R., Zhao, Y., Azeem, F., Zhao, Y., Ahmed, H. G. M., Atif, R. M., Pan, Y., Zhu, X., Liang, Y., Zhang, H., et al. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.960007 · Cited by 34

Global Individual Ancestry Using Principal Components for Family Data

de Andrade, M., Ray, D., Pereira, A. C., & Soler, J. M. P. (2015) · Human Heredity · doi:10.1159/000381908 · Cited by 20

Imaging genetics — towards discovery neuroscience

Ge, T., Schumann, G., & Feng, J. (2013) · Quantitative Biology · doi:10.1007/s40484-013-0023-1 · Cited by 21

Ancient Ancestry Informative Markers for Identifying Fine-Scale Ancient Population Structure in Eurasians

Esposito, U., Das, R., Syed, S., Pirooznia, M., & Elhaik, E. (2018) · Genes · doi:10.3390/genes9120625 · Cited by 19

Association between nonsynonymous mutations of starch synthase IIa and starch quality in rice (Oryza sativa)

Yu, G., Olsen, K. M., & Schaal, B. A. (2010) · New Phytologist · doi:10.1111/j.1469-8137.2010.03495.x · Cited by 20

Systems biology and heart failure: concepts, methods, and potential research applications

Adams, K. F. (2009) · Heart Failure Reviews · doi:10.1007/s10741-009-9138-x · Cited by 20

Power considerations for λ inflation factor in meta-analyses of genome-wide association studies

Georgiopoulos, G. A., & Εvangelou, Ε. (2016) · Genetics Research · doi:10.1017/s0016672316000069 · Cited by 18

Lack of Effects of the Genetic Polymorphisms of Interleukin-10 in Clinical Outcomes of COVID-19

Avendaño-Félix, M. M., Ochoa‐Ramírez, L. A., Ramos‐Payán, R., Aguilar‐Medina, M., Ayala-Ham, A. D. R., Rendón‐Aguilar, H., Lizárraga-Verdugo, E. R., Peraza‐Garay, F. D. J., Ríos‐Tostado, J. J., & Velarde‐Félix, J. S. (2021) · Viral Immunology · doi:10.1089/vim.2021.0022 · Cited by 20

Sample matching by inferred agonal stress in gene expression analyses of the brain

Li, J. Z., Meng, F., Tsavaler, L., Evans, S. J., Choudary, P. V., Tomita, H., Vawter, M. P., Walsh, D. M. A., Shokoohi, V., Chung, T. R., et al. (2007) · BMC Genomics · doi:10.1186/1471-2164-8-336 · Cited by 19

Covariate adjustment in family‐based association studies

Whittemore, A. S., Halpern, J., & Ahsan, H. (2004) · Genetic Epidemiology · doi:10.1002/gepi.20055 · Cited by 19

Identifying susceptibility genes for immunological disorders: patterns, power, and proof

Plenge, R. M., & Rioux, J. D. (2006) · Immunological Reviews · doi:10.1111/j.0105-2896.2006.00372.x · Cited by 19

The comparative patterns of linkade disequilibrium in European populations and its implication for genetic association studies

Lõhmussaar, E. (2006) · DSpace repository (University of Tartu) · Cited by 19

Cluster ensemble based on Random Forests for genetic data

Al-Husain, L., & Hafez, A. M. (2017) · BioData Mining · doi:10.1186/s13040-017-0156-2 · Cited by 18

Mixed‐effects Logistic Approach for Association Following Linkage Scan for Complex Disorders

Xu, H., & Shete, S. S. (2006) · Annals of Human Genetics · doi:10.1111/j.1469-1809.2006.00321.x · Cited by 18

Bio-Repository of DNA in stroke (BRAINS): A study protocol

Yadav, S., Schanz, R., Maheshwari, A., Khan, M. S., Slark, J., de Silva, R., Bentley, P., Froguel, P., Kooner, J. S., Shrivastav, P., et al. (2011) · BMC Medical Genetics · doi:10.1186/1471-2350-12-34 · Cited by 18

A genome scan for genes underlying adult body size differences between Central African hunter-gatherers and farmers

Pemberton, T. J., Verdu, P., Becker, N. S., Willer, C. J., Hewlett, B. S., Le Bomin, S., Froment, A., Rosenberg, N. A., & Heyer, E. (2018) · Human Genetics · doi:10.1007/s00439-018-1902-3 · Cited by 18

Efficient approaches for large-scale GWAS with genotype uncertainty

Jørsboe, E., & Albrechtsen, A. (2021) · G3 Genes Genomes Genetics · doi:10.1093/g3journal/jkab385 · Cited by 18

Indoor and outdoor air pollution and lung cancer in New Hampshire and Vermont

Sloan, C., Andrew, A. S., Gruber, J. F., Mwenda, K. M., Moore, J. H., Onega, T., Karagas, M. R., Shi, X., & Duell, E. J. (2012) · Toxicological & Environmental Chemistry Reviews · doi:10.1080/02772248.2012.659930 · Cited by 18

Participant identification in genetic association studies: improved methods and practical implications

Masca, N. G. D., Burton, P. R., & Sheehan, N. A. (2011) · International Journal of Epidemiology · doi:10.1093/ije/dyr149 · Cited by 19

New Interventions for Malaria: Mining the Human and Parasite Genomes

Ntoumi, F., Kwiatkowski, D. P., Diakité, M., Mutabingwa, T. K., & Duffy, P. E. (2007) · American Journal of Tropical Medicine and Hygiene · doi:10.4269/ajtmh.2007.77.270 · Cited by 18

Efficient Utilization of Rare Variants for Detection of Disease-Related Genomic Regions

Zhang, L., Pei, Y., Li, J., Papasian, C. J., & Deng, H. (2010) · PLoS ONE · doi:10.1371/journal.pone.0014288 · Cited by 19

Genetic diversity and natural selection footprints of the glycine amidinotransferase gene in various human populations

Khan, A., Tian, L., Zhang, C., Yuan, K., & Xu, S. (2016) · Scientific Reports · doi:10.1038/srep18755 · Cited by 17

Performance of model-based multifactor dimensionality reduction methods for epistasis detection by controlling population structure

Abegaz, F., Lishout, F. V., John, J. M., Chiachoompu, K., Bhardwaj, A., Duroux, D., Gusareva, E. S., Wei, Z., Hakonarson, H. H., & Steen, K. V. (2021) · BioData Mining · doi:10.1186/s13040-021-00247-w · Cited by 17

Targeted re-sequencing and genome-wide association analysis for wood property traits in breeding population of Eucalyptus tereticornis × E. grandis

Dasgupta, M. G., Bari, M. P. A., Shanmugavel, S., Dharanishanthi, V., Muthupandi, M., Kumar, N., Chauhan, S., Kalaivanan, J., Mohan, H., Krutovsky, K. V., et al. (2021) · Genomics · doi:10.1016/j.ygeno.2021.11.013 · Cited by 18

A Genetics Primer for Social Health Research

Way, B. M., & Gurbaxani, B. M. (2008) · Social and Personality Psychology Compass · doi:10.1111/j.1751-9004.2008.00084.x · Cited by 17

Genome-wide genetic heterogeneity discovery with categorical covariates

Llinares-López, F., Meng-Papaxanthos, L., Bodenham, D. A., Roqueiro, D., COPDGene Investigators, & Borgwardt, K. M. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx071 · Cited by 17

The genetics of late maturity alpha-amylase (LMA) in North American spring wheat (Triticum aestivum L.)

Liu, C., Parveen, R. S., Revolinski, S. R., Garland-Campbell, K. A., Pumphrey, M., & Steber, C. M. (2021) · Seed Science Research · doi:10.1017/s0960258521000064 · Cited by 17

A propensity score approach to correction for bias due to population stratification using genetic and non‐genetic factors

Zhao, H., Rebbeck, T. R., & Mitra, N. (2009) · Genetic Epidemiology · doi:10.1002/gepi.20419 · Cited by 17

The genetics of cardiovascular disease

Delles, C., McBride, M., Padmanabhan, S., & Dominiczak, A. F. (2008) · Trends in Endocrinology and Metabolism · doi:10.1016/j.tem.2008.07.010 · Cited by 16

Resources for Genetic Variation Studies

Serre, D., & Hudson, T. J. (2006) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev.genom.7.080505.115806 · Cited by 17

Ecogeographic genetic epidemiology

Sloan, C., Duell, E. J., Shi, X., Irwin, R. E., Andrew, A. S., Williams, S. M., & Moore, J. H. (2008) · Genetic Epidemiology · doi:10.1002/gepi.20386 · Cited by 17

Genome-Wide Association Analyses of Equine Metabolic Syndrome Phenotypes in Welsh Ponies and Morgan Horses

Norton, E. M., Schultz, N. E., Geor, R. J., McFarlane, D., Mickelson, J. R., & McCue, M. E. (2019) · Genes · doi:10.3390/genes10110893 · Cited by 16

A Novel Method to Detect Gene-Gene Interactions in Structured Populations: MDR-SP

Niu, A., Zhang, S., & Sha, Q. (2011) · Annals of Human Genetics · doi:10.1111/j.1469-1809.2011.00681.x · Cited by 17

Analysis of association of MEF2C, SOST and JAG1 genes with bone mineral density in Mexican-Mestizo postmenopausal women

Velázquez‐Cruz, R., Jiménez-Ortega, R. F., Parra-Torres, A. Y., Castillejos-López, M. D. J., Patiño, N., Quiterio, M., Villarreal-Molina, M. T., & Salmerón, J. (2014) · BMC Musculoskeletal Disorders · doi:10.1186/1471-2474-15-400 · Cited by 16

Statistical distributions of test statistics used for quantitative trait association mapping in structured populations

Teyssèdre, S., Elsen, J.-M., & Ricard, A. (2012) · Genetics Selection Evolution · doi:10.1186/1297-9686-44-32 · Cited by 17

Comparison of Statistical Methods for Estimating Genetic Admixture in a Lung Cancer Study of African Americans and Latinos

Aldrich, M. C., Selvin, S., Hansen, H. M., Barcellos, L. F., Wrensch, M., Sison, J. D., Quesenberry, C. P., Kittles, R. A., Silva, G. O., Buffler, P. A., et al. (2008) · American Journal of Epidemiology · doi:10.1093/aje/kwn224 · Cited by 16

Association Mapping of Complex Trait Loci With Context-Dependent Effects and Unknown Context Variable

Sillanpää, M. J., & Bhattacharjee, M. (2006) · Genetics · doi:10.1534/genetics.106.061275 · Cited by 16

Rethinking the Epigenetic Framework to Unravel the Molecular Pathology of Schizophrenia

Cariaga-Martínez, A. E., & Alelú‐Paz, R. (2017) · International Journal of Molecular Sciences · doi:10.3390/ijms18040790 · Cited by 15

Association statistics under the PPL framework

Huang, Y., & Vieland, V. J. (2010) · Genetic Epidemiology · doi:10.1002/gepi.20537 · Cited by 15

Genetic Polymorphisms of Tumor Necrosis Factor Alpha and Susceptibility to Dengue Virus Infection in a Mexican Population

Sánchez-Leyva, M., Sánchez‐Zazueta, J. G., Osuna‐Ramos, J. F., Rendón‐Aguilar, H., Félix-Espinoza, R., Becerra‐Loaiza, D. S., Sánchez-García, D. C., Romero-Quintana, J. G., Castillo-Ureta, H., Velarde-Rodríguez, I., et al. (2017) · Viral Immunology · doi:10.1089/vim.2017.0029 · Cited by 15

Optimizing the Power to Identify the Genetic Basis of Complex Traits with Evolve and Resequence Studies

Vlachos, C., & Kofler, R. (2019) · Molecular Biology and Evolution · doi:10.1093/molbev/msz183 · Cited by 15

Identification of favorable haplotypes/alleles and candidate genes for three plant architecture-related traits via a restricted two-stage multilocus genome-wide association study in upland cotton

Wang, C., Ma, Q., Xie, X., Zhang, X., Yang, D., Su, J., Ma, X., & Lin, H. (2021) · Industrial Crops and Products · doi:10.1016/j.indcrop.2021.114458 · Cited by 20

The Int7G24A variant of transforming growth factor-beta receptor type I is a risk factor for colorectal cancer in the male Spanish population: a case-control study

Castillejo, A., Mata‐Balaguer, T., Guarinós, C., Castillejo, M. I., Martínez-Cantó, A., Barberá, V. M., Montenegro, P. C., Ochoa, E., Lázaro, R., Guillén‐Ponce, C., et al. (2009) · BMC Cancer · doi:10.1186/1471-2407-9-406 · Cited by 15

Genetic Approaches to Coronary Heart Disease

Cohen, J. C. (2006) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2006.06.046 · Cited by 16

A variant in the gene FUT9 is associated with susceptibility to placental malaria infection

Sikora, M., Ferrer-Admetlla, A., Laayouni, H., Menéndez, C., Mayor, A. G., Bardají, A., Sigaúque, B., Mandomando, I., Alonso, P. L., Bertranpetit, J., et al. (2009) · Human Molecular Genetics · doi:10.1093/hmg/ddp240 · Cited by 15

Novel genetic variants associated with brain functional networks in 18,445 adults from the UK Biobank

Foo, H., Thalamuthu, A., Jiang, J., Koch, F. C., Mather, K. A., Wen, W. X., & Sachdev, P. S. (2021) · Scientific Reports · doi:10.1038/s41598-021-94182-9 · Cited by 15

Variations in the uncoupling protein-3 gene are associated with specific obesity phenotypes

van Abeelen, A. F. M., de Krom, M., Hendriks, J., Grobbee, D. E., Adan, R. A. H., & van der Schouw, Y. T. (2008) · European Journal of Endocrinology · doi:10.1530/eje-07-0834 · Cited by 15

Genotyping, sequencing and analysis of 140,000 adults from the Mexico City Prospective StudyPreprint

Ziyatdinov, A., Torres, J., Alegre-Díaz, J., Backman, J., Mbatchou, J. S., Turner, M., Gaynor, S. M., Joseph, T., Zou, Y., Liu, D., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.06.26.495014 · Cited by 15

Periodicity significance testing with null-signal templates: reassessment of PTF’s SMBH binary candidates

Robnik, J., Bayer, A. E., Charisi, M., Haiman, Z., Lin, A., & Seljak, U. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae2220 · Cited by 14

A panel of 32 AIMs suitable for population stratification correction and global ancestry estimation in Mexican mestizos

Huerta‐Chagoya, A., Moreno-Macías, H., Fernández-López, J. C., Ordóñez-Sánchez, M. L., Rodríguez-Guillén, R., Contreras, A. V., Hidalgo‐Miranda, A., Alfaro-Ruíz, L. A., Salazar-Fernandez, E., Moreno‐Estrada, A., et al. (2019) · BMC Genetics · doi:10.1186/s12863-018-0707-7 · Cited by 14

Unsupervised discovery of ancestry-informative markers and genetic admixture proportions in biobank-scale datasets

Ko, S., Chu, B. B., Peterson, D., Okenwa, C., Papp, J. C., Alexander, D. H., Sobel, E. M., Zhou, H., & Lange, K. L. (2023) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2022.12.008 · Cited by 13

Faster model-based estimation of ancestry proportions

Santander, C. G., Refoyo-Martínez, A., & Meisner, J. (2024) · Peer Community Journal · doi:10.24072/pcjournal.503 · Cited by 15

Identification of QTL for seed coat colour and oil content in Brassica napus by association mapping using SSR markers

Qu, C., Hasan, M., Lu, K., Liu, L., Zhang, K., Fu, F., Wang, M., Liu, S., Bu, H., Wang, R., et al. (2014) · Canadian Journal of Plant Science · doi:10.4141/cjps2013-411 · Cited by 14

Genetic variants in the cocaine- and amphetamine-regulated transcript gene (CARTPT) and cocaine dependence

Lohoff, F. W., Bloch, P. J., Weller, A. E., Nall, A. H., Doyle, G. A. R., Buono, R. J., Ferraro, T. N., Kampman, K. M., Pettinati, H. M., Dackis, C. A., et al. (2008) · Neuroscience Letters · doi:10.1016/j.neulet.2008.05.073 · Cited by 13

Methods for Detecting and Correcting for Population Stratification

Edwards, T. L., & Gao, X. (2012) · Current Protocols in Human Genetics · doi:10.1002/0471142905.hg0122s73 · Cited by 15

ETHNOPRED: a novel machine learning method for accurate continental and sub-continental ancestry identification and population stratification correction

Hajiloo, M., Sapkota, Y., Mackey, J. R., Robson, P. J., Greiner, R., & Damaraju, S. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-61 · Cited by 14

WhoGEM: an admixture-based prediction machine accurately predicts quantitative functional traits in plants

Gentzbittel, L., Ben, C., Mazurier, M., Shin, M., Lorenz, T., Rickauer, M., Marjoram, P., Nuzhdin, S. V., & Tatarinova, T. V. (2019) · Genome biology · doi:10.1186/s13059-019-1697-0 · Cited by 15

Dense SNP association study for bipolar I disorder on chromosome 18p11 suggests two loci with excess paternal transmission

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Genes and osteoporosis

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