Hints of hidden heritability in GWAS

Finding the missing heritability of complex diseases

Manolio, T. A., Collins, F. S., Cox, N. J., Goldstein, D. B., Hindorff, L. A., Hunter, D. J., McCarthy, M. I., Ramos, E. M., Cardon, L. R., Chakravarti, A., et al. (2009) · Nature · doi:10.1038/nature08494 · Cited by 8,336

Common polygenic variation contributes to risk of schizophrenia and bipolar disorder

The International Schizophrenia Consortium (2009) · Nature · doi:10.1038/nature08185 · Cited by 4,821

Common SNPs explain a large proportion of the heritability for human height

Yang, J., Benyamin, B., McEvoy, B. P., Gordon, S., Henders, A. K., Nyholt, D. R., Madden, P. A., Heath, A. C., Martin, N. G., Montgomery, G. W., et al. (2010) · Nature Genetics · doi:10.1038/ng.608 · Cited by 4,446

XV.—The Correlation between Relatives on the Supposition of Mendelian Inheritance.

Fisher, R. A. (1919) · Transactions of the Royal Society of Edinburgh · doi:10.1017/s0080456800012163 · Cited by 4,387

Missing heritability and strategies for finding the underlying causes of complex disease

Eichler, E. E., Flint, J., Gibson, G. C., Kong, A., Leal, S. M., Moore, J. H., & Nadeau, J. H. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2809 · Cited by 1,728

The investigation of linkage between a quantitative trait and a marker locus

Haseman, J. K., & Elston, R. C. (1972) · Behavior Genetics · doi:10.1007/bf01066731 · Cited by 1,367

Estimation of effect size distribution from genome-wide association studies and implications for future discoveries

Park, J.-H., Wacholder, S., Gail, M. H., Peters, U., Jacobs, K. B., Chanock, S. J., & Chatterjee, N. (2010) · Nature Genetics · doi:10.1038/ng.610 · Cited by 678

The genetic interpretation of area under the ROC curve in genomic profiling

Wray, N. R., Yang, J., Goddard, M. E., & Visscher, P. M. (2010) · PLoS Genetics · doi:10.1371/journal.pgen.1000864 · Cited by 334

Harnessing the information contained within genome-wide association studies to improve individual prediction of complex disease risk

Evans, D. M., Visscher, P. M., & Wray, N. R. (2009) · Human Molecular Genetics · doi:10.1093/hmg/ddp295 · Cited by 297

Reaching new heights: insights into the genetics of human stature

Weedon, M. N., & Frayling, T. M. (2008) · Trends in Genetics · doi:10.1016/j.tig.2008.09.006 · Cited by 147

Genome-wide Association Study of Resistant Starch (RS) Phenotypes in a Barley Variety Collection

Shu, X., Backes, G., & Rasmussen, S. K. (2012) · Journal of Agricultural and Food Chemistry · doi:10.1021/jf3031875 · Cited by 33

An overview of recent technological developments in bovine genomics

Hossein‐Zadeh, N. G. (2024) · Veterinary and Animal Science · doi:10.1016/j.vas.2024.100382 · Cited by 36

Regional evaluation of childhood acute lymphoblastic leukemia genetic susceptibility loci among Japanese

Urayama, K. Y., Takagi, M., Kawaguchi, T., Matsuo, K., Tanaka, Y., Ayukawa, Y., Arakawa, Y., Hasegawa, D., Yuza, Y., Kaneko, T., et al. (2018) · Scientific Reports · doi:10.1038/s41598-017-19127-7 · Cited by 32

What have we learned from six years of GWAS in autoimmune diseases, and what is next?

Hu, X., & Daly, M. J. (2012) · Current Opinion in Immunology · doi:10.1016/j.coi.2012.09.001 · Cited by 34

Phenotype prediction based on genome-wide DNA methylation data

Wilhelm, T. E. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-193 · Cited by 31

Toward an integrative science of the developing human mind and brain: Focus on the developing cortex

Jernigan, T. L., Brown, T. T., Bartsch, H., & Dale, A. M. (2015) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2015.07.008 · Cited by 30

Genetic Risk Prediction: Individualized Variability in Susceptibility to Toxicants

Nebert, D. W., Zhang, G., & Vesell, E. S. (2013) · The Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-011112-140241 · Cited by 26

Unoccupied aerial systems discovered overlooked loci capturing the variation of entire growing period in maize

Adak, A., Murray, S. C., Anderson, S. L., Popescu, S., Malambo, L., Romay, M. C., & de León, N. (2021) · The Plant Genome · doi:10.1002/tpg2.20102 · Cited by 27

BiForce Toolbox: powerful high-throughput computational analysis of gene-gene interactions in genome-wide association studies

Gyenesei, A., Moody, J., Laiho, A., Semple, C. A. M., Haley, C., & Wei, W. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks550 · Cited by 30

Novel genetic loci associated HLA-B*08:01 positive myasthenia gravis

Varadé, J., Wang, N., Lim, C. K., Zhang, T., Zhang, Y., Liu, X., Piehl, F., Matell, R., Cao, H., Xu, X., et al. (2017) · Journal of Autoimmunity · doi:10.1016/j.jaut.2017.10.002 · Cited by 28

A Robust Model-free Approach for Rare Variants Association Studies Incorporating Gene-Gene and Gene-Environmental Interactions

Fan, R., & Lo, S. (2013) · PLoS ONE · doi:10.1371/journal.pone.0083057 · Cited by 27

Highly Polygenic Variation in Environmental Perception Determines Dauer Larvae Formation in Growing Populations of Caenorhabditis elegans

Green, J. W. M., Stastna, J. J., Orbidans, H. E., & Harvey, S. C. (2014) · PLoS ONE · doi:10.1371/journal.pone.0112830 · Cited by 26

Identifying a novel biological mechanism for alcohol addiction associated with circRNA networks acting as potential miRNA sponges

Vornholt, E., Drake, J. W., Mamdani, M., McMichael, G. O., Taylor, Z. N., Bacanu, S., Miles, M. F., & Vladimirov, V. I. (2021) · Addiction Biology · doi:10.1111/adb.13071 · Cited by 26

Controlling the joint local false discovery rate is more powerful than meta-analysis methods in joint analysis of summary statistics from multiple genome-wide association studies

Jiang, W., & Yu, W. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw690 · Cited by 23

A Polymorphism Affecting MYB Binding within the Promoter of the PDCD4 Gene is Associated with Severe Asthma in Children

Binia, A., Van Stiphout, N., Liang, L., Michel, S., Bhavsar, P. K., Fan Chung, K., Brightling, C. E., Barnes, P. J., Kabesch, M., Bush, A., et al. (2013) · Human Mutation · doi:10.1002/humu.22340 · Cited by 24

The current review of adolescent obesity: the role of genetic factors

Yılmaz, B., & Karadağ, M. G. (2020) · Journal of Pediatric Endocrinology and Metabolism · doi:10.1515/jpem-2020-0480 · Cited by 27

Genome-Wide Association Studies and Prediction of Normal Tissue Toxicity

West, C., Dunning, A. M., & Rosenstein, B. S. (2012) · Seminars in Radiation Oncology · doi:10.1016/j.semradonc.2011.12.007 · Cited by 24

A novel approach to genome-wide association analysis identifies genetic associations with primary biliary cholangitis and primary sclerosing cholangitis in Polish patients

Paziewska, A., Habior, A., Rogowska, A., Zych, W., Goryca, K., Karczmarski, J., Dąbrowska, M., Ambrożkiewicz, F., Walewska‐Zielecka, B., Krawczyk, M., et al. (2017) · BMC Medical Genomics · doi:10.1186/s12920-016-0239-9 · Cited by 24

A Novel QTL for Powdery Mildew Resistance in Nordic Spring Barley (Hordeum vulgare L. ssp. vulgare) Revealed by Genome-Wide Association Study

Bengtsson, T., Åhman, I. M., Manninen, O., Reitan, L., Christerson, T., Jensen, J. D., Krusell, L., Jahoor, A., & Orabi, J. (2017) · Frontiers in Plant Science · doi:10.3389/fpls.2017.01954 · Cited by 24

The Impact of Improved Microarray Coverage and Larger Sample Sizes on Future Genome‐Wide Association Studies

Lindquist, K. J., Jorgenson, E., Hoffmann, T. J., & Witte, J. S. (2013) · Genetic Epidemiology · doi:10.1002/gepi.21724 · Cited by 23

Is Life Law-Like?

Weiss, K. M., & Buchanan, A. V. (2011) · Genetics · doi:10.1534/genetics.111.131318 · Cited by 24

ClickGene: an open cloud-based platform for big pan-cancer data genome-wide association study, visualization and exploration

Bi, J.-H., Tong, Y.-F., Qiu, Z.-W., Yang, X.-F., Minna, J. D., Gazdar, A. F., & Song, K. (2019) · BioData Mining · doi:10.1186/s13040-019-0202-3 · Cited by 23

Genetics in liver disease: new concepts

Zimmer, V., & Lammert, F. (2011) · Current Opinion in Gastroenterology · doi:10.1097/mog.0b013e3283444862 · Cited by 23

Genotype-driven identification of a molecular network predictive of advanced coronary calcium in ClinSeq® and Framingham Heart Study cohorts

Oguz, C., Sen, S. K., Davis, A. R., Fu, Y., O’Donnell, C. J., & Gibbons, G. H. (2017) · BMC Systems Biology · doi:10.1186/s12918-017-0474-5 · Cited by 23

Deep mixed model for marginal epistasis detection and population stratification correction in genome-wide association studies

Wang, H., Yue, T., Yang, J., Wu, W., & Xing, E. P. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-3300-9 · Cited by 23

Atrial fibrillation and heart failure: intersecting populations, morbidities, and mortality

Dickinson, O., Chen, L. Y., & Francis, G. S. (2013) · Heart Failure Reviews · doi:10.1007/s10741-013-9409-4 · Cited by 22

The genetics of common kidney disease: a pathway toward clinical relevance

Drawz, P. E., & Sedor, J. R. (2011) · Nature Reviews Nephrology · doi:10.1038/nrneph.2011.85 · Cited by 22

Interpretation of Manhattan Plots and Other Outputs of Genome-Wide Association Studies

Wang, J., Yu, J., Lipka, A. E., & Zhang, Z. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-2237-7_5 · Cited by 22

Reproduction as a window for health in men

Belladelli, F., Muncey, W. W., & Eisenberg, M. L. (2023) · Fertility and Sterility · doi:10.1016/j.fertnstert.2023.01.014 · Cited by 22

Nuclear Genes Associated with Mitochondrial DNA Processes as Contributors to Parkinson's Disease Risk

Müller-Nedebock, A. C., van der Westhuizen, F. H., Kõks, S., & Bardien, S. (2021) · Movement Disorders · doi:10.1002/mds.28475 · Cited by 21

Systematic review of genome-wide association studies of abdominal aortic aneurysm

Singh, T. P., Field, M. A., Bown, M. J., Jones, G. T., & Golledge, J. (2021) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2021.05.001 · Cited by 22

Genome-wide association study in Alzheimer’s disease: a bibliometric and visualization analysis

Zhang, J., Wang, Y., Zhang, Y., & Yao, J. (2023) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2023.1290657 · Cited by 22

A novel variational Bayes multiple locusZ-statistic for genome-wide association studies with Bayesian model averaging

Logsdon, B. A., Carty, C. L., Reiner, A. P., Dai, J. Y., & Kooperberg, C. L. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts261 · Cited by 19

A human iPSC-array-based GWAS identifies a virus susceptibility locus in the NDUFA4 gene and functional variants

Han, Y., Tan, L., Zhou, T., Yang, L., Carrau, L., Lacko, L. A., Saeed, M., Zhu, J., Zhao, Z., Nilsson-Payant, B. E., et al. (2022) · Cell stem cell · doi:10.1016/j.stem.2022.09.008 · Cited by 19

Negative Affectivity, Political Contention, and Turnout: A Genopolitics Field Experiment

Settle, J. E., Dawes, C. T., Loewen, P. J., & Panagopoulos, C. (2016) · Political Psychology · doi:10.1111/pops.12379 · Cited by 19

Characterisation of Genome-Wide Association Epistasis Signals for Serum Uric Acid in Human Population Isolates

Wei, W., Hemani, G., Hicks, A. A., Vitart, V., Cabrera-Cardenas, C., Navarro, P., Huffman, J. E., Hayward, C., Knott, S., Rudan, I., et al. (2011) · PLoS ONE · doi:10.1371/journal.pone.0023836 · Cited by 18

Genetic underpinnings of lung function and COPD

Ranjan, A., Singh, A., Walia, G. K., SACHDEVA, M. P., & Gupta, V. (2019) · Journal of Genetics · doi:10.1007/s12041-019-1119-9 · Cited by 18

Heterogeneous ozone effects on the DNA methylome of bronchial cells observed in a crossover study

Bind, M., Rubin, D. B., Cárdenas, A., Dhingra, R., Ward-Caviness, C. K., Liu, Z., Mirowsky, J. E., Schwartz, J. D., Díaz-Sánchez, D., & Devlin, R. B. (2020) · Scientific Reports · doi:10.1038/s41598-020-72068-6 · Cited by 18

Functional and Structural Consequence of Rare Exonic Single Nucleotide Polymorphisms: One Story, Two Tales

Gu, W., Gurguis, C. I., Zhou, J. J., Zhu, Y., Ko, E.-A., Ko, J., Wang, T., & Zhou, T. (2015) · Genome Biology and Evolution · doi:10.1093/gbe/evv191 · Cited by 18

The Value of Statistical or Bioinformatics Annotation for Rare Variant Association With Quantitative Trait

Byrnes, A., Wu, M. C., Wright, F. A., Li, M., & Li, Y. (2013) · Genetic Epidemiology · doi:10.1002/gepi.21747 · Cited by 17

Game-theoretic probability combination with applications to resolving conflicts between statistical methods

Bickel, D. R. (2012) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2012.04.002 · Cited by 18

Human genome epidemiology, progress and future

Shen, H., & Guangfu, J. (2013) · Journal of Biomedical Research · doi:10.7555/jbr.27.20130040 · Cited by 18

The Novel ASIC2 Locus Is Associated with Severe Gingival Inflammation

Zhang, S., Divaris, K., MOSS, K. L., Yu, N., Barros, S. P., Marchesan, J. T., Morelli, T., Agler, C. S., Kim, S. J., Wu, D., et al. (2016) · JDR Clinical & Translational Research · doi:10.1177/2380084416645290 · Cited by 17

Proteinarium: Multi-sample protein-protein interaction analysis and visualization tool

Armanious, D., Schuster, J. S., Tollefson, G. A., Agudelo, A., DeWan, A. T., Istrail, S., Padbury, J. F., & Uzun, A. (2020) · Genomics · doi:10.1016/j.ygeno.2020.07.028 · Cited by 16

Targeted Sequencing and Meta-Analysis of Preterm Birth

Uzun, A., Schuster, J. S., McGonnigal, B., Schorl, C., DeWan, A. T., & Padbury, J. F. (2016) · PLoS ONE · doi:10.1371/journal.pone.0155021 · Cited by 17

Correction: Heritability and Genetic Correlations Explained by Common SNPs for Metabolic Syndrome Traits

Vattikuti, S., Guo, J., & Chow, C. C. (2012) · PLoS Genetics · doi:10.1371/annotation/61bb5924-6688-4ee5-a37f-d48aa09ad66a · Cited by 17

A Set-Based Mixed Effect Model for Gene-Environment Interaction and Its Application to Neuroimaging Phenotypes

Wang, C., Sun, J., Guillaume, B., Ge, T., Hibar, D. P., Greenwood, C. M. T., Qiu, A., & Initiative, T. A. D. N. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00191 · Cited by 16

Deep sequencing reveals a DAP1 regulatory haplotype that potentiates autoimmunity in systemic lupus erythematosus

Raj, P., Song, R., Zhu, H., Riediger, L., Jun, D., Liang, C., Arana, C., Zhang, B., Gao, Y., Wakeland, B., et al. (2020) · Genome biology · doi:10.1186/s13059-020-02184-z · Cited by 16

Effectiveness of Shrinkage and Variable Selection Methods for the Prediction of Complex Human Traits using Data from Distantly Related Individuals

Berger, S., Pérez‐Rodríguez, P., Veturi, Y., Simianer, H., & Campos, G. D. L. (2015) · Annals of Human Genetics · doi:10.1111/ahg.12099 · Cited by 16

The Identification of Pathway Markers in Intracranial Aneurysm Using Genome-Wide Association Data from Two Different Populations

Bakır-Güngör, B., & Sezerman, O. U. (2013) · PLoS ONE · doi:10.1371/journal.pone.0057022 · Cited by 16

Complex adaptive responses during antagonistic coevolution between Tribolium castaneum and its natural parasite Nosema whitei revealed by multiple fitness components

Bérénos, C., Schmid‐Hempel, P., & Wegner, K. M. (2012) · BMC Evolutionary Biology · doi:10.1186/1471-2148-12-11 · Cited by 16

Human genome-microbiome interaction: metagenomics frontiers for the aetiopathology of autoimmune diseases

Gündoğdu, A., & Nalbantoglu, U. (2017) · Microbial Genomics · doi:10.1099/mgen.0.000112 · Cited by 16

Genetic variation across RNA metabolism and cell death gene networks is implicated in the semantic variant of primary progressive aphasia

Bonham, L. W., Steele, N. Z. R., Karch, C. M. M., Broce, I., Geier, E. G., Wen, N., Momeni, P., Hardy, J., Miller, Z., Gorno‐Tempini, M. L., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-46415-1 · Cited by 15

Parametric Estimation of the Local False Discovery Rate for Identifying Genetic Associations

Yang, Y., Aghababazadeh, F. A., & Bickel, D. R. (2012) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2012.140 · Cited by 14

A statistical boosting framework for polygenic risk scores based on large-scale genotype data

Klinkhammer, H., Staerk, C., Maj, C., Krawitz, P. M., & Mayr, A. J. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2022.1076440 · Cited by 16

From Interaction to Co-Association —A Fisher r-To-z Transformation-Based Simple Statistic for Real World Genome-Wide Association Study

Yuan, Z., Liu, H., Zhang, X., Li, F., Zhao, J., Zhang, F., & Xue, F. (2013) · PLoS ONE · doi:10.1371/journal.pone.0070774 · Cited by 13

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

Multisite haplotype on cattle chromosome 3 is associated with quantitative trait locus effects on lactation traits

Cohen, M. W., Donthu, R., Larkin, D. M., Kumar, C. G., Rodriguez‐Zas, S. L., Andropolis, K. E., Oliveira, R., & Lewin, H. A. (2011) · Physiological Genomics · doi:10.1152/physiolgenomics.00253.2010 · Cited by 13

Redefining disease

Bell, J. I. (2010) · Clinical Medicine · doi:10.7861/clinmedicine.10-6-584 · Cited by 12

A Clinical Risk Model for Personalized Screening and Prevention of Breast Cancer

Eriksson, M., Czene, K., Vachon, C. M., Conant, E. F., & Hall, P. N. (2023) · Cancers · doi:10.3390/cancers15123246 · Cited by 14

Association Mapping: A New Paradigm for Dissection of Complex Traits in Crops

Pasam, R., & Sharma, R. K. (2014) · Springer eBooks · doi:10.1007/978-81-322-1880-7_1 · Cited by 12

On Epistasis: A Methodological Review for Detecting Gene-Gene Interactions Underlying Various Types of Phenotypic Traits

Li, M., Lou, X.-Y., & Lu, Q. (2012) · Recent Patents on Biotechnology · doi:10.2174/1872208311206030230 · Cited by 12

Association of serum levels of AngII, KLK1, and ACE/KLK1 polymorphisms with acute myocardial infarction induced by coronary artery stenosis

Dai, S.-H., Li, J.-F., Feng, J.-B., Li, R.-J., Li, C.-B., Li, Z., Zhang, Y., & Li, D.-Q. (2016) · Journal of the Renin-Angiotensin-Aldosterone System · doi:10.1177/1470320316655037 · Cited by 11

Fibrate Pharmacogenomics: Expanding Past the Genome

House, J. S., & Motsinger‐Reif, A. A. (2020) · Pharmacogenomics · doi:10.2217/pgs-2019-0140 · Cited by 12

Association of SHMT1, MAZ, ERG, and L3MBTL3 Gene Polymorphisms with Susceptibility to Multiple Sclerosis

Mehrabani, S. Z. N., Shushizadeh, M. H., Abazari, M. F., Aleagha, M. N., Ardalan, A., Abdollahzadeh, R., & Azarnezhad, A. (2018) · Biochemical Genetics · doi:10.1007/s10528-018-9894-1 · Cited by 11

Direct‐to‐consumer genetic testing in China and its role in GWAS discovery and replication

Kang, K., Sun, X., Wang, L., Yao, X., Tang, S., Deng, J., Wu, X., Yang, C., & Chen, G. (2020) · Quantitative Biology · doi:10.1007/s40484-020-0209-2 · Cited by 11

Stress-sensitive neurosignalling in depression: an integrated network biology approach to candidate gene selection for genetic association analysis

van Eekelen, J. A. M., Ellis, J. A., Pennell, C. E., Saffery, R., Mattes, E., Craig, J. M., & Olsson, C. A. (2012) · Mental Illness · doi:10.4081/mi.2012.e21 · Cited by 10

A Bioinformatics Approach to Preterm Birth

Uzun, A., Sharma, S., & Padbury, J. F. (2012) · American Journal of Reproductive Immunology · doi:10.1111/j.1600-0897.2012.01122.x · Cited by 10

Whole Genome Sequencing Identifies CRISPLD2 as a Lung Function Gene in Children With Asthma

Kachroo, P., Hecker, J., Chawes, B., Ahluwalia, T. S., Cho, M. H., Qiao, D., Kelly, R. S., Chu, S. H., Virkud, Y. V., Huang, M., et al. (2019) · CHEST Journal · doi:10.1016/j.chest.2019.08.2202 · Cited by 10

Bench Research Informed by GWAS Results

Kondratyev, N. V., Алфимова, М. В., Golov, A. K., & Голимбет, В. Е. (2021) · Cells · doi:10.3390/cells10113184 · Cited by 11

A GWAS assessment of the contribution of genomic imprinting to the variation of body mass index in mice

Hu, Y., Rosa, G. J. M., & Gianola, D. (2015) · BMC Genomics · doi:10.1186/s12864-015-1721-z · Cited by 9

Genome-Wide Association Studies and Heritability Estimation in the Functional Genomics Era

Carpio, D. P. D., Lozano, R. D., Wolfe, M. D., & Jannink, J. (2018) · Population genomics · doi:10.1007/13836_2018_12 · Cited by 8

Applications and challenges of biotechnology in oil palm breeding

Soh, A. C. (2018) · IOP Conference Series Earth and Environmental Science · doi:10.1088/1755-1315/183/1/012002 · Cited by 9

Genetic association scan of 32 osteoarthritis susceptibility genes identified TP63 associated with an endemic osteoarthritis, Kashin-Beck disease

Cheng, B., Liang, C., Yang, X., Li, P., Liu, L., Cheng, S., Jia, Y., Zhang, L., Ma, M., Qi, X., et al. (2021) · Bone · doi:10.1016/j.bone.2021.115997 · Cited by 9

inPHAP: Interactive visualization of genotype and phased haplotype data

Jäger, G., Peltzer, A., & Nieselt, K. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-200 · Cited by 9

Adult stem cells in psychiatric disorders – New discoveries in peripheral blood

Kucharska–Mazur, J., Jabłoński, M., Misiak, B., Frydecka, D., RYBAKOWSKI, J. K., Ratajczak, M. Z., & Samochowiec, J. (2017) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2017.04.005 · Cited by 9

Influence of SNP * SNP interaction on BMI in E uropean A merican adolescents: findings from the N ational L ongitudinal S tudy of A dolescent H ealth

Young, K. L., Graff, M., North, K. E., Richardson, A. S., Bradfield, J. P., Grant, S. F. A., Lange, L. A., Lange, E. M., Harris, K. M., & Gordon‐Larsen, P. (2015) · Pediatric Obesity · doi:10.1111/ijpo.12026 · Cited by 9

Integrative Bayesian variable selection with gene-based informative priors for genome-wide association studies

Zhang, X., Xue, F., Liu, H., Zhu, D., Peng, B., Wiemels, J. L., & Yang, X. (2014) · BMC Genetics · doi:10.1186/s12863-014-0130-7 · Cited by 8

Another Round of “Clue” to Uncover the Mystery of Complex Traits

Verma, S. S., & Ritchie, M. D. (2018) · Genes · doi:10.3390/genes9020061 · Cited by 9

Genome wide association studies, next generation sequencing and their application in animal breeding and genetics: a review.

Khanzadeh, H., Hossein‐Zadeh, N. G., & Ghovvati, S. (2020) · Iranian journal of applied animal science · Cited by 8

Incorporating regulatory interactions into gene-set analyses for GWAS data: A controlled analysis with the MAGMA tool

Groenewoud, D., Shye, A., & Elkon, R. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009908 · Cited by 8

The genetic interacting landscape of 63 candidate genes in Major Depressive Disorder: an explorative study

Lekman, M., Hössjer, O., Andrews, P. C., Källberg, H., Uvehag, D., Charney, D. S., Manji, H. K., Rush, A. J., McMahon, F. J., Moore, J. H., et al. (2014) · BioData Mining · doi:10.1186/1756-0381-7-19 · Cited by 8

Non-coding RNAs: could they be the answer?

Costa, F. F. (2010) · Briefings in Functional Genomics · doi:10.1093/bfgp/elq029 · Cited by 9

Immunogenetics and the Mapping of Immunological Functions

Lamont, S. J., Dekkers, J. C. M., & Zhou, H. (2013) · Elsevier eBooks · doi:10.1016/b978-0-12-396965-1.00011-x · Cited by 8

A Philosophical Perspective on Evolutionary Systems Biology

O’Malley, M. A., Soyer, O. S., & Siegal, M. L. (2015) · Biological Theory · doi:10.1007/s13752-015-0202-6 · Cited by 11

Genetic Polymorphisms Associated with Fetal Hemoglobin (HbF) Levels and F-Cell Numbers: A Systematic Review of Genome-Wide Association Studies

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The Promise and Challenges of Integrating Biological and Prevention Sciences: A Community-Engaged Model for the Next Generation of Translational Research

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