A Unified Approach to Genotype Imputation and Haplotype-Phase Inference for Large Data Sets of Trios and Unrelated Individuals

A tutorial on hidden Markov models and selected applications in speech recognition

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Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls

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A New Statistical Method for Haplotype Reconstruction from Population Data

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A second generation human haplotype map of over 3.1 million SNPs

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Rapid and Accurate Haplotype Phasing and Missing-Data Inference for Whole-Genome Association Studies By Use of Localized Haplotype Clustering

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A new multipoint method for genome-wide association studies by imputation of genotypes

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Genome-wide association defines more than 30 distinct susceptibility loci for Crohn's disease

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A Fast and Flexible Statistical Model for Large-Scale Population Genotype Data: Applications to Inferring Missing Genotypes and Haplotypic Phase

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Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes

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A high-resolution recombination map of the human genome

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Linkage disequilibrium and haplotype block patterns in popcorn populations

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Gene‐level associations in suicide attempter families show overrepresentation of synaptic genes and genes differentially expressed in brain development

Sokolowski, M., Wasserman, J., & Wasserman, D. (2018) · American Journal of Medical Genetics Part B: Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32694 · Cited by 30

Giraffe lineages are shaped by major ancient admixture events

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Strategies for genotype imputation in composite beef cattle

Chud, T. C. S., Ventura, R. V., Schenkel, F. S., Carvalheiro, R., Buzanskas, M. E., Rosa, J. O., Mudadu, M. A., da Silva, M. V. G. B., Mokry, F. B., Marcondes, C. R., et al. (2015) · BMC Genetics · doi:10.1186/s12863-015-0251-7 · Cited by 31

Genome‐Wide Association Analysis of Imputed Rare Variants: Application to Seven Common Complex Diseases

Mägi, R., Asimit, J. L., Day-Williams, A., Zeggini, E., & Morris, A. P. (2012) · Genetic Epidemiology · doi:10.1002/gepi.21675 · Cited by 30

Power of Family‐Based Association Designs to Detect Rare Variants in Large Pedigrees Using Imputed Genotypes

Saad, M., & Wijsman, E. M. (2013) · Genetic Epidemiology · doi:10.1002/gepi.21776 · Cited by 31

A Network-Based Kernel Machine Test for the Identification of Risk Pathways in Genome-Wide Association Studies

Freytag, S., Manitz, J., Schlather, M., Kneib, T., Amos, C. I., Risch, A., Chang-Claude, J. C., Heinrich, J., & Bickeböller, H. (2013) · Human Heredity · doi:10.1159/000357567 · Cited by 29

A New Genotype Imputation Method with Tolerance to High Missing Rate and Rare Variants

Yang, Y., Wang, Q., Chen, Q., Liao, R., Zhang, X., Yang, H., Zheng, Y., Zhang, Z., & Pan, Y. (2014) · PLoS ONE · doi:10.1371/journal.pone.0101025 · Cited by 31

Impact of Inherited Genetic Variants Associated With Lipid Profile, Hypertension, and Coronary Artery Disease on the Risk of Intracranial and Abdominal Aortic Aneurysms

Hof, F. N. G. V. ’., Ruigrok, Y. M., Baas, A. F., Kiemeney, L. A. L. M., Vermeulen, S. H. H. M., Uitterlinden, A. G., Albert Hofman, D., Rivadeneira, F., Rinkel, G. J. E., & de Bakker, P. I. W. (2013) · Circulation Cardiovascular Genetics · doi:10.1161/circgenetics.113.000022 · Cited by 30

Genetic variation of major histocompatibility complex (MHC) in wild Red Junglefowl (Gallus gallus)

Nguyen‐Phuc, H., Fulton, J. E., & Berres, M. E. (2016) · Poultry Science · doi:10.3382/ps/pev364 · Cited by 33

Testing Rare-Variant Association without Calling Genotypes Allows for Systematic Differences in Sequencing between Cases and Controls

Hu, Y., Liao, P., Johnston, H. R., Allen, A. S., & Satten, G. A. (2016) · PLoS Genetics · doi:10.1371/journal.pgen.1006040 · Cited by 35

Genome-wide association mapping for milk fat composition and fine mapping of a QTL for de novo synthesis of milk fatty acids on bovine chromosome 13

Olsen, H. G., Knutsen, T. M., Köhler, A., Svendsen, M., Gidskehaug, L., Grove, H., Nome, T., Sodeland, M., Sundsaasen, K. K., Kent, M., et al. (2017) · Genetics Selection Evolution · doi:10.1186/s12711-017-0294-5 · Cited by 30

Challenges in funding and developing genomic software: roots and remedies

Siepel, A. C. (2019) · Genome biology · doi:10.1186/s13059-019-1763-7 · Cited by 28

Comprehensive genotyping of a Brazilian cassava (Manihot esculenta Crantz) germplasm bank: insights into diversification and domestication

Ogbonna, A. C., de Andrade, L. R. B., Mueller, L. A., de Oliveira, E. J., & Bauchet, G. J. (2021) · Theoretical and Applied Genetics · doi:10.1007/s00122-021-03775-5 · Cited by 31

Genetic diversity in global chicken breeds in relation to their genetic distances to wild populations

Malomane, D. K., Weigend, S., Schmitt, A. O., Weigend, A., Reimer, C., & Simianer, H. (2021) · Genetics Selection Evolution · doi:10.1186/s12711-021-00628-z · Cited by 34

Tandem repeat sequence variation as causative Cis-eQTLs for protein-coding gene expression variation: The case of CSTB

Borel, C., Migliavacca, E., Letourneau, A., Gagnebin, M., Béna, F., Sailani, M. R., Dermitzakis, E. T., Sharp, A. J., & Antonarakis, S. E. (2012) · Human Mutation · doi:10.1002/humu.22115 · Cited by 29

Population Structure With Localized Haplotype Clusters

Browning, S. R., & Weir, B. S. (2010) · Genetics · doi:10.1534/genetics.110.116681 · Cited by 30

Elevated risk of invasive group A streptococcal disease and host genetic variation in the human leucocyte antigen locus

Parks, T., Elliott, K. S., Lamagni, T. L., Auckland, K., Mentzer, A. J., Guy, R. L., Cartledge, D., Strakova, L., Connor, D. O., Pollard, A. J., et al. (2019) · Genes and Immunity · doi:10.1038/s41435-019-0082-z · Cited by 29

AlphaFamImpute: high-accuracy imputation in full-sib families from genotype-by-sequencing data

Whalen, A., Gorjanc, G., & Hickey, J. M. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa499 · Cited by 29

Genome‐wide association studies identify locus on 6p21 influencing lung function in the K orean population

Kim, W. J., Lee, M. K., Shin, C., Cho, N. H., Lee, S. D., Oh, Y., & Sung, J. (2014) · Respirology · doi:10.1111/resp.12230 · Cited by 29

Association between multiple sclerosis risk-associated SNPs and relapse and disability – a prospective cohort study

Lin, R., Taylor, B. V., Simpson, S., Charlesworth, J., Ponsonby, A., Pittas, F., Dwyer, T., & van der Mei, I. A. F. (2013) · Multiple Sclerosis Journal · doi:10.1177/1352458513496882 · Cited by 29

Leveraging reads that span multiple single nucleotide polymorphisms for haplotype inference from sequencing data

Yang, W.-Y., Hormozdiari, F., Wang, Z., He, D., Paşaniuc, B., & Eskin, E. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt386 · Cited by 31

FTO association and interaction with time spent sitting

Klimentidis, Y. C., Arora, A., Chougule, A., Zhou, J. J., & Raichlen, D. A. (2015) · International Journal of Obesity · doi:10.1038/ijo.2015.190 · Cited by 29

Genetic architecture dissection by genome-wide association analysis reveals avian eggshell ultrastructure traits

Duan, Z., Sun, C., Shen, M., Wang, K., Yang, N., Zheng, J., & Xu, G. (2016) · Scientific Reports · doi:10.1038/srep28836 · Cited by 30

Associations between a fetal imprinted gene allele score and late pregnancy maternal glucose concentrations

Petry, C. J., Mooslehner, K. A., Prentice, P. M., Hayes, M. G., Nodzenski, M., Scholtens, D. M., Hughes, I. A., Acerini, C. L., Ong, K. K., Lowe, W. L., et al. (2017) · Diabetes & Metabolism · doi:10.1016/j.diabet.2017.03.002 · Cited by 28

Optimizing Selection of the Reference Population for Genotype Imputation From Array to Sequence Variants

Butty, A. M., Sargolzaei, M., Miglior, F., Stothard, P., Schenkel, F. S., Gredler, B., & Baes, C. F. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00510 · Cited by 29

Evaluation of sequencing strategies for whole-genome imputation with hybrid peeling

Ros‐Freixedes, R., Whalen, A., Gorjanc, G., Mileham, A. J., & Hickey, J. M. (2020) · Genetics Selection Evolution · doi:10.1186/s12711-020-00537-7 · Cited by 29

A new mechanism for a familiar mutation – bovine DGAT1 K232A modulates gene expression through multi-junction exon splice enhancement

Fink, T., Lopdell, T., Tiplady, K., Handley, R. R., Johnson, T., Spelman, R. J., Davis, S. R., Snell, R. G., & Littlejohn, M. D. (2020) · BMC Genomics · doi:10.1186/s12864-020-07004-z · Cited by 29

Genome-wide association studies uncover genes associated with litter traits in the pig

Zhao, Y., Gao, G., Zhou, Y., Guo, C., Li, B., El-Ashram, S. A., & Li, Z. (2022) · animal · doi:10.1016/j.animal.2022.100672 · Cited by 34

Balancing Selection on a Regulatory Region Exhibiting Ancient Variation That Predates Human–Neandertal Divergence

Gökçümen, Ö., Zhu, Q., Mulder, L. C. F., Iskow, R. C., Austermann, C., Scharer, C. D., Raj, T., Boss, J. M., Sunyaev, S. R., Price, A. L., et al. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003404 · Cited by 29

Genome-Wide Association Study Identifies Phospholipase C zeta 1 (PLCz1) as a Stallion Fertility Locus in Hanoverian Warmblood Horses

Schrimpf, R., Dierks, C., Martinsson, G., Sieme, H., & Distl, O. (2014) · PLoS ONE · doi:10.1371/journal.pone.0109675 · Cited by 29

Increased power of mixed models facilitates association mapping of 10 loci for metabolic traits in an isolated population

Kenny, E. E., Kim, M., Gusev, A., Lowe, J. K., Salit, J., Smith, J. G., Kovvali, S., Kang, H. M., Newton‐Cheh, C., Daly, M. J., et al. (2010) · Human Molecular Genetics · doi:10.1093/hmg/ddq510 · Cited by 27

Genomic relationships based on X chromosome markers and accuracy of genomic predictions with and without X chromosome markers

Su, G., Guldbrandtsen, B., Aamand, G. P., Strandén, I., & Lund, M. S. (2014) · Genetics Selection Evolution · doi:10.1186/1297-9686-46-47 · Cited by 29

The development of genomics applied to dairy breeding

da Silva, M. V. G. B., Santos, D. J. D. A., Boison, S. A., Utsunomiya, A. T. H., do Carmo, A. S., Sonstegard, T. S., Cole, J. B., & Tassell, C. P. V. (2014) · Livestock Science · doi:10.1016/j.livsci.2014.05.017 · Cited by 29

Association between common alcohol dehydrogenase gene (ADH) variants and schizophrenia and autism

Zuo, L., Wang, K., Zhang, X.-Y., Pan, X., Wang, G., Tan, Y., Zhong, C., Krystal, J. H., State, M. W., Zhang, H., et al. (2013) · Human Genetics · doi:10.1007/s00439-013-1277-4 · Cited by 29

The Valdostana goat: a genome-wide investigation of the distinctiveness of its selective sweep regions

Talenti, A., Bertolini, F., Pagnacco, G., Pilla, F., Ajmone‐Marsan, P., Rothschild, M. F., & Crepaldi, P. (2017) · Mammalian Genome · doi:10.1007/s00335-017-9678-7 · Cited by 28

Inference Attacks against Kin Genomic Privacy

Ayday, E., & Humbert, M. (2017) · IEEE Security & Privacy · doi:10.1109/msp.2017.3681052 · Cited by 29

Modeling copy number variation in the genomic prediction of maize hybrids

Lyra, D. H., Galli, G., Alves, F. C., Granato, Í. S. C., Vidotti, M. S., Sousa, M. B. E., Morosini, J. S., Crossa, J. L., & Fritsche‐Neto, R. (2018) · Theoretical and Applied Genetics · doi:10.1007/s00122-018-3215-2 · Cited by 29

A splice donor variant in CCDC189 is associated with asthenospermia in Nordic Red dairy cattle

Iso‐Touru, T., Wurmser, C., Venhoranta, H., Hiltpold, M., Savolainen, T., Sironen, A. I., Fischer, K., Flisikowski, K., Fries, R., Vicente‐Carrillo, A., et al. (2019) · BMC Genomics · doi:10.1186/s12864-019-5628-y · Cited by 29

In search of rare variants: Preliminary results from whole genome sequencing of 1,325 individuals with psychophysiological endophenotypes

Vrieze, S. I., Malone, S. M., Vaidyanathan, U., Kwong, A. M., Kang, H. M., Zhan, X., Flickinger, M., Irons, D. E., Jun, G., Locke, A. E., et al. (2014) · Psychophysiology · doi:10.1111/psyp.12350 · Cited by 30

Sparse single-step method for genomic evaluation in pigs

Ostersen, T., Christensen, O. F., Madsen, P., & Henryon, M. A. (2016) · Genetics Selection Evolution · doi:10.1186/s12711-016-0227-8 · Cited by 28

zanardi : an open-source pipeline for multiple-species genomic analysis of SNP array data

Marras, G., Rossoni, A., Schwarzenbacher, H., Biffani, S., Biscarini, F., & Nicolazzi, E. L. (2016) · Animal Genetics · doi:10.1111/age.12485 · Cited by 28

Comparative selection signature analyses identify genomic footprints in Reggiana cattle, the traditional breed of the Parmigiano-Reggiano cheese production system

Bertolini, F., Schiavo, G., Bovo, S., Sardina, M. T., Mastrangelo, S., Dall’Olio, S., Portolano, B., & Fontanesi, L. (2020) · animal · doi:10.1017/s1751731119003318 · Cited by 28

Disentangling group specific QTL allele effects from genetic background epistasis using admixed individuals in GWAS: An application to maize flowering

Rio, S., Mary‐Huard, T., Moreau, L., Bauland, C., Palaffre, C., Madur, D., Combes, V., & Charcosset, A. (2020) · PLoS Genetics · doi:10.1371/journal.pgen.1008241 · Cited by 29

The complex pattern of genetic associations of leprosy with HLA class I and class II alleles can be reduced to four amino acid positions

Dallmann-Sauer, M., Fava, V. M., Gzara, C., Orlova, M., Thuc, N. V., Thai, V. H., Alcaïs, A., Abel, L., Cobat, A., & Schurr, E. (2020) · PLoS Pathogens · doi:10.1371/journal.ppat.1008818 · Cited by 28

Detection of selection signatures in farmed coho salmon (Oncorhynchus kisutch) using dense genome-wide information

López, M. E., Cádiz, M. I., Rondeau, E. B., Koop, B. F., & Yañez, J. M. (2021) · Scientific Reports · doi:10.1038/s41598-021-86154-w · Cited by 29

Accuracies of genomic predictions for disease resistance of striped catfish to Edwardsiella ictaluri using artificial intelligence algorithms

Vu, N. T., Phúc, T. H., Oanh, K. T. P., Sáng, N. V., Trang, T. T., & Nguyen, N. H. (2021) · G3 Genes Genomes Genetics · doi:10.1093/g3journal/jkab361 · Cited by 27

A New Genomewide Association Meta-Analysis of Alcohol Dependence

Zuo, L., Tan, Y., Zhang, X., Wang, X., Krystal, J. H., Tabakoff, B., Zhong, C., & Luο, X. (2015) · Alcoholism Clinical and Experimental Research · doi:10.1111/acer.12786 · Cited by 28

Examining Markers in 8q24 to Explain Differences in Evidence for Association With Cleft Lip With/Without Cleft Palate Between A sians and E uropeans

Murray, T., Taub, M. A., Ruczinski, I., Scott, A. F., Hetmanski, J. B., Schwender, H. R., Patel, P. J., Zhang, T. X., Munger, R. G., Wilcox, A. J., et al. (2012) · Genetic Epidemiology · doi:10.1002/gepi.21633 · Cited by 28

Accuracy of imputation using the most common sires as reference population in layer chickens

Heidaritabar, M., Calus, M. P. L., Vereijken, A., Groenen, M. A. M., & Bastiaansen, J. W. M. (2015) · BMC Genetics · doi:10.1186/s12863-015-0253-5 · Cited by 28

Low-Pass Genome-Wide Sequencing and Variant Inference Using Identity-by-Descent in an Isolated Human Population

Gusev, A., Shah, M. J., Kenny, E. E., Ramachandran, A., Lowe, J. K., Salit, J., Lee, C.-M., Levandowsky, E., Weaver, T., Doan, Q. C., et al. (2011) · Genetics · doi:10.1534/genetics.111.134874 · Cited by 27

Genome-wide association study identifies new disease loci for isolated clubfoot

Zhang, T.-X., Haller, G., Lin, P., Alvarado, D. M., Hecht, J. T., Blanton, S. H., Richards, B. S., Rice, J. P., Dobbs, M. B., & Gurnett, C. A. (2014) · Journal of Medical Genetics · doi:10.1136/jmedgenet-2014-102303 · Cited by 28

Including overseas performance information in genomic evaluations of Australian dairy cattle

Haile‐Mariam, M., Pryce, J. E., Schrooten, C., & Hayes, B. J. (2015) · Journal of Dairy Science · doi:10.3168/jds.2014-8785 · Cited by 28

Short communication: Validation of genomic breeding value predictions for feed intake and feed efficiency traits

Pryce, J. E., González-Recio, Ó., Thornhill, J. B., Marett, L. C., Wales, W. J., Coffey, M. P., de Haas, Y., Veerkamp, R. F., & Hayes, B. J. (2013) · Journal of Dairy Science · doi:10.3168/jds.2013-7376 · Cited by 28

Polyceraty (multi‐horns) in Damara sheep maps to ovine chromosome 2

Greyvenstein, O., Reich, C. M., van Marle-Köster, E., Riley, D. G., & Hayes, B. J. (2016) · Animal Genetics · doi:10.1111/age.12411 · Cited by 28

Characterising private and shared signatures of positive selection in 37 Asian populations

Liu, X., Lu, D., Saw, W., Shaw, P. J., Wangkumhang, P., Ngamphiw, C., Fucharoen, S., Lert-itthiporn, W., Chin-inmanu, K., Chau, T. N. B., et al. (2017) · European Journal of Human Genetics · doi:10.1038/ejhg.2016.181 · Cited by 28

Investigation of six testicular germ cell tumor susceptibility genes suggests a parent-of-origin effect in SPRY4

Karlsson, R., Andreassen, K. E., Kristiansen, W., Aschim, E. L., Bremnes, R. M., Dahl, O., Fosså, S. D., Klepp, O., Langberg, C. W., Solberg, A., et al. (2013) · Human Molecular Genetics · doi:10.1093/hmg/ddt188 · Cited by 27

An across-breed genome wide association analysis of susceptibility to paratuberculosis in dairy cattle

Sallam, A. M., Zare, Y., ALPAY, F., Shook, G. E., Collins, M. T., Alsheikh, S., Sharaby, M. A., & Kirkpatrick, B. W. (2017) · Journal of Dairy Research · doi:10.1017/s0022029916000807 · Cited by 27

Strategies for phasing and imputation in a population isolate

Herzig, A. F., Nutile, T., Babron, M., Ciullo, M., Bellenguez, C., & Leutenegger, A. (2018) · Genetic Epidemiology · doi:10.1002/gepi.22109 · Cited by 30

Rare Variants Imputation in Admixed Populations: Comparison Across Reference Panels and Bioinformatics Tools

Sariya, S., Lee, J. H., Mayeux, R. P., Vardarajan, B. N., Reyes‐Dumeyer, D., Manly, J. J., Brickman, A. M., Lantigua, R., Medrano, M. Z., Jiménez‐Velázquez, I. Z., et al. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00239 · Cited by 27

Adapting Genotyping-by-Sequencing and Variant Calling for Heterogeneous Stock Rats

Gileta, A. F., Gao, J., Chitre, A. S., Bimschleger, H. V., Pierre, C. L. S., Gopalakrishnan, S., & Palmer, A. A. (2020) · G3 Genes Genomes Genetics · doi:10.1534/g3.120.401325 · Cited by 25

Genomic insight into the divergence and adaptive potential of a forgotten landrace G. hirsutum L. purpurascens

Nazir, M. F., He, S., Ahmed, H., Sarfraz, Z., Jia, Y., Li, H., Sun, G., Iqbal, M. S., Pan, Z., & Du, X. (2021) · Journal of genetics and genomics/Journal of Genetics and Genomics · doi:10.1016/j.jgg.2021.04.009 · Cited by 27

Dissection of the genetic basis of genotype-by-environment interactions for grain yield and main agronomic traits in Iranian bread wheat landraces and cultivars

Alipour, H., Abdi, H., Rahimi, Y., & Bihamta, M. R. (2021) · Scientific Reports · doi:10.1038/s41598-021-96576-1 · Cited by 30

Identification of New Candidate Genes Related to Semen Traits in Duroc Pigs through Weighted Single-Step GWAS

Zhang, X., Lin, Q., Liao, W., Zhang, W., Li, T., Li, J., Zhang, Z., Huang, X., & Zhang, H. (2023) · Animals · doi:10.3390/ani13030365 · Cited by 27

Breed identification using breed-informative SNPs and machine learning based on whole genome sequence data and SNP chip data

Zhao, P., Wang, D., Teng, J., Yang, C., Zhang, X., Wei, X., & Zhang, Q. (2023) · Journal of Animal Science and Biotechnology/Journal of animal science and biotechnology · doi:10.1186/s40104-023-00880-x · Cited by 28

Systematic assessment of imputation performance using the 1000 Genomes reference panels

Liu, Q., Cirulli, E. T., Han, Y., Yao, S., Liu, S., & Zhu, Q. (2014) · Briefings in Bioinformatics · doi:10.1093/bib/bbu035 · Cited by 27

Whole-genome association study of fatty acid composition in a diverse range of beef cattle breeds

Kelly, M., Tume, R. K., Fortes, M. R. S., & Thompson, J. M. (2014) · Journal of Animal Science · doi:10.2527/jas.2013-6901 · Cited by 27

High resolution HLA haplotyping by imputation for a British population bioresource

Neville, M. J., Lee, W., Humburg, P., Wong, D., Barnardo, M., Karpe, F., & Knight, J. C. (2017) · Human Immunology · doi:10.1016/j.humimm.2017.01.006 · Cited by 28

Improving power of association tests using multiple sets of imputed genotypes from distributed reference panels

Zhou, W., Fritsche, L. G., Das, S., Zhang, H., Nielsen, J. B., Holmen, O. L., Chen, J., Lin, M., Elvestad, M. B., Hveem, K., et al. (2017) · Genetic Epidemiology · doi:10.1002/gepi.22067 · Cited by 27

A genome-wide association study explores the genetic determinism of host resistance to Salmonella pullorum infection in chickens

Li, X., Nie, C., Liu, Y., Chen, Y., Lv, X., Wang, L., Zhang, J., Li, K., Jia, Y., Ban, L., et al. (2019) · Genetics Selection Evolution · doi:10.1186/s12711-019-0492-4 · Cited by 27

Genome-Wide Association Study and Genomic Prediction Elucidate the Distinct Genetic Architecture of Aluminum and Proton Tolerance in Arabidopsis thaliana

Nakano, Y., Kusunoki, K., Hoekenga, O. A., Tanaka, K., Iuchi, S., Sakata, Y., Kobayashi, M., Yamamoto, Y. Y., Koyama, H., & Kobayashi, Y. (2020) · Frontiers in Plant Science · doi:10.3389/fpls.2020.00405 · Cited by 27

Genome-wide association study for T lymphocyte subpopulations in swine

Lü, X., Fu, W., Luo, Y.-R., Ding, X., Zhou, J., Liu, Y., Liu, J., & Zhang, Q. (2012) · BMC Genomics · doi:10.1186/1471-2164-13-488 · Cited by 27

Genetic mapping of quantitative trait loci associated with drought tolerance in chickpea (Cicer arietinum L.)

Yadava, Y. K., Chaudhary, P., Yadav, S., Rizvi, A. H., Kumar, T., Srivastava, R. N., Soren, K. R., Bharadwaj, C., Srinivasan, R., Singh, N. K., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-44990-y · Cited by 26

The influence of admixture and consanguinity on population genetic diversity in Middle East

Yang, X. W., Al‐Bustan, S. A., Feng, Q., Guo, W., Ma, Z.-M., Marafie, M. J., Jacob, S. P., Al‐Mulla, F., & Xu, S. (2014) · Journal of Human Genetics · doi:10.1038/jhg.2014.81 · Cited by 27

A schizophrenia-associated HLA locus affects thalamus volume and asymmetry

Brucato, N., Guadalupe, T. M., Franke, B., Fisher, S. E., & Francks, C. (2015) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2015.02.021 · Cited by 26

Genome-Wide Local Ancestry Approach Identifies Genes and Variants Associated with Chemotherapeutic Susceptibility in African Americans

Wheeler, H. E., Gorsic, L. K., Welsh, M. M., Stark, A. L., Gamazon, E. R., Cox, N. J., & Dolan, M. E. (2011) · PLoS ONE · doi:10.1371/journal.pone.0021920 · Cited by 26

Current software for genotype imputation

Ellinghaus, D., Schreiber, S., Franke, A., & Nothnagel, M. (2009) · Human Genomics · doi:10.1186/1479-7364-3-4-371 · Cited by 26

Effective Population Size, Extended Linkage Disequilibrium and Signatures of Selection in the Rare Dog Breed Lundehund

Pfahler, S., & Distl, O. (2015) · PLoS ONE · doi:10.1371/journal.pone.0122680 · Cited by 26

No evidence that common genetic risk variation is shared between schizophrenia and autism

Vorstman, J. A. S., Anney, R., Derks, E. M., Gallagher, L., Gill, M., de Jonge, M. V., van Engeland, H., Kahn, R. S., & Ophoff, R. A. (2012) · American Journal of Medical Genetics Part B Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32121 · Cited by 25

Residual feed intake of lactating Holstein-Friesian cows predicted from high-density genotypes and phenotyping of growing heifers

Davis, S. R., Macdonald, K. A., Waghorn, G. C., & Spelman, R. J. (2014) · Journal of Dairy Science · doi:10.3168/jds.2013-7205 · Cited by 26

Single-step genomic model improved reliability and reduced the bias of genomic predictions in Danish Jersey

Ma, P., Lund, M. S., Nielsen, U., Aamand, G. P., & Su, G. (2015) · Journal of Dairy Science · doi:10.3168/jds.2015-9703 · Cited by 26

A missense mutation in solute carrier family 12, member 1 (SLC12A1) causes hydrallantois in Japanese Black cattle

Sasaki, S., Hasegawa, K., Higashi, T., Suzuki, Y., Sugano, S., Yasuda, Y., & Sugimoto, Y. (2016) · BMC Genomics · doi:10.1186/s12864-016-3035-1 · Cited by 26

Prospective association of a genetic risk score and lifestyle intervention with cardiovascular morbidity and mortality among individuals with type 2 diabetes: the Look AHEAD randomised controlled trial

The Look AHEAD Research Group (2015) · Diabetologia · doi:10.1007/s00125-015-3610-z · Cited by 24

Genome-wide association study reveals a QTL and strong candidate genes for umbilical hernia in pigs on SSC14

Grindflek, E. H., Hansen, M. H. S., Lien, S., & van Son, M. (2018) · BMC Genomics · doi:10.1186/s12864-018-4812-9 · Cited by 26

Genetic and cytological analyses reveal the recombination landscape of a partially differentiated plant sex chromosome in kiwifruit

Pilkington, S. M., Tahir, J., Hilario, E., Gardiner, S. E., Chagné, D., Catanach, A. S., McCallum, J. A., Jesson, L. K., Fraser, L. G., McNeilage, M. A., et al. (2019) · BMC Plant Biology · doi:10.1186/s12870-019-1766-2 · Cited by 25

Estimations of genomic linkage disequilibrium and effective population sizes in three sheep populations

Zhao, F., Wang, G., Zeng, T., Wei, C., Zhang, L., Wang, H., Zhang, S., Liu, R., Liu, Z., & Du, L. (2014) · Livestock Science · doi:10.1016/j.livsci.2014.10.015 · Cited by 27

A generic coalescent‐based framework for the selection of a reference panel for imputation

Paşaniuc, B., Avinery, R., Gur, T., Skibola, C. F., Bracci, P. M., & Halperin, E. (2010) · Genetic Epidemiology · doi:10.1002/gepi.20505 · Cited by 25

Genetic and genomic analysis of hyperthelia in Brown Swiss cattle

Butty, A. M., Frischknecht, M., Gredler, B., Neuenschwander, S., Moll, J., Bieber, A., Baes, C. F., & Seefried, F. R. (2016) · Journal of Dairy Science · doi:10.3168/jds.2016-11420 · Cited by 25

Polygenic risk score for bipolar disorder associates with divergent thinking and brain structures in the prefrontal cortex

Takeuchi, H., Kimura, R., Tomita, H., Taki, Y., Kikuchi, Y., Ono, C., Yu, Z., Matsudaira, I. Y., Nouchi, R., Yokoyama, R., et al. (2021) · Human Brain Mapping · doi:10.1002/hbm.25667 · Cited by 24

Genome-wide association mapping and genomic prediction of agronomical traits and breeding values in Iranian wheat under rain-fed and well-watered conditions

Rabieyan, E., Bihamta, M. R., Moghaddam, M. E., Mohammadi, V., & Alipour, H. (2022) · BMC Genomics · doi:10.1186/s12864-022-08968-w · Cited by 25

Validation of markers with non-additive effects on milk yield and fertility in Holstein and Jersey cows

Aliloo, H., Pryce, J. E., González-Recio, Ó., Cocks, B. G., & Hayes, B. J. (2015) · BMC Genetics · doi:10.1186/s12863-015-0241-9 · Cited by 25

Bipolar polygenic loading and bipolar spectrum features in major depressive disorder

Wiste, A. K., Robinson, E., Milaneschi, Y., Meier, S. M., Ripke, S., Clements, C. C., Fitzmaurice, G. M., Rietschel, M., Penninx, B. W. J. H., Smoller, J. W., et al. (2014) · Bipolar Disorders · doi:10.1111/bdi.12201 · Cited by 25

Fast accurate missing SNP genotype local imputation

Wang, Y., Cai, Z., Stothard, P., Moore, S., Goebel, R. G., Wang, L., & Lin, G. (2012) · BMC Research Notes · doi:10.1186/1756-0500-5-404 · Cited by 26

A Coalescent Model for Genotype Imputation

Jewett, E. M., Zawistowski, M., Rosenberg, N. A., & Zöllner, S. K. (2012) · Genetics · doi:10.1534/genetics.111.137984 · Cited by 25

Performance of HLA allele prediction methods in African Americans for class II genes HLA-DRB1, −DQB1, and –DPB1

Levin, A. M., Adrianto, I., Datta, I., Iannuzzi, M. C., Trudeau, S., McKeigue, P., Montgomery, C. G., & Rybicki, B. A. (2014) · BMC Genetics · doi:10.1186/1471-2156-15-72 · Cited by 24

Genetic Architecture and Candidate Genes Identified for Follicle Number in Chicken

Shen, M., Sun, H., Qu, L., Ma, M., Dou, T., Lü, J., Guo, J., Hu, Y., Wang, X., Li, Y., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-16557-1 · Cited by 25

Fine mapping of the MHC region identifies major independent variants associated with Han Chinese primary biliary cholangitis

Wang, C., Zheng, X., Tang, R., Han, C., Jiang, Y., Wu, J. H., Shao, Y., Gao, Y., Yu, J., Hu, Z., et al. (2019) · Journal of Autoimmunity · doi:10.1016/j.jaut.2019.102372 · Cited by 25

Genome‐wide scans of genetic variants for psychophysiological endophenotypes: A methodological overview

Iacono, W. G., Malone, S. M., Vaidyanathan, U., & Vrieze, S. I. (2014) · Psychophysiology · doi:10.1111/psyp.12343 · Cited by 25

HapFABIA: Identification of very short segments of identity by descent characterized by rare variants in large sequencing data

Hochreiter, S. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt1013 · Cited by 25

Estimating realized heritability for growth in Zhikong scallop (Chlamys farreri) using genome-wide complex trait analysis

Guo, H., Zeng, Q., Li, Y., Wang, Y., Chen, Z., Lin, P., Wang, S., & Bao, Z. (2018) · Aquaculture · doi:10.1016/j.aquaculture.2018.07.046 · Cited by 26

High-accuracy haplotype imputation using unphased genotype data as the references

Li, W., Xu, W., Fu, G., Ma, L., Richards, J. C., Rao, W., Bythwood, T., Guo, S., & Song, Q. (2015) · Gene · doi:10.1016/j.gene.2015.07.082 · Cited by 6

Data supporting the high-accuracy haplotype imputation using unphased genotype data as the references

Li, W., Xu, W., He, S., Ma, L., & Song, Q. (2016) · Data in Brief · doi:10.1016/j.dib.2016.06.029 · Cited by 3

The effects of reference panel perturbations on the accuracy of genotype imputationPreprint

Li, J. H., Liu, A., Buerkle, C. A., Palmer, W. J., Belbin, G. M., Ahangari, M. M., Gibson, M. J. S., & Flagel, L. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.08.10.552684 · Cited by 5

Rapid and Accurate Haplotype Phasing and Missing-Data Inference for Whole-Genome Association Studies By Use of Localized Haplotype Clustering

Browning, S. R., & Browning, B. L. (2007) · The American Journal of Human Genetics · doi:10.1086/521987 · Cited by 3,217

Missing data imputation and haplotype phase inference for genome-wide association studies

Browning, S. R. (2008) · Human Genetics · doi:10.1007/s00439-008-0568-7 · Cited by 196