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

Rabiner, L. R. (1989) · Proceedings of the IEEE · doi:10.1109/5.18626 · Cited by 22,743

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

The Wellcome Trust Case Control Consortium, Management Committee, Burton, P. R., Clayton, D. G., Cardon, L. R., Craddock, N., Deloukas, P., Duncanson, A., Kwiatkowski, D. P., McCarthy, M. I., et al. (2007) · Nature · doi:10.1038/nature05911 · Cited by 9,442

A New Statistical Method for Haplotype Reconstruction from Population Data

Stephens, M., Smith, N. J. C., & Donnelly, P. (2001) · The American Journal of Human Genetics · doi:10.1086/319501 · Cited by 7,512

A second generation human haplotype map of over 3.1 million SNPs

The International HapMap Consortium (2007) · Nature · doi:10.1038/nature06258 · Cited by 4,438

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

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

Marchini, J., Howie, B., Myers, S., McVean, G., & Donnelly, P. (2007) · Nature Genetics · doi:10.1038/ng2088 · Cited by 2,609

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

the NIDDK IBD Genetics Consortium, Barrett, J. C., the Belgian-French IBD Consortium, the Wellcome Trust Case Control Consortium, Hansoul, S., Nicolae, D. L., Cho, J. H., Duerr, R. H., Rioux, J. D., Brant, S. R., et al. (2008) · Nature Genetics · doi:10.1038/ng.175 · Cited by 2,593

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

Scheet, P. A., & Stephens, M. (2006) · The American Journal of Human Genetics · doi:10.1086/502802 · Cited by 1,959

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

Wellcome Trust Case Control Consortium, Zeggini, E., Scott, L. J., Saxena, R., Voight, B. F., Marchini, J. L., Hu, T., de Bakker, P. I., Abecasis, G. R., Almgren, P., et al. (2008) · Nature Genetics · doi:10.1038/ng.120 · Cited by 1,864

A high-resolution recombination map of the human genome

Kong, A., Gudbjartsson, D. F., Sainz, J., Jonsdottir, G. M., Gudjonsson, S. A., Richardsson, B., Sigurdardottir, S. L., Barnard, J., Hallbeck, B., Másson, G., et al. (2002) · Nature Genetics · doi:10.1038/ng917 · Cited by 1,750

Short communication: Genotype imputation within and across Nordic cattle breeds

Brøndum, R. F., Ma, P., Lund, M. S., & Su, G. (2012) · Journal of Dairy Science · doi:10.3168/jds.2012-5585 · Cited by 37

Using chromatin marks to interpret and localize genetic associations to complex human traits and diseases

Trynka, G., & Raychaudhuri, S. (2013) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2013.10.009 · Cited by 35

Association of High Myopia with Crystallin Beta A4 (CRYBA4) Gene Polymorphisms in the Linkage-Identified MYP6 Locus

Ho, D. W., Yap, M. K. H., Ng, P. W., Fung, W. Y., & Yip, S. P. (2012) · PLoS ONE · doi:10.1371/journal.pone.0040238 · Cited by 37

Incorporating heifer feed efficiency in the Australian selection index using genomic selection

González-Recio, Ó., Pryce, J. E., Haile‐Mariam, M., & Hayes, B. J. (2014) · Journal of Dairy Science · doi:10.3168/jds.2013-7515 · Cited by 37

Detection of Ancestry Informative HLA Alleles Confirms the Admixed Origins of Japanese Population

Nakaoka, H., Mitsunaga, S., Hosomichi, K., Liou, S., Sawamoto, T., Fujiwara, T., Tsutsui, N., Suematsu, K., Shinagawa, A., Inoko, H., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0060793 · Cited by 37

Dissection of additive genetic variability for quantitative traits in chickens using SNP markers

Abdollahi-Arpanahi, R., Pakdel, A., Nejati‐Javaremi, A., Shahrbabak, M. M., Morota, G., Valente, B. D., Kranis, A., Rosa, G. J. M., & Gianola, D. (2014) · Journal of Animal Breeding and Genetics · doi:10.1111/jbg.12079 · Cited by 38

Genome-wide QTL mapping of nine body composition and bone mineral density traits in pigs

Rothammer, S., Kremer, P. V., Bernau, M., Fernández-Fígares, I., Pfister-Schär, J., Međugorac, I., & Scholz, A. M. (2014) · Genetics Selection Evolution · doi:10.1186/s12711-014-0068-2 · Cited by 37

Differential contribution of genomic regions to marked genetic variation and prediction of quantitative traits in broiler chickens

Abdollahi-Arpanahi, R., Morota, G., Valente, B. D., Kranis, A., Rosa, G. J. M., & Gianola, D. (2016) · Genetics Selection Evolution · doi:10.1186/s12711-016-0187-z · Cited by 39

GeneImp: Fast Imputation to Large Reference Panels Using Genotype Likelihoods from Ultralow Coverage Sequencing

Spiliopoulou, A., Colombo, M., Orchard, P., Agakov, F. V., & McKeigue, P. (2017) · Genetics · doi:10.1534/genetics.117.200063 · Cited by 37

Natural Selection Drives Rapid Functional Evolution of Young Drosophila Duplicate Genes

Jiang, X., & Assis, R. (2017) · Molecular Biology and Evolution · doi:10.1093/molbev/msx230 · Cited by 34

Assessment of the performance of different imputation methods for low-coverage sequencing in Holstein cattle

Teng, J., Zhao, P., Wang, D., Chen, Z., Tang, H., Li, J., Mei, C., Yang, Z., Ning, C., & Zhang, Q. (2022) · Journal of Dairy Science · doi:10.3168/jds.2021-21360 · Cited by 41

Genome-wide association mapping and genomic prediction for pre‑harvest sprouting resistance, low α-amylase and seed color in Iranian bread wheat

Rabieyan, E., Bihamta, M. R., Moghaddam, M. E., Mohammadi, V., & Alipour, H. (2022) · BMC Plant Biology · doi:10.1186/s12870-022-03628-3 · Cited by 37

Inferring combined CNV/SNP haplotypes from genotype data

Su, S.-Y., Asher, J. E., Järvelin, M., Froguel, P., Blakemore, A. I. F., Balding, D. J., & Coin, L. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq157 · Cited by 37

The genetic diversity of Ethiopian barley genotypes in relation to their geographical origin

Teklemariam, S. S., Bayissa, K. N., Matros, A., Pillen, K., Ordon, F., & Wehner, G. (2022) · PLoS ONE · doi:10.1371/journal.pone.0260422 · Cited by 36

Selection signatures for local and regional adaptation in Chinese Mongolian horse breeds reveal candidate genes for hoof health

Han, H., Randhawa, I. A. S., MacHugh, D. E., McGivney, B. A., Katz, L. M., Dugarjaviin, M., & Hill, E. W. (2023) · BMC Genomics · doi:10.1186/s12864-023-09116-8 · Cited by 36

Loci associated with adult stature also affect calf birth survival in cattle

Sahana, G., Höglund, J., Guldbrandtsen, B., & Lund, M. S. (2015) · BMC Genetics · doi:10.1186/s12863-015-0202-3 · Cited by 36

Combining Family‐ and Population‐Based Imputation Data for Association Analysis of Rare and Common Variants in Large Pedigrees

Saad, M., & Wijsman, E. M. (2014) · Genetic Epidemiology · doi:10.1002/gepi.21844 · Cited by 37

The use of imputed values in the meta-analysis of genome-wide association studies

Jiao, S., Hsu, L., Hutter, C. M., & Peters, U. (2011) · Genetic Epidemiology · doi:10.1002/gepi.20608 · Cited by 36

Imputation-Based Population Genetics Analysis of Plasmodium falciparum Malaria Parasites

Samad, H., Coll, F., Preston, M. D., Ocholla, H., Fairhurst, R. M., & Clark, T. G. (2015) · PLoS Genetics · doi:10.1371/journal.pgen.1005131 · Cited by 36

Sensitivity to prior specification in Bayesian genome-based prediction models

Lehermeier, C., Wimmer, V., Albrecht, T., Auinger, H.-J., Gianola, D., Schmid, V. J., & Schön, C. (2013) · Statistical Applications in Genetics and Molecular Biology · doi:10.1515/sagmb-2012-0042 · Cited by 35

A nonsense mutation in PLD4 is associated with a zinc deficiency-like syndrome in Fleckvieh cattle

Jung, S., Pausch, H., Langenmayer, M. C., Schwarzenbacher, H., Majzoub‐Altweck, M., Gollnick, N. S., & Fries, R. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-623 · Cited by 36

Proper Use of Allele-Specific Expression Improves Statistical Power for cis -eQTL Mapping with RNA-Seq Data

Hu, Y.-J., Sun, W., Tzeng, J., & Perou, C. M. (2015) · Journal of the American Statistical Association · doi:10.1080/01621459.2015.1038449 · Cited by 37

Enlarging a training set for genomic selection by imputation of un-genotyped animals in populations of varying genetic architecture

Pimentel, E. C. G., Wensch-Dorendorf, M., König, S., & Swalve, H. H. (2013) · Genetics Selection Evolution · doi:10.1186/1297-9686-45-12 · Cited by 36

Genome‐wide Association Studies of Specific Antinuclear Autoantibody Subphenotypes in Primary Biliary Cholangitis

Wang, C., Zheng, X., Jiang, P., Tang, R., Gong, Y., Dai, Y., Wang, L., Xu, P., Sun, W., Wang, L., et al. (2019) · Hepatology · doi:10.1002/hep.30604 · Cited by 36

Historical Introgressions from a Wild Relative of Modern Cassava Improved Important Traits and May Be Under Balancing Selection

Wolfe, M. D., Bauchet, G. J., Chan, A. W., Lozano, R. D., Ramu, P., Egesi, C. N., Kawuki, R., Kulakow, P., Rabbi, I. Y., & Jannink, J. (2019) · Genetics · doi:10.1534/genetics.119.302757 · Cited by 34

Advances in genetics toward identifying pathogenic cell states of rheumatoid arthritis

Amariuta, T., Luo, Y., Knevel, R., Okada, Y., & Raychaudhuri, S. (2019) · Immunological Reviews · doi:10.1111/imr.12827 · Cited by 33

A combination of genome-wide association study and selection signature analysis dissects the genetic architecture underlying bone traits in chickens

Li, Y. D., Liu, X., Li, Z., Wang, W. J., Li, Y. M., Cao, Z. P., Luan, P., Xiao, F., Gao, H., Guo, H., et al. (2021) · animal · doi:10.1016/j.animal.2021.100322 · Cited by 37

Identification of Selection Footprints on the X Chromosome in Pig

Ma, Y., Zhang, H., Zhang, Q., & Ding, X. (2014) · PLoS ONE · doi:10.1371/journal.pone.0094911 · Cited by 36

Gene–gene and gene–environment interactions in ulcerative colitis

Wang, M., Fiocchi, C., Zhu, X., Ripke, S., Kamboh, M. I., Rebert, N. A., Duerr, R. H., & Achkar, J. (2013) · Human Genetics · doi:10.1007/s00439-013-1395-z · Cited by 36

Practical Use of Methods for Imputation of HLA Alleles from SNP Genotype DataPreprint

Motyer, A., Vukcevic, D., Dilthey, A., Donnelly, P., McVean, G., & Leslie, S. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/091009 · Cited by 36

imputeqc: an R package for assessing imputation quality of genotypes and optimizing imputation parameters

Khvorykh, G. V., & Владимирович, Х. А. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-03589-0 · Cited by 35

Genome-Wide Search for Gene-Gene Interactions in Colorectal Cancer

Jiao, S., Hsu, L., Berndt, S. I., Bezieau, S., Brenner, H., Buchanan, D. D., Caan, B. J., Campbell, P. T., Carlson, C. S., Casey, G., et al. (2012) · PLoS ONE · doi:10.1371/journal.pone.0052535 · Cited by 36

Defining the Contribution of CNTNAP2 to Autism Susceptibility

Sampath, S., Bhat, S., Gupta, S., O’Connor, A., West, A. B., Arking, D. E., & Chakravarti, A. (2013) · PLoS ONE · doi:10.1371/journal.pone.0077906 · Cited by 39

Accuracy of genome-wide imputation in Braford and Hereford beef cattle

Piccoli, M. L., Neto, J. B., Cardoso, F. F., Sargolzaei, M., Larmer, S. G., & Schenkel, F. S. (2014) · BMC Genetics · doi:10.1186/s12863-014-0157-9 · Cited by 35

Genomic prediction using models with dominance and imprinting effects for backfat thickness and average daily gain in Danish Duroc pigs

Guo, X., Christensen, O. F., Ostersen, T., Wang, Y., Lund, M. S., & Su, G. (2016) · Genetics Selection Evolution · doi:10.1186/s12711-016-0245-6 · Cited by 35

Gene expression analysis of blood, liver, and muscle in cattle divergently selected for high and low residual feed intake1

Khansefid, M., Millen, C. A., Chen, Y., Pryce, J. E., Chamberlain, A. J., Jagt, C. J. V., Gondro, C., & Goddard, M. E. (2017) · Journal of Animal Science · doi:10.2527/jas2016.1320 · Cited by 35

Multitrait meta-analysis identified genomic regions associated with sexual precocity in tropical beef cattle1

de Melo, T. P., Fortes, M. R. S., Bresolin, T., Mota, L. F. M., de Albuquerque, L. G., & Carvalheiro, R. (2018) · Journal of Animal Science · doi:10.1093/jas/sky289 · Cited by 35

Skim sequencing: an advanced NGS technology for crop improvement

Kumar, P., Choudhary, M., Jat, B. S., Kumar, B., Singh, V. K., Kumar, V., Singla, D., & Rakshit, S. (2021) · Journal of Genetics · doi:10.1007/s12041-021-01285-3 · Cited by 39

Genome-Wide Association Study and Selective Sweep Analysis Reveal the Genetic Architecture of Body Weights in a Chicken F2 Resource Population

Wang, S., Wang, Y., Li, Y., Xiao, F., Guo, H., Gao, H., Wang, N., Zhang, H., & Li, H. (2022) · Frontiers in Veterinary Science · doi:10.3389/fvets.2022.875454 · Cited by 35

Genome-Wide Association Analysis of Ischemic Stroke in Young Adults

Cheng, Y.-C., O’Connell, J. R., Cole, J. W., Stine, O. C., Dueker, N., McArdle, P. F., Sparks, M. J., Shen, J., Laurie, C. C., Nelson, S. C., et al. (2011) · G3 Genes Genomes Genetics · doi:10.1534/g3.111.001164 · Cited by 36

Prospects and limits of marker imputation in quantitative genetic studies in European elite wheat (Triticum aestivum L.)

He, S., Zhao, Y., Mette, M. F., Bothe, R., Ebmeyer, E., Sharbel, T. F., Reif, J. C., & Jiang, Y. (2015) · BMC Genomics · doi:10.1186/s12864-015-1366-y · Cited by 35

Accuracy of genomic prediction using an evenly spaced, low-density single nucleotide polymorphism panel in broiler chickens

Wang, C., Habier, D., Peiris, B. L., Wolc, A., Kranis, A., Watson, K., Avendaño, S., Garrick, D. J., Fernando, R. L., Lamont, S. J., et al. (2013) · Poultry Science · doi:10.3382/ps.2012-02941 · Cited by 35

Confirmation of a non-synonymous SNP in PNPLA8 as a candidate causal mutation for Weaver syndrome in Brown Swiss cattle

Kunz, E., Rothammer, S., Pausch, H., Schwarzenbacher, H., Seefried, F. R., Matiasek, K., Seichter, D., Ruß, I., Fries, R., & Međugorac, I. (2016) · Genetics Selection Evolution · doi:10.1186/s12711-016-0201-5 · Cited by 35

Approaching Genetics Through the MHC Lens: Tools and Methods for HLA Research

Douillard, V., Castelli, E. C., Mack, S. J., Hollenbach, J. A., Gourraud, P., Vince, N., & Limou, S. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.774916 · Cited by 38

Fine mapping of a quantitative trait locus for bovine milk fat composition on Bos taurus autosome 19

Bouwman, A. C., Visker, M. H. P. W., van Arendonk, J. A. M., & Bovenhuis, H. (2013) · Journal of Dairy Science · doi:10.3168/jds.2013-7197 · Cited by 34

A Genome Scan for Selection Signatures in Pigs

Ma, Y., Wei, J., Zhang, Q., Chen, L., Wang, J., Liu, J., & Ding, X. (2015) · PLoS ONE · doi:10.1371/journal.pone.0116850 · Cited by 36

Analyses and Comparison of Imputation-Based Association Methods

Pei, Y., Zhang, L., Li, J., & Deng, H. (2010) · PLoS ONE · doi:10.1371/journal.pone.0010827 · Cited by 34

PRIMAL: Fast and Accurate Pedigree-based Imputation from Sequence Data in a Founder Population

Livne, O. E., Han, L., Alkorta‐Aranburu, G., Wentworth-Sheilds, W., Abney, M., Ober, C., & Nicolae, D. L. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004139 · Cited by 36

Family-Based Association Analysis of Alcohol Dependence Criteria and Severity

Wetherill, L. F., Kapoor, M., Arpana Agrawal, A., Bucholz, K. K., Koller, D. L., Bertelsen, S. E., Le, N., Wang, J., Almasy, L. A., HESSELBROCK, V. M., et al. (2013) · Alcoholism Clinical and Experimental Research · doi:10.1111/acer.12251 · Cited by 33

A Simple Test Identifies Selection on Complex Traits

Beissinger, T., Kruppa, J., Cavero, D., Ha, N.-T., Erbe, M., & Simianer, H. (2018) · Genetics · doi:10.1534/genetics.118.300857 · Cited by 32

Model-Based Verification of Hypotheses on the Origin of Modern Japanese Revisited by Bayesian Inference Based on Genome-Wide SNP Data

Nakagome, S., Sato, T. K., Ishida, H., Hanihara, T., Yamaguchi, T., Kimura, R., Mano, S., & Oota, H. (2015) · Molecular Biology and Evolution · doi:10.1093/molbev/msv045 · Cited by 34

A computationally efficient algorithm for genomic prediction using a Bayesian model

Wang, T., Chen, Y. P., Goddard, M. E., Meuwissen, T. H. E., Kemper, K. E., & Hayes, B. J. (2015) · Genetics Selection Evolution · doi:10.1186/s12711-014-0082-4 · Cited by 34

Modelling genetic variations using fragmentation-coagulation processes

Teh, Y. W. (2014) · Open Collections · doi:10.14288/1.0056655 · Cited by 34

Partitioning of genomic variance reveals biological pathways associated with udder health and milk production traits in dairy cattle

Edwards, S. M., Thomsen, B., Madsen, P., & Sørensen, P. B. (2015) · Genetics Selection Evolution · doi:10.1186/s12711-015-0132-6 · Cited by 34

A Genetic Signature of Spina Bifida Risk from Pathway-Informed Comprehensive Gene-Variant Analysis

Marini, N. J., Hoffmann, T. J., Lammer, E. J., Hardin, J., Lazaruk, K. D., Stein, J. B., Gilbert, D. A., Wright, C., Lipzen, A., Pennacchio, L. A., et al. (2011) · PLoS ONE · doi:10.1371/journal.pone.0028408 · Cited by 33

A review of genomic selection - Implications for the South African beef and dairy cattle industries

van Marle-Köster, E., Visser, C., & Berry, D. P. (2013) · South African Journal of Animal Science · doi:10.4314/sajas.v43i1.1 · Cited by 33

A genome-wide association study in a large F2-cross of laying hens reveals novel genomic regions associated with feather pecking and aggressive pecking behavior

Lutz, V., Stratz, P., Preuß, S., Tetens, J., Grashorn, M. A., Bessei, W., & Bennewitz, J. (2017) · Genetics Selection Evolution · doi:10.1186/s12711-017-0287-4 · Cited by 37

Genotype imputation from various low-density SNP panels and its impact on accuracy of genomic breeding values in pigs

Grossi, D. A., Brito, L. F., Jafarikia, M., Schenkel, F. S., & Feng, Z. Z. (2018) · animal · doi:10.1017/s175173111800085x · Cited by 33

Genome wide association study for the identification of genes associated with tail fat deposition in Chinese sheep breeds

Zhu, C., Li, N., Cheng, H., & Ma, Y. (2021) · Biology Open · doi:10.1242/bio.054932 · Cited by 33

Genomic selection for recovery of original genetic background from hybrids of endangered and common breeds

Amador, C., Hayes, B. J., & Daetwyler, H. D. (2013) · Evolutionary Applications · doi:10.1111/eva.12113 · Cited by 32

Imputing Variants in HLA-DR Beta Genes Reveals That HLA-DRB1 Is Solely Associated with Rheumatoid Arthritis and Systemic Lupus Erythematosus

Kim, K.-W., Bang, S., Yoo, D., Cho, S., Choi, C., Sung, Y., Kim, T., Jun, J., Kang, Y. M., Suh, C., et al. (2016) · PLoS ONE · doi:10.1371/journal.pone.0150283 · Cited by 33

Identifying genetic relatives without compromising privacyPreprint

He, D., Furlotte, N. A., Hormozdiari, F., Joo, J. W. J., Wadia, A., Ostrovsky, R., Sahai, A., & Eskin, E. (2014) · Genome Research · doi:10.1101/gr.153346.112 · Cited by 32

A Genome-Wide Association Study of Protein, Oil, and Amino Acid Content in Wild Soybean (Glycine soja)

Kim, W. J., Kang, B. H., Kang, S., Shin, S., Chowdhury, S., Jeong, S., Choi, M., Park, S.-K., Moon, J., Ryu, J., et al. (2023) · Plants · doi:10.3390/plants12081665 · Cited by 32

Cosmopolitan and ethnic-specific replication of genetic risk factors for asthma in 2 Latino populations

Galanter, J., Torgerson, D. G., Gignoux, C. R., Sen, Ś., Roth, L. A., Vía, M., Aldrich, M. C., Eng, C. S., Huntsman, S., Rodríguez‐Santana, J., et al. (2011) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2011.03.050 · Cited by 30

Efficient Imputation of Missing Markers in Low-Coverage Genotyping-by-Sequencing Data from Multiparental Crosses

Huang, B. E., Raghavan, C., Mauleon, R., Broman, K. W., & Leung, H. (2014) · Genetics · doi:10.1534/genetics.113.158014 · Cited by 32

Whole-exome sequencing of over 4100 men of African ancestry and prostate cancer risk

Rand, K. A., Rohland, N., Tandon, A., Stram, A. H., Sheng, X., Do, R., Paşaniuc, B., Allen, A., Quinque, D., Mallick, S., et al. (2015) · Human Molecular Genetics · doi:10.1093/hmg/ddv462 · Cited by 32

Gene-Based Mapping and Pathway Analysis of Metabolic Traits in Dairy Cows

Ha, N.-T., Gross, J. J., van Dorland, A., Tetens, J., Thaller, G. E., Schlather, M., Bruckmaier, R. M., & Simianer, H. (2015) · PLoS ONE · doi:10.1371/journal.pone.0122325 · Cited by 32

Identification of QTLs for Flesh Mealiness in Apple (Malus × domestica Borkh.)

Moriya, S., Kunihisa, M., Okada, K., Iwanami, H., Iwata, H., Minamikawa, M. F., Katayose, Y., Matsumoto, T., Mori, S., Sasaki, H., et al. (2016) · The Horticulture Journal · doi:10.2503/hortj.mi-156 · Cited by 32

Using single-step genomic best linear unbiased predictor to enhance the mitigation of seasonal losses due to heat stress in pigs

Fragomeni, B. D. O., Lourenco, D. A. L., Tsuruta, S., Bradford, H. L., Gray, K. A., Huang, Y., & Misztal, I. (2016) · Journal of Animal Science · doi:10.2527/jas.2016-0820 · Cited by 32

Genome-Wide Associations for Water-Soluble Carbohydrate Concentration and Relative Maturity in Wheat Using SNP and DArT Marker Arrays

Ovenden, B. W., Milgate, A., Wade, L. J., Rebetzke, G. J., & Holland, J. B. (2017) · G3 Genes Genomes Genetics · doi:10.1534/g3.117.039842 · Cited by 33

Detection of two non-synonymous SNPs in SLC45A2 on BTA20 as candidate causal mutations for oculocutaneous albinism in Braunvieh cattle

Rothammer, S., Kunz, E., Seichter, D., Krebs, S., Wassertheurer, M., Fries, R., Brem, G., & Međugorac, I. (2017) · Genetics Selection Evolution · doi:10.1186/s12711-017-0349-7 · Cited by 32

QTL analysis of cooking time and quality traits in dry bean (Phaseolus vulgaris L.)

Berry, M., Izquierdo, P., Jeffery, H. R., Shaw, S., Nchimbi‐Msolla, S., & Cichy, K. A. (2020) · Theoretical and Applied Genetics · doi:10.1007/s00122-020-03598-w · Cited by 32

Plant-ImputeDB: an integrated multiple plant reference panel database for genotype imputation

Gao, Y., Yang, Z., Yang, W., Yang, Y., Gong, J., Yang, Q., & Niu, X. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa953 · Cited by 31

Diversification, Introgression, and Rampant Cytonuclear Discordance in Rocky Mountains Chipmunks (Sciuridae: Tamias)

Sarver, B. A. J., Herrera, N. D., Sneddon, D., Hunter, S. S., Settles, M. L., Kronenberg, Z. N., Demboski, J. R., Good, J. M., & Sullivan, J. M. (2020) · Systematic Biology · doi:10.1093/sysbio/syaa085 · Cited by 31

Genomic Analyses Revealed the Genetic Difference and Potential Selection Genes of Growth Traits in Two Duroc Lines

Li, D., Huang, M., Zhuang, Z., Ding, R., Gu, T., Hong, L., Zheng, E., Li, Z., Cai, G., Wu, Z., et al. (2021) · Frontiers in Veterinary Science · doi:10.3389/fvets.2021.725367 · Cited by 32

Identification of Candidate Variants Associated With Bone Weight Using Whole Genome Sequence in Beef Cattle

Niu, Q., Zhang, T., Xu, L., Wang, T., Wang, Z., Zhu, B., Gao, X., Chen, Y., Zhang, L., Gao, H., et al. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.750746 · Cited by 33

Genomic population structure and local adaptation of the wild strawberry Fragaria nilgerrensis

Hu, Y., Feng, C., Yang, L., Edger, P. P., & Kang, M. (2022) · Horticulture Research · doi:10.1093/hr/uhab059 · Cited by 31

On the origin and structure of haplotype blocks

Shipilina, D., Pal, A., Stankowski, S., Chan, Y. F., & Barton, N. (2022) · Molecular Ecology · doi:10.1111/mec.16793 · Cited by 36

Identification of novel germline polymorphisms governing capecitabine sensitivity

O’Donnell, P. H., Stark, A. L., Gamazon, E. R., Wheeler, H. E., McIlwee, B. E., Gorsic, L. K., Im, H. K., Huang, R. S., Cox, N. J., & Dolan, M. E. (2012) · Cancer · doi:10.1002/cncr.26737 · Cited by 33

Genomic data as the “hitchhiker's guide” to cattle adaptation: tracking the milestones of past selection in the bovine genome

Utsunomiya, Y. T., O’Brien, A. M. P., Sonstegard, T. S., Sölkner, J., & Garcia, J. F. (2015) · Frontiers in Genetics · doi:10.3389/fgene.2015.00036 · Cited by 32

Multi-allelic haplotype model based on genetic partition for genomic prediction and variance component estimation using SNP markers

Da, Y. (2015) · BMC Genetics · doi:10.1186/s12863-015-0301-1 · Cited by 32

Discovery and characterization of Alu repeat sequences via precise local read assembly

Halo, J. V., Baron, A., Diroff, N. M., & Kidd, J. M. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv1089 · Cited by 31

On the performance of tests for the detection of signatures of selection: a case study with the Spanish autochthonous beef cattle populations

González‐Rodríguez, A., Munilla, S., Mouresan, E. F., Álvarez, J. J. C., Díaz, C., Piedrafita, J., Altarriba, J., de la Fuente, J. Á. B., Molina, A., & Varona, L. (2016) · Genetics Selection Evolution · doi:10.1186/s12711-016-0258-1 · Cited by 35

Genetic architecture of gene expression underlying variation in host response to porcine reproductive and respiratory syndrome virus infection

Kommadath, A., Bao, H., Choi, I., Reecy, J. M., Koltes, J. E., Fritz‐Waters, E., Eisley, C. J., Grant, J. R., Rowland, R. R. R., Tuggle, C. K., et al. (2017) · Scientific Reports · doi:10.1038/srep46203 · Cited by 33

Genome sequences and population genomics reveal climatic adaptation and genomic divergence between two closely related sweetgum species

Xu, W., Ren, C., Zhang, X., Comes, H. P., Liu, X., Li, Y., Kettle, C. J., Jalonen, R., Gaisberger, H., Ma, Y., et al. (2024) · The Plant Journal · doi:10.1111/tpj.16675 · Cited by 33

Genome‐wide association QTL mapping for teat number in a purebred population of Duroc pigs

Arakawa, A., Okumura, N., Taniguchi, M., Hayashi, T., Hirose, K., Fukawa, K., Ito, T., Matsumoto, T., Uenishi, H., & Mikawa, S. (2015) · Animal Genetics · doi:10.1111/age.12331 · Cited by 31

Genetic Variation Predicts Serum Lycopene Concentrations in a Multiethnic Population of Postmenopausal Women ,

Zubair, N., Kooperberg, C. L., Liu, J., Di, C., Peters, U., & Neuhouser, M. L. (2014) · Journal of Nutrition · doi:10.3945/jn.114.202150 · Cited by 31

Use of molecular technologies for the advancement of animal breeding: genomic selection in dairy cattle populations in Australia, Ireland and New Zealand

Spelman, R. J., Hayes, B. J., & Berry, D. P. (2013) · Animal Production Science · doi:10.1071/an12304 · Cited by 31

Genome-wide association and genomic prediction identifies associated loci and predicts the sensitivity of Tobacco ringspot virus in soybean plant introductions

Chang, H., Brown, P. J., Lipka, A. E., Domier, L. L., & Hartman, G. L. (2016) · BMC Genomics · doi:10.1186/s12864-016-2487-7 · Cited by 32

A comparison of different algorithms for phasing haplotypes using Holstein cattle genotypes and pedigree data

Miar, Y., Sargolzaei, M., & Schenkel, F. S. (2017) · Journal of Dairy Science · doi:10.3168/jds.2016-11590 · Cited by 34

Recombination Variation Shapes Phylogeny and Introgression in Wild Diploid Strawberries

Feng, C., Wang, J., Liston, A., & Kang, M. (2023) · Molecular Biology and Evolution · doi:10.1093/molbev/msad049 · Cited by 29

Improving the accuracy of genomic prediction in Chinese Holstein cattle by using one-step blending

Li, X., Wang, S., Huang, J. W., Li, L., Zhang, Q., & Ding, X. (2014) · Genetics Selection Evolution · doi:10.1186/s12711-014-0066-4 · Cited by 31

Imputation of sequence level genotypes in the Franches-Montagnes horse breed

Frischknecht, M., Neuditschko, M., Jagannathan, V., Drögemüller, C., Tetens, J., Thaller, G. E., Leeb, T., & Rieder, S. (2014) · Genetics Selection Evolution · doi:10.1186/s12711-014-0063-7 · Cited by 31

Differential Effects of Chromosome 9p21 Variation on Subphenotypes of Intracranial Aneurysm

Nakaoka, H., Takahashi, T., Akiyama, K., Cui, T., Tajima, A., Krischek, B., Kasuya, H., Hata, A., & Inoue, I. (2010) · Stroke · doi:10.1161/strokeaha.110.586529 · Cited by 32

Social environment influences the relationship between genotype and gene expression in wild baboons

Runcie, D. E., Wiedmann, R. T., Archie, E. A., Jeanne Altmann, A., Wray, G. A., Alberts, S. C., & Tung, J. (2013) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2012.0345 · Cited by 32

The utility of low-density genotyping for imputation in the Thoroughbred horse

Corbin, L. J., Kranis, A., Blott, S. C., Swinburne, J. E., Vaudin, M., Bishop, S. C., & Woolliams, J. A. (2014) · Genetics Selection Evolution · doi:10.1186/1297-9686-46-9 · Cited by 31

Whole‐genome prediction of fatty acid composition in meat of Japanese Black cattle

Onogi, A., Ogino, A., Komatsu, T., Shoji, N., Shimizu, K., Kurogi, K., Yasumori, T., Togashi, K., & Iwata, H. (2015) · Animal Genetics · doi:10.1111/age.12300 · Cited by 31

Genome-Wide Analysis Identifies Germ-Line Risk Factors Associated with Canine Mammary Tumours

Melin, M., Rivera, P., Arendt, M. L., Elvers, I., Murén, E., Gustafson, U., Starkey, M., Borge, K. S., Lingaas, F., Häggström, J. U., et al. (2016) · PLoS Genetics · doi:10.1371/journal.pgen.1006029 · Cited by 33

Diversity analysis and genomic prediction of Sclerotinia resistance in sunflower using a new 25 K SNP genotyping array

Livaja, M., Unterseer, S., Erath, W., Lehermeier, C., Wieseke, R., Plieske, J., Polley, A., Luerßen, H., Wieckhorst, S., Mascher, M., et al. (2015) · Theoretical and Applied Genetics · doi:10.1007/s00122-015-2629-3 · Cited by 31

Estimations of linkage disequilibrium, effective population size and ROH‐based inbreeding coefficients in Spanish Churra sheep using imputed high‐density SNP genotypes

Chitneedi, P., Arranz, J. J., Suárez‐Vega, A., García-Gámez, E., & Gutiérrez‐Gil, B. (2017) · Animal Genetics · doi:10.1111/age.12564 · Cited by 31

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