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

A nonsense mutation in the COL7A1 gene causes epidermolysis bullosa in Vorderwald cattle

Pausch, H., Ammermüller, S., Wurmser, C., Hamann, H., Tetens, J., Drögemüller, C., & Fries, R. (2016) · BMC Genetics · doi:10.1186/s12863-016-0458-2 · Cited by 25

GWAS and Post-GWAS High-Resolution Mapping Analyses Identify Strong Novel Candidate Genes Influencing the Fatty Acid Composition of the Longissimus dorsi Muscle in Pigs

Lee, J.-B., Kang, Y., Kim, S.-G., Woo, J.-H., Shin, M., Park, N.-G., Yang, B., Han, S., Han, K. M., Lim, H., et al. (2021) · Genes · doi:10.3390/genes12091323 · Cited by 24

Assessment of Imputation Quality: Comparison of Phasing and Imputation Algorithms in Real Data

Stahl, K., Gola, D., & König, I. R. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.724037 · Cited by 24

Independent contribution of polygenic risk for schizophrenia and cannabis use in predicting psychotic-like experiences in young adulthood: testing gene × environment moderation and mediation

Elkrief, L., Lin, B. D., Marchi, M., Afzali, M. H., Banaschewski, T., Bokde, A. L. W., Quinlan, E. B., Desrivières, S., Flor, H., Garavan, H., et al. (2021) · Psychological Medicine · doi:10.1017/s0033291721003378 · Cited by 25

Genomic divergence and a lack of recent introgression between commercial and wild bumblebees ( Bombus terrestris )

Hjort, C. K., Paris, J. R., Olsson, P., Herbertsson, L., de Miranda, J. R., Dudaniec, R. Y., & Smith, H. G. (2022) · Evolutionary Applications · doi:10.1111/eva.13346 · Cited by 25

QTL mapping and identification of candidate genes using a genome-wide association study for heat tolerance at anthesis in rice (Oryza sativa L.)

Hu, C., Jiang, J., Li, Y., Song, S., Zou, Y., Jing, C., Zhang, Y., Wang, D., He, Q., & Dang, X. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.983525 · Cited by 25

Genome-wide association mapping for wheat morphometric seed traits in Iranian landraces and cultivars under rain-fed and well-watered conditions

Rabieyan, E., Bihamta, M. R., Moghaddam, M. E., Mohammadi, V., & Alipour, H. (2022) · Scientific Reports · doi:10.1038/s41598-022-22607-0 · Cited by 24

Optimal methods for meta-analysis of genome-wide association studies

Zhou, B., Shi, J., & Whittemore, A. S. (2011) · Genetic Epidemiology · doi:10.1002/gepi.20603 · Cited by 24

The Mammalian Cervical Vertebrae Blueprint Depends on theT(brachyury) Gene

Kromik, A., Ulrich, R., Kusenda, M., Tipold, A., Stein, V. M., Hellige, M., Dziallas, P., Hadlich, F., Widmann, P., Goldammer, T., et al. (2015) · Genetics · doi:10.1534/genetics.114.169680 · Cited by 24

Rapid haplotype inference for nuclear families

Williams, A. L., Housman, D. E., Rinard, M., & Gifford, D. K. (2010) · Genome biology · doi:10.1186/gb-2010-11-10-r108 · Cited by 24

Assessing haplotype-based models for genomic evaluation in Holstein cattle

Karimi, Z., Sargolzaei, M., Robinson, J. A. B., & Schenkel, F. S. (2018) · Canadian Journal of Animal Science · doi:10.1139/cjas-2018-0009 · Cited by 24

Correction: Genome-Wide Copy Number Analysis Uncovers a New HSCR Gene: NRG3

Tang, C. S.-M., Cheng, G., So, M., Yip, B. H. K., Miao, X., Wong, E. H.-M., Ngan, E., Lui, V. C., Song, Y., Chan, D., et al. (2012) · PLoS Genetics · doi:10.1371/annotation/0ffda9e0-ba27-40c1-b766-df58de9083d5 · Cited by 24

Utilizing Genotype Imputation for the Augmentation of Sequence Data

Fridley, B. L., Jenkins, G. D., Deyo-Svendsen, M., Hebbring, S. J., & Freimuth, R. R. (2010) · PLoS ONE · doi:10.1371/journal.pone.0011018 · Cited by 23

Rare Variants in Transcript and Potential Regulatory Regions Explain a Small Percentage of the Missing Heritability of Complex Traits in Cattle

González-Recio, Ó., Daetwyler, H. D., MacLeod, I. M., Pryce, J. E., Bowman, P. J., Hayes, B. J., & Goddard, M. E. (2015) · PLoS ONE · doi:10.1371/journal.pone.0143945 · Cited by 23

Comparison of shape quantification methods for genomic prediction, and genome-wide association study of sorghum seed morphology

Sakamoto, L., Kajiya‐Kanegae, H., Noshita, K., Takanashi, H., Kobayashi, M., Kudo, T., Yano, K., Tokunaga, T., Tsutsumi, N., & Iwata, H. (2019) · PLoS ONE · doi:10.1371/journal.pone.0224695 · Cited by 22

Distinguishing pedigree relationships via multi-way identity by descent sharing and sex-specific genetic maps

Qiao, Y., Sannerud, J., Basu-Roy, S., Hayward, C., & Williams, A. L. (2020) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2020.12.004 · Cited by 23

Towards a Cost-Effective Implementation of Genomic Prediction Based on Low Coverage Whole Genome Sequencing in Dezhou Donkey

Zhao, P., Teng, J., Zhang, X., Wang, D., Zhang, X., Li, S., Jiang, X. P., Li, H., Ning, C., & Zhang, Q. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.728764 · Cited by 23

Using selection index theory to estimate consistency of multi-locus linkage disequilibrium across populations

Wientjes, Y. C. J., Veerkamp, R. F., & Calus, M. P. L. (2015) · BMC Genetics · doi:10.1186/s12863-015-0252-6 · Cited by 23

Genetic Risk Score for Prediction of Newborn Adiposity and Large-for-Gestational-Age Birth

Chawla, R., Badon, S. E., Rangarajan, J., Reisetter, A. C., Armstrong, L. L., Lowe, L. P., Urbanek, M., Metzger, B. E., Hayes, M. G., Scholtens, D. M., et al. (2014) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2013-4221 · Cited by 23

A Multistage Association Study Identifies a Breast Cancer Genetic Locus at NCOA7

Higginbotham, K. S. P., Breyer, J. P., Bradley, K. M., Schuyler, P. A., Plummer, W. D., Freudenthal, M. E., Trentham‐Dietz, A., Newcomb, P. A., Sanders, M. E., Page, D. L., et al. (2011) · Cancer Research · doi:10.1158/0008-5472.can-10-2653 · Cited by 23

A frameshift mutation in GON4L is associated with proportionate dwarfism in Fleckvieh cattle

Schwarzenbacher, H., Wurmser, C., Flisikowski, K., Misurova, L., Jung, S., Langenmayer, M. C., Schnieke, A., Knubben‐Schweizer, G., Fries, R., & Pausch, H. (2016) · Genetics Selection Evolution · doi:10.1186/s12711-016-0207-z · Cited by 23

Deploying Fourier Coefficients to Unravel Soybean Canopy Diversity

Jubery, T. Z., Shook, J. M., Parmley, K. A., Zhang, J., Naik, H. S., Higgins, R. H., Sarkar, S., Singh, A., Singh, A. K., & Ganapathysubramanian, B. (2017) · Frontiers in Plant Science · doi:10.3389/fpls.2016.02066 · Cited by 23

A search for overlapping genetic susceptibility loci between non-Hodgkin lymphoma and autoimmune diseases

Conde, L., Bracci, P. M., Halperin, E., & Skibola, C. F. (2011) · Genomics · doi:10.1016/j.ygeno.2011.03.007 · Cited by 23

The Clark Phaseable Sample Size Problem: Long-Range Phasing and Loss of Heterozygosity in GWAS

Halldórsson, B. V., Aguiar, D., Tarpine, R., & Istrail, S. (2011) · Journal of Computational Biology · doi:10.1089/cmb.2010.0288 · Cited by 24

Pathway‐Based Approaches for Sequencing‐Based Genome‐Wide Association Studies

Wu, G., & Zhi, D. (2013) · Genetic Epidemiology · doi:10.1002/gepi.21728 · Cited by 24

EMINIM: An Adaptive and Memory-Efficient Algorithm for Genotype Imputation

Kang, H. M., Zaitlen, N., & Eskin, E. (2010) · Journal of Computational Biology · doi:10.1089/cmb.2009.0199 · Cited by 22

Inbred Strain Variant Database (ISVdb): A Repository for Probabilistically Informed Sequence Differences Among the Collaborative Cross Strains and Their Founders

Oreper, D., Cai, Y., Tarantino, L. M., de Villena, F. P., & Valdar, W. S. J. (2017) · G3 Genes Genomes Genetics · doi:10.1534/g3.117.041491 · Cited by 20

Context‐Specific Genomic Selection Strategies Outperform Phenotypic Selection for Soybean Quantitative Traits in the Progeny Row Stage

Smallwood, C., Saxton, A. M., Gillman, J. D., Bhandari, H. S., Wadl, P. A., Fallen, B. D., Hyten, D. L., Song, Q., & Pantalone, V. R. (2019) · Crop Science · doi:10.2135/cropsci2018.03.0197 · Cited by 22

Genomic prediction of growth traits for pigs in the presence of genotype by environment interactions using single‐step genomic reaction norm model

Song, H., Zhang, Q., Misztal, I., & Ding, X. (2020) · Journal of Animal Breeding and Genetics · doi:10.1111/jbg.12499 · Cited by 22

Copy number variations in BOLA‐DQA2 , BOLA‐DQB , and BOLA‐DQA5 show the genomic architecture and haplotype frequency of major histocompatibility complex class II genes in Holstein cows

Fukunaga, K., Yamashita, Y., & Yagisawa, T. (2020) · HLA · doi:10.1111/tan.14086 · Cited by 22

Genomic prediction of growth traits in scallops using convolutional neural networks

Zhu, X., Ni, P., Xing, Q., Wang, Y., Huang, X., Hu, X., Hu, J., Wu, X., & Bao, Z. (2021) · Aquaculture · doi:10.1016/j.aquaculture.2021.737171 · Cited by 23

Hybridization Dynamics and Extensive Introgression in the Daphnia longispina Species Complex: New Insights from a High-Quality Daphnia galeata Reference Genome

Nickel, J. H., Schell, T., Holtzem, T., Thielsch, A., Dennis, S. R., Schlick-Steiner, B. C., Steiner, F. M., Möst, M., Pfenninger, M., Schwenk, K., et al. (2021) · Genome Biology and Evolution · doi:10.1093/gbe/evab267 · Cited by 22

Integration of genome-wide association study and selection signatures reveals genetic determinants for skeletal muscle production traits in an F2 chicken population

Yu-dong, L. I., Bai, X., Liu, X., Wei-jia, W. A. N. G., Zi-wei, L. I., Ning, W. A. N. G., Xiao, F., Gao, H., Guo, H., Li, H., et al. (2022) · Journal of Integrative Agriculture · doi:10.1016/s2095-3119(21)63805-4 · Cited by 22

A recessive genetic model and runs of homozygosity in major depressive disorder

Power, R. A., Keller, M. C., Ripke, S., Abdellaoui, A., Wray, N. R., Sullivan, P. F., MDD PGC Working Group, & Breen, G. D. (2014) · American Journal of Medical Genetics Part B Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32217 · Cited by 22

Pathway-based identification of SNPs predictive of survival

Pang, H., Hauser, M. A., & Minvielle, S. (2011) · European Journal of Human Genetics · doi:10.1038/ejhg.2011.3 · Cited by 22

CNV analysis in Chinese children of mental retardation highlights a sex differentiation in parental contribution to de novo and inherited mutational burdens

Wang, B., Ji, T., Zhou, X., Wang, J., Wang, X., Wang, J., Zhu, D., Zhang, X., Sham, P. C., Zhang, X., et al. (2016) · Scientific Reports · doi:10.1038/srep25954 · Cited by 25

Novel harmful recessive haplotypes for reproductive traits in pigs

Häggman, J., & Uimari, P. (2016) · Journal of Animal Breeding and Genetics · doi:10.1111/jbg.12240 · Cited by 22

Detection of candidate genes for growth and carcass traits using genome-wide association strategy in Chinese Simmental beef cattle

Zhang, W., Xu, L., Gao, H., Wu, Y., Gao, X., Zhang, L., Zhu, B., Song, Y., Bao, J., Li, J., et al. (2016) · Animal Production Science · doi:10.1071/an16165 · Cited by 22

Discovery and fine-mapping of loci associated with MUFAs through trans-ethnic meta-analysis in Chinese and European populations

Hu, Y., Tanaka, T., Zhu, J., Guan, W., Wu, J. H. Y., Psaty, B. M., McKnight, B., King, I. B., Sun, Q., Richard, M. A., et al. (2017) · Journal of Lipid Research · doi:10.1194/jlr.p071860 · Cited by 20

Highly accurate sequence imputation enables precise QTL mapping in Brown Swiss cattle

Frischknecht, M., Pausch, H., Bapst, B., Signer‐Hasler, H., Flury, C., Garrick, D. J., Stricker, C., Fries, R., & Gredler, B. (2017) · BMC Genomics · doi:10.1186/s12864-017-4390-2 · Cited by 22

Genome-Wide Analysis of Genetic Diversity in Plasmodium falciparum Isolates From China–Myanmar Border

Ye, R., Tian, Y., Huang, Y., Zhang, Y., Wang, J., Sun, X., Zhou, H., Zhang, D., & Pan, W. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.01065 · Cited by 22

Imputation Accuracy from Low to Moderate Density Single Nucleotide Polymorphism Chips in a Thai Multibreed Dairy Cattle Population

Jattawa, D., Elzo, M. A., Koonawootrittriron, S., & Suwanasopee, T. (2015) · Asian-Australasian Journal of Animal Sciences · doi:10.5713/ajas.15.0291 · Cited by 22

Allelic expression analysis in the brain suggests a role for heterogeneous insults affecting epigenetic processes in autism spectrum disorders

Ben‐David, E., Shohat, S., & Shifman, S. (2014) · Human Molecular Genetics · doi:10.1093/hmg/ddu128 · Cited by 21

Introgression and incomplete lineage sorting blurred phylogenetic relationships across the genomes of sclerophyllous oaks from southwest China

Ma, X., Ren, Y., & Sun, H. (2024) · Cladistics · doi:10.1111/cla.12570 · Cited by 21

Genome-wide association study and prediction of genomic breeding values for fatty-acid composition in Korean Hanwoo cattle using a high-density single-nucleotide polymorphism array1

Bhuiyan, M. S. A., Kim, Y. K., Kim, H. J., Lee, D. H., Lee, S.-H., Yoon, H. B., & Lee, S. H. (2018) · Journal of Animal Science · doi:10.1093/jas/sky280 · Cited by 21

Cannabinoids and psychotic symptoms: A potential role for a genetic variant in the P2X purinoceptor 7 (P2RX7) gene

Boks, M. P., He, Y., Schubart, C. D., van Gastel, W. A., Elkrief, L., Huguet, G., van Eijk, K. R., Vinkers, C. H., Kahn, R. S., Paus, T., et al. (2020) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2020.04.051 · Cited by 21

Neighbor GWAS: incorporating neighbor genotypic identity into genome-wide association studies of field herbivory

Sato, Y., Yamamoto, E., Shimizu, K. K., & Nagano, A. J. (2021) · Heredity · doi:10.1038/s41437-020-00401-w · Cited by 24

Genome-wide association study of drought tolerance in cassava

Silva, P. M. D. S., Sousa, M. B. E., de Oliveira, E. J., Morgante, C. V., de Oliveira, C. R. S., Vieira, S. L., & Borel, J. C. (2021) · Euphytica · doi:10.1007/s10681-021-02800-4 · Cited by 22

Impact of pre- and post-variant filtration strategies on imputation

Charon, C., Allodji, R. S., Meyer, V., & Deleuze, J. (2021) · Scientific Reports · doi:10.1038/s41598-021-85333-z · Cited by 23

Evaluation of Bayesian alphabet and GBLUP based on different marker density for genomic prediction in Alpine Merino sheep

Zhu, S., Guo, T., Yuan, C., Liu, J., Li, J., Han, M., Zhao, H., Wu, Y., Sun, W., Wang, X., et al. (2021) · G3 Genes Genomes Genetics · doi:10.1093/g3journal/jkab206 · Cited by 21

Genomic and transcriptomic analyses enable the identification of important genes associated with subcutaneous fat deposition in Holstein cows

Zhang, H., Mi, S., Brito, L. F., Hu, L., Wang, L., Ma, L., Xu, Q., Guo, G., Yu, Y., & Wang, Y. (2023) · Journal of genetics and genomics/Journal of Genetics and Genomics · doi:10.1016/j.jgg.2023.01.011 · Cited by 21

Age-dependent genetic architectures of chicken body weight explored by multidimensional GWAS and molQTL analyses

Zhong, C., Li, X., Guan, D., Zhang, B., Wang, X., Qu, L., Zhou, H., Fang, L., Sun, C., & Yang, N. (2024) · Journal of genetics and genomics/Journal of Genetics and Genomics · doi:10.1016/j.jgg.2024.09.003 · Cited by 22

Fine mapping of the chromosome 10q11-q21 linkage region in Alzheimer's disease cases and controls

Fallin, M. D., Szymanski, M., Wang, R., Gherman, A., Bassett, S. S., & Avramopoulos, D. (2010) · Neurogenetics · doi:10.1007/s10048-010-0234-9 · Cited by 21

Genotype Imputation for Latinos Using the HapMap and 1000 Genomes Project Reference Panels

Gao, X., Marjoram, P., McKean‐Cowdin, R., Torres, M., Gauderman, W. J., & Varma, R. (2012) · Frontiers in Genetics · doi:10.3389/fgene.2012.00117 · Cited by 21

Bootstrap study of genome-enabled prediction reliabilities using haplotype blocks across Nordic Red cattle breeds

Cuyabano, B. C. D., Su, G., Rosa, G. J. M., Lund, M. S., & Daniel Gianola, B. (2015) · Journal of Dairy Science · doi:10.3168/jds.2015-9360 · Cited by 21

Including different groups of genotyped females for genomic prediction in a Nordic Jersey population

Gao, H., Madsen, P., Nielsen, U., Aamand, G. P., Su, G., Byskov, K., & Jensen, J. (2015) · Journal of Dairy Science · doi:10.3168/jds.2015-9947 · Cited by 21

Exact Inference for Hardy-Weinberg Proportions with Missing Genotypes: Single and Multiple Imputation

Graffelman, J., Nelson, S. C., Gogarten, S. M., & Weir, B. S. (2015) · G3 Genes Genomes Genetics · doi:10.1534/g3.115.022111 · Cited by 21

The influence of genetic susceptibility and calcium plus vitamin D supplementation on fracture risk

Wang, Y., Wactawski‐Wende, J., Sucheston‐Campbell, L. E., Preus, L. M., Hovey, K. M. O., Nie, J., Jackson, R. D., Handelman, S. K., Nassir, R., Crandall, C. J., et al. (2017) · American Journal of Clinical Nutrition · doi:10.3945/ajcn.116.144550 · Cited by 21

A genomewide association study in divergently selected lines in rabbits reveals novel genomic regions associated with litter size traits

Sosa‐Madrid, B. S., Santacreu, M. A., Blasco, A., Fontanesi, L., Pena, R. N., & Ibáñez‐Escriche, N. (2019) · Journal of Animal Breeding and Genetics · doi:10.1111/jbg.12451 · Cited by 21

Extensive haplotypes are associated with population differentiation and environmental adaptability in Upland cotton (Gossypium hirsutum)

Dai, P., Sun, G., Jia, Y., Pan, Z., Tian, Y., Peng, Z., Li, H., He, S., & Du, X. (2020) · Theoretical and Applied Genetics · doi:10.1007/s00122-020-03668-z · Cited by 23

Fast and accurate long-range phasing in a UK Biobank cohortPreprint

Loh, P., Palamara, P. F., & Price, A. L. (2015) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/028282 · Cited by 24

High-throughput and cost-effective genotyping by low-coverage whole genome sequencing with genotype imputation in Pacific oyster, Crassostrea gigas

Yang, B., Li, Y., Li, Q., & Liu, S. (2024) · Aquaculture · doi:10.1016/j.aquaculture.2024.741134 · Cited by 20

Sequence-based GWAS in 180,000 German Holstein cattle reveals new candidate variants for milk production traits

Križanac, A.-M., Reimer, C., Heise, J., Liu, Z., Pryce, J. E., Bennewitz, J., Thaller, G., Falker‐Gieske, C., & Tetens, J. (2025) · Genetics Selection Evolution · doi:10.1186/s12711-025-00951-9 · Cited by 31

Pre-selecting markers based on fixation index scores improved the power of genomic evaluations in a combined Yorkshire pig population

Ye, S., Song, H., Ding, X., Zhang, Z., & Li, J. (2020) · animal · doi:10.1017/s1751731120000506 · Cited by 20

Improved haplotype inference by exploiting long-range linking and allelic imbalance in RNA-seq datasets

Berger, E., Yörükoğlu, D., Zhang, L., Nyquist, S. K., Shalek, A. K., Kellis, M., Numanagić, I., & Berger, B. (2020) · Nature Communications · doi:10.1038/s41467-020-18320-z · Cited by 18

Genetic pre-screening for glaucoma in population-based epidemiology: protocol for a double-blind prospective screening study within Lifelines (EyeLife)

Neustaeter, A., Nolte, I. M., Snieder, H., & Jansonius, N. M. (2021) · BMC Ophthalmology · doi:10.1186/s12886-020-01771-9 · Cited by 20

Genetic parameters and genome-wide association for milk production traits and somatic cell score in different lactation stages of Shanghai Holstein population

Liu, D., Xu, Z., Zhao, W., Wang, S., Li, T., Zhu, K., Liu, G.-L., Zhao, X., Wang, Q., Pan, Y., et al. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.940650 · Cited by 20

Comparison of methods for the implementation of genome-assisted evaluation of Spanish dairy cattle

Jiménez-Montero, J. A., González-Recio, Ó., & Alenda, R. (2012) · Journal of Dairy Science · doi:10.3168/jds.2012-5631 · Cited by 21

Effect of reference population size and available ancestor genotypes on imputation of Mexican Holstein genotypes

García-Ruíz, A., Lopez, F. J., Wiggans, G. R., Tassell, C. P. V., & Montaldo, H. H. (2015) · Journal of Dairy Science · doi:10.3168/jds.2014-9132 · Cited by 20

Robust Estimation of Local Genetic Ancestry in Admixed Populations Using a Nonparametric Bayesian Approach

Sohn, K.-A., Ghahramani, Z., & Xing, E. P. (2012) · Genetics · doi:10.1534/genetics.112.140228 · Cited by 19

An independent validation association study of carcass quality, shear force, intramuscular fat percentage and omega-3 polyunsaturated fatty acid content with gene markers in Australian lamb

Knight, M. I., Daetwyler, H. D., Hayes, B. J., Hayden, M., Ball, A. J., Pethick, D. W., & McDonagh, M. B. (2013) · Meat Science · doi:10.1016/j.meatsci.2013.07.008 · Cited by 20

Prioritizing animals for dense genotyping in order to impute missing genotypes of sparsely genotyped animals

Yu, X., Woolliams, J. A., & Meuwissen, T. H. E. (2014) · Genetics Selection Evolution · doi:10.1186/1297-9686-46-46 · Cited by 20

An Empirical Comparison of Joint and Stratified Frameworks for Studying G × E Interactions: Systolic Blood Pressure and Smoking in the CHARGE Gene‐Lifestyle Interactions Working Group

Sung, Y. J., Winkler, T. W., Manning, A. K., Aschard, H., Gudnason, V. G., Harris, T. B., Smith, A. V., Boerwinkle, E., Brown, M. R., Morrison, A. C., et al. (2016) · Genetic Epidemiology · doi:10.1002/gepi.21978 · Cited by 19

Improved imputation accuracy in Hispanic/Latino populations with larger and more diverse reference panels: applications in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL)

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Identification of potential genomic regions and candidate genes for egg albumen quality by a genome-wide association study

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