Hail-Powered Science

If you use Hail for published work, please cite the software. You can get a citation for the version of Hail you installed by executing:

import hail as hl
print(hl.citation())

Or you could include the following line in your bibliography:

Hail Team. Hail 0.2. https://github.com/hail-is/hail

Otherwise, we welcome you to add additional examples by editing this page directly, after which we will review the pull request to confirm the addition is valid. Please adhere to the existing formatting conventions.

Last updated on February 22, 2024

2024

• Kwak, S.H., Srinivasan, S., Chen, L. et al. Genetic architecture and biology of youth-onset type 2 diabetes. Nat Metab 6, 226–237 (2024). https://doi.org/10.1038/s42255-023-00970-0 https://www.nature.com/articles/s42255-023-00970-0

• Zhao, S., Crouse, W., Qian, S. et al. Adjusting for genetic confounders in transcriptome-wide association studies improves discovery of risk genes of complex traits. Nat Genet 56, 336–347 (2024). https://doi.org/10.1038/s41588-023-01648-9 https://www.nature.com/articles/s41588-023-01648-9

2023

• Lee, S., Kim, J. & Ohn, J.H. Exploring quantitative traits-associated copy number deletions through reanalysis of UK10K consortium whole genome sequencing cohorts. BMC Genomics 24, 787 (2023). https://doi.org/10.1186/s12864-023-09903-3 https://link.springer.com/article/10.1186/s12864-023-09903-3

• Langlieb, J., Sachdev, N.S., Balderrama, K.S. et al. The molecular cytoarchitecture of the adult mouse brain. Nature 624, 333–342 (2023). https://doi.org/10.1038/s41586-023-06818-7 https://www.nature.com/articles/s41586-023-06818-7

• Leońska-Duniec, A., Borczyk, M., Korostyński, M. et al. Genetic variants in myostatin and its receptors promote elite athlete status. BMC Genomics 24, 761 (2023). https://doi.org/10.1186/s12864-023-09869-2 https://link.springer.com/article/10.1186/s12864-023-09869-2

• Chen, S., Francioli, L.C., Goodrich, J.K. et al. A genomic mutational constraint map using variation in 76,156 human genomes. Nature 625, 92–100 (2024). https://doi.org/10.1038/s41586-023-06045-0 https://www.nature.com/articles/s41586-023-06045-0

• Mosca, M.J., Cho, H. Reconstruction of private genomes through reference-based genotype imputation. Genome Biol 24, 271 (2023). https://doi.org/10.1186/s13059-023-03105-6 https://link.springer.com/article/10.1186/s13059-023-03105-6

• Stöberl, N., Donaldson, J., Binda, C.S. et al. Mutant huntingtin confers cell-autonomous phenotypes on Huntington’s disease iPSC-derived microglia. Sci Rep 13, 20477 (2023). https://doi.org/10.1038/s41598-023-46852-z https://www.nature.com/articles/s41598-023-46852-z

• Tamman, A.J.F., Koller, D., Nagamatsu, S. et al. Psychosocial moderators of polygenic risk scores of inflammatory biomarkers in relation to GrimAge. Neuropsychopharmacol. 49, 699–708 (2024). https://doi.org/10.1038/s41386-023-01747-5 https://www.nature.com/articles/s41386-023-01747-5

• Mignogna, G., Carey, C.E., Wedow, R. et al. Patterns of item nonresponse behaviour to survey questionnaires are systematic and associated with genetic loci. Nat Hum Behav 7, 1371–1387 (2023). https://doi.org/10.1038/s41562-023-01632-7 https://www.nature.com/articles/s41562-023-01632-7

• Al-Jumaan, M., Chu, H., Alsulaiman, A. et al. Interplay of Mendelian and polygenic risk factors in Arab breast cancer patients. Genome Med 15, 65 (2023). https://doi.org/10.1186/s13073-023-01220-4 https://genomemedicine.biomedcentral.com/articles/10.1186/s13073-023-01220-4

• Ilves N, Pajusalu S, Kahre T, et al. High Prevalence of Collagenopathies in Preterm- and Term-Born Children With Periventricular Venous Hemorrhagic Infarction. Journal of Child Neurology. 2023;38(6-7):373-388. doi:10.1177/08830738231186233. https://journals.sagepub.com/doi/full/10.1177/08830738231186233

• Mignogna, G., Carey, C.E., Wedow, R. et al. Patterns of item nonresponse behaviour to survey questionnaires are systematic and associated with genetic loci. Nat Hum Behav 7, 1371–1387 (2023). https://doi.org/10.1038/s41562-023-01632-7 https://www.nature.com/articles/s41562-023-01632-7

• Josefine U Melchiorsen, Kimmie V Sørensen, Jette Bork-Jensen, Hüsün S Kizilkaya, Lærke S Gasbjerg, Alexander S Hauser, Jørgen Rungby, Henrik T Sørensen, Allan Vaag, Jens S Nielsen, Oluf Pedersen, Allan Linneberg, Bolette Hartmann, Anette P Gjesing, Jens J Holst, Torben Hansen, Mette M Rosenkilde, Niels Grarup, Rare Heterozygous Loss-of-Function Variants in the Human GLP-1 Receptor Are Not Associated With Cardiometabolic Phenotypes, The Journal of Clinical Endocrinology & Metabolism, Volume 108, Issue 11, November 2023, Pages 2821–2833, https://doi.org/10.1210/clinem/dgad290. https://academic.oup.com/jcem/article/108/11/2821/7180819

• Vukadinovic, Milos et al. Deep learning-enabled analysis of medical images identifies cardiac sphericity as an early marker of cardiomyopathy and related outcomes. Med, Volume 4, Issue 4, 252 - 262.e3. https://www.cell.com/med/fulltext/S2666-6340(23)00069-7

• Epi25 Collaborative; Chen S, Neale BM, Berkovic SF. Shared and distinct ultra-rare genetic risk for diverse epilepsies: A whole-exome sequencing study of 54,423 individuals across multiple genetic ancestries. medRxiv [Preprint]. 2023 Feb 24:2023.02.22.23286310. doi: 10.1101/2023.02.22.23286310. PMID: 36865150; PMCID: PMC9980234. https://pubmed.ncbi.nlm.nih.gov/36865150/

• Kurki, M.I., Karjalainen, J., Palta, P. et al. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature 613, 508–518 (2023). https://doi.org/10.1038/s41586-022-05473-8 https://www.nature.com/articles/s41586-022-05473-8

• Mortensen, Ó., Thomsen, E., Lydersen, L.N. et al. FarGen: Elucidating the distribution of coding variants in the isolated population of the Faroe Islands. Eur J Hum Genet 31, 329–337 (2023). https://doi.org/10.1038/s41431-022-01227-2 https://www.nature.com/articles/s41431-022-01227-2

• Steiner, H.E., Carrion, K.C., Giles, J.B., Lima, A.R., Yee, K., Sun, X., Cavallari, L.H., Perera, M.A., Duconge, J. and Karnes, J.H. (2023), Local Ancestry-Informed Candidate Pathway Analysis of Warfarin Stable Dose in Latino Populations. Clin Pharmacol Ther, 113: 680-691. https://doi.org/10.1002/cpt.2787 https://ascpt.onlinelibrary.wiley.com/doi/full/10.1002/cpt.2787

2022

• Huang, J., Tao, Q., Ang, T.F.A. et al. The impact of increasing levels of blood C-reactive protein on the inflammatory loci SPI1 and CD33 in Alzheimer’s disease. Transl Psychiatry 12, 523 (2022). https://doi.org/10.1038/s41398-022-02281-6 https://www.nature.com/articles/s41398-022-02281-6

• Wadon, M.E., Fenner, E., Kendall, K.M. et al. Clinical and genotypic analysis in determining dystonia non-motor phenotypic heterogeneity: a UK Biobank study. J Neurol 269, 6436–6451 (2022). https://doi.org/10.1007/s00415-022-11307-4 https://link.springer.com/article/10.1007/s00415-022-11307-4

• Andi Madihah Manggabarani, Takuyu Hashiguchi, Masatsugu Hashiguchi, Atsushi Hayashi, Masataka Kikuchi, Yusdar Mustamin, Masaru Bamba, Kunihiro Kodama, Takanari Tanabata, Sachiko Isobe, Hidenori Tanaka, Ryo Akashi, Akihiro Nakaya, Shusei Sato, Construction of prediction models for growth traits of soybean cultivars based on phenotyping in diverse genotype and environment combinations, DNA Research, Volume 29, Issue 4, August 2022, dsac024, https://doi.org/10.1093/dnares/dsac024 https://academic.oup.com/dnaresearch/article/29/4/dsac024/6653298?login=false

• Chaffin, M., Papangeli, I., Simonson, B. et al. Single-nucleus profiling of human dilated and hypertrophic cardiomyopathy. Nature 608, 174–180 (2022). https://doi.org/10.1038/s41586-022-04817-8 https://www.nature.com/articles/s41586-022-04817-8

• Lee, J., Lee, J., Jeon, S. et al. A database of 5305 healthy Korean individuals reveals genetic and clinical implications for an East Asian population. Exp Mol Med 54, 1862–1871 (2022). https://doi.org/10.1038/s12276-022-00871-4 https://www.nature.com/articles/s12276-022-00871-4

• Akingbuwa, W.A., Hammerschlag, A.R., Bartels, M. et al. Ultra-rare and common genetic variant analysis converge to implicate negative selection and neuronal processes in the aetiology of schizophrenia. Mol Psychiatry 27, 3699–3707 (2022). https://doi.org/10.1038/s41380-022-01621-8 https://www.nature.com/articles/s41380-022-01621-8

• Mitja, K.I., et al. FinnGen: Unique genetic insights from combining isolated population and national health register data. medRxiv 2022.03.03.22271360; doi: https://doi.org/10.1101/2022.03.03.22271360. https://www.medrxiv.org/content/10.1101/2022.03.03.22271360v1

• Akingbuwa, O. A. (2022). Polygenic analyses of childhood and adult psychopathology, and their overlap. [PhD- Thesis - Research and graduation internal, Vrije Universiteit Amsterdam]. https://research.vu.nl/ws/portalfiles/portal/149553301/O+A++Akingbuwa+-+thesis.pdf

2021

• Atkinson, E.G., et al. "Tractor uses local ancestry to enable the inclusion of admixed individuals in GWAS and to boost power", Nature Genetics (2021). https://doi.org/10.1038/s41588-020-00766-y https://www.nature.com/articles/s41588-020-00766-y

• Maes, H.H. "Notes on Three Decades of Methodology Workshops", Behavior Genetics (2021). https://doi.org/10.1007/s10519-021-10049-9 https://link.springer.com/article/10.1007/s10519-021-10049-9

• Malanchini, M., et al. "Pathfinder: A gamified measure to integrate general cognitive ability into the biological, medical and behavioural sciences.", bioRxiv (2021). https://www.biorxiv.org/content/10.1101/2021.02.10.430571v1.abstract https://www.biorxiv.org/content/10.1101/2021.02.10.430571v1.abstract

2020

• Zekavat, S.M., et al. "Hematopoietic mosaic chromosomal alterations and risk for infection among 767,891 individuals without blood cancer", medRxiv (2020). https://doi.org/10.1101/2020.11.12.20230821 https://europepmc.org/article/ppr/ppr238896

• Kwong, A.K., et al. "Exome Sequencing in Paediatric Patients with Movement Disorders with Treatment Possibilities", Research Square (2020). https://doi.org/10.21203/rs.3.rs-101211/v1 https://europepmc.org/article/ppr/ppr235428

• Krissaane, I, et al. “Scalability and cost-effectiveness analysis of whole genome-wide association studies on Google Cloud Platform and Amazon Web Services”, Journal of the American Medical Informatics Association (2020) ocaa068 https://doi.org/10.1093/jamia/ocaa068 https://academic.oup.com/jamia/article/doi/10.1093/jamia/ocaa068/5876972

• Karaca M, Atceken N, Karaca Ş, Civelek E, Şekerel BE, Polimanti R. “Phenotypic and Molecular Characterization of Risk Loci Associated With Asthma and Lung Function” Allergy Asthma Immunol Res. (2020) 12(5):806-820. https://doi.org/10.4168/aair.2020.12.5.806 https://e-aair.org/DOIx.php?id=10.4168/aair.2020.12.5.806

• Muniz Carvalho, C., Wendt, F.R., Maihofer, A.X. et al. Dissecting the genetic association of C-reactive protein with PTSD, traumatic events, and social support. Neuropsychopharmacol. (2020). https://doi.org/10.1038/s41386-020-0655-6 https://www.nature.com/articles/s41386-020-0655-6#citeas

2019

• Farhan, Sali MK, et al. “Exome sequencing in amyotrophic lateral sclerosis implicates a novel gene, DNAJC7, encoding a heat-shock protein” Nature Neuroscience (2019): 307835. https://www.nature.com/articles/s41593-019-0530-0

• Gay, Nicole R. et al. “Impact of admixture and ancestry on eQTL analysis and GWAS colocalization in GTEx” bioRxiv (2019) 836825; https://www.biorxiv.org/content/10.1101/836825v1

• Sakaue, Saori et al. “Trans-biobank analysis with 676,000 individuals elucidates the association of polygenic risk scores of complex traits with human lifespan” bioRxiv (2019): 856351 https://www.biorxiv.org/content/10.1101/856351v1

• Polimanti, Renato et al. “Leveraging genome-wide data to investigate differences between opioid use vs. opioid dependence in 41,176 individuals from the Psychiatric Genomics Consortium” bioRxiv (2019): 765065 https://www.biorxiv.org/content/10.1101/765065v1

• Lescai, Francesco et al. “Meta-analysis of Scandinavian Schizophrenia Exomes” bioRxiv (2019): 836957; https://www.biorxiv.org/content/10.1101/836957v2

• Bolze, Alexandre, et al. “Selective constraints and pathogenicity of mitochondrial DNA variants inferred from a novel database of 196,554 unrelated individuals” bioRxiv (2019): 798264;https://www.biorxiv.org/content/10.1101/798264v1

• De Lillo, A., De Angelis, F., Di Girolamo, M. et al. “Phenome-wide association study of TTR and RBP4 genes in 361,194 individuals reveals novel insights in the genetics of hereditary and wildtype transthyretin amyloidoses.” Hum Genet 138, 1331–1340 (2019). https://www.ncbi.nlm.nih.gov/pubmed/31659433

• Pividori, Milton, et al. “Shared and distinct genetic risk factors for childhood-onset and adult-onset asthma: genome-wide and transcriptome-wide studies.” The Lancet Respiratory Medicine 7.6 (2019): 509-522. https://www.biorxiv.org/content/10.1101/427427v2

• Werling, Donna, et al. “Whole-genome and RNA sequencing reveal variation and transcriptomic coordination in the developing human prefrontal cortex.” bioRxiv (2019): 538421. https://www.biorxiv.org/content/10.1101/585430v1

• Satterstrom, Kyle F., et al. “Large-scale exome sequencing study implicates both developmental and functional changes in the neurobiology of autism.” bioRxiv (2019): 538421. https://www.biorxiv.org/content/10.1101/484113v3

• Huang, Qin, et al. “Delivering genes across the blood-brain barrier: LY6A, a novel cellular receptor for AAV-PHP. B capsids.” bioRxiv (2019): 538421. https://www.biorxiv.org/content/10.1101/538421v1

• Kurki, Mitja I., et al. “Contribution of rare and common variants to intellectual disability in a sub-isolate of Northern Finland.” Nature Communications 10.1 (2019): 410. https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/30679432/

• Martin, Alicia R., et al. “Current clinical use of polygenic scores will risk exacerbating health disparities.” bioRxiv (2019): 441261. https://www.biorxiv.org/content/10.1101/441261v3

• Collaborative, Epi25, et al. “Ultra-rare genetic variation in the epilepsies: a whole-exome sequencing study of 17,606 individuals.” American Journal of Human Genetics (2019): https://www.cell.com/ajhg/fulltext/S0002-9297(19)30207-1

• Karczewski, Konrad J., et al. “The mutational constraint spectrum quantified from variation in 141,456 humans.” bioRxiv (2019): 531210. https://www.biorxiv.org/content/10.1101/531210v4

• Whiffin, Nicola, et al. “Human loss-of-function variants suggest that partial LRRK2 inhibition is a safe therapeutic strategy for Parkinsons disease.” bioRxiv() (2019): 561472. https://www.biorxiv.org/content/10.1101/561472v1

• Cummings, Beryl B., et al. “Transcript expression-aware annotation improves rare variant discovery and interpretation.” bioRxiv (2019): 554444. https://www.biorxiv.org/content/10.1101/554444v1

• Wang, Qingbo, et al. “Landscape of multi-nucleotide variants in 125,748 human exomes and 15,708 genomes.” bioRxiv (2019): 573378. https://www.biorxiv.org/content/10.1101/573378v2

• Minikel, Eric Vallabh, et al. “Evaluating potential drug targets through human loss-of-function genetic variation.” bioRxiv (2019): 530881. https://www.biorxiv.org/content/10.1101/530881v2

• Collins, Ryan L., et al. “An open resource of structural variation for medical and population genetics.” bioRxiv (2019): 578674. https://www.biorxiv.org/content/10.1101/578674v1

• Whiffin, Nicola, et al. “Characterising the loss-of-function impact of 5’untranslated region variants in whole genome sequence data from 15,708 individuals.” bioRxiv (2019): 543504. https://www.biorxiv.org/content/10.1101/543504v1

• Lacaze, Paul, et al. “The Medical Genome Reference Bank: a whole-genome data resource of 4000 healthy elderly individuals. Rationale and cohort design.” European Journal of Human Genetics 27.2 (2019): 308. https://www.nature.com/articles/s41431-018-0279-z

• Cirulli, Elizabeth T., et al. “Genome-wide rare variant analysis for thousands of phenotypes in 54,000 exomes.” bioRxiv (2019): 692368. https://www.biorxiv.org/content/10.1101/692368v1.abstract

• Kerminen, Sini, et al. “Geographic Variation and Bias in the Polygenic Scores of Complex Diseases and Traits in Finland.” American Journal of Human Genetics (2019). https://www.biorxiv.org/content/10.1101/485441v1.abstract

• Jiang, Fan, Kyle Ferriter, and Claris Castillo. “PIVOT: Cost-Aware Scheduling of Data-Intensive Applications in a Cloud-Agnostic System.” https://renci.org/wp-content/uploads/2019/02/Cloud_19.pdf

• Pividori, Milton, et al. “Shared and distinct genetic risk factors for childhood-onset and adult-onset asthma: genome-wide and transcriptome-wide studies.” Lancet Respiratory Medicine 7.6 (2019): 509-522. https://www.biorxiv.org/content/10.1101/427427v2

• Cox, Samantha L., et al. “Genetic contributions to variation in human stature in prehistoric Europe.” bioRxiv (2019): 690545. https://www.biorxiv.org/content/10.1101/690545v1.abstract

• Abrar, Faheem. A Modular Parallel Pipeline Architecture for GWAS Applications in a Cluster Environment. Diss. University of Saskatchewan, 2019. https://harvest.usask.ca/handle/10388/12087

• Khera, Amit V., et al. “Whole-genome sequencing to characterize monogenic and polygenic contributions in patients hospitalized with early-onset myocardial infarction.” Circulation 139.13 (2019): 1593-1602. https://www.ahajournals.org/doi/10.1161/CIRCULATIONAHA.118.035658

2018

• An, Joon-Yong, et al. “Genome-wide de novo risk score implicates promoter variation in autism spectrum disorder.” Science (2018): 1. https://science.sciencemag.org/content/362/6420/eaat6576.full

• Molnos, Sophie Claudia. Metabolites: implications in type 2 diabetes and the effect of epigenome-wide interaction with genetic variation. Diss. Technische Universität München, 2018. https://mediatum.ub.tum.de/1372795f

• Bis, Joshua C., et al. “Whole exome sequencing study identifies novel rare and common Alzheimer’s-associated variants involved in immune response and transcriptional regulation.” Molecular Psychiatry (2018): 1. https://www.nature.com/articles/s41380-018-0112-7

• Gormley, Padhraig, et al. “Common variant burden contributes to the familial aggregation of migraine in 1,589 families.” Neuron 98.4 (2018): 743-753. https://www.ncbi.nlm.nih.gov/pubmed/30189203

• Rivas, Manuel A., et al. “Insights into the genetic epidemiology of Crohn’s and rare diseases in the Ashkenazi Jewish population.” PLoS Genetics 14.5 (2018): e1007329. https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1007329

• Satterstrom, F. Kyle, et al. “ASD and ADHD have a similar burden of rare protein-truncating variants.” bioRxiv (2018): 277707. https://www.biorxiv.org/content/10.1101/277707v1

• Zekavat, Seyedeh M., et al. “Deep coverage whole genome sequences and plasma lipoprotein (a) in individuals of European and African ancestries.” Nature Communications 9.1 (2018): 2606. https://www.nature.com/articles/s41467-018-04668-w

• Natarajan, Pradeep, et al. “Deep-coverage whole genome sequences and blood lipids among 16,324 individuals.” Nature Communications 9.1 (2018): 3391. https://www.nature.com/articles/s41467-018-04668-w

• Ganna, Andrea, et al. “Quantifying the impact of rare and ultra-rare coding variation across the phenotypic spectrum.” American Journal of Human Genetics 102.6 (2018): 1204-1211. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5992130/

• Khera, Amit V., et al. “Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations.” Nature Genetics 50.9 (2018): 1219. https://www.nature.com/articles/s41588-018-0183-z?_ga=2.263293700.980063710.1543017600-1151073636.1543017600

• Roselli, Carolina, et al. “Multi-ethnic genome-wide association study for atrial fibrillation.” Nature Genetics 50.9 (2018): 1225. https://www.nature.com/articles/s41588-018-0133-9

• Arachchi, Harindra, et al. “matchbox: An open‐source tool for patient matching via the Matchmaker Exchange.” Human Mutation 39.12 (2018): 1827-1834. https://onlinelibrary.wiley.com/doi/abs/10.1002/humu.23655

• Laisk, Triin, et al. “GWAS meta-analysis highlights the hypothalamic-pituitary-gonadal axis (HPG axis) in the genetic regulation of menstrual cycle length.” bioRxiv (2018): 333708. https://www.biorxiv.org/content/10.1101/333708v1.abstract

• Rees, Elliott, et al. “Association between schizophrenia and both loss of function and missense mutations in paralog conserved sites of voltage-gated sodium channels.” bioRxiv (2018): 246850. https://www.biorxiv.org/content/10.1101/246850v1.abstract

• Haas, Mary E., et al. “Genetic association of albuminuria with cardiometabolic disease and blood pressure.” American Journal of Human Genetics 103.4 (2018): 461-473. https://www.cell.com/ajhg/pdf/S0002-9297(18)30270-2.pdf

• Abel, Haley J., et al. “Mapping and characterization of structural variation in 17,795 deeply sequenced human genomes.” bioRxiv (2018): 508515. https://www.biorxiv.org/content/10.1101/508515v1.abstract

• Lane, Jacqueline M., et al. “Biological and clinical insights from genetics of insomnia symptoms.” bioRxiv (2018): 257956. https://www.biorxiv.org/content/10.1101/257956v1.abstract

• Pividori, Milton, et al. “Shared and distinct genetic risk factors for childhood onset and adult onset asthma.” bioRxiv (2018): 427427. https://www.biorxiv.org/content/10.1101/427427v1.abstract

2017

• Lessard, Samuel, et al. “Human genetic variation alters CRISPR-Cas9 on-and off-targeting specificity at therapeutically implicated loci.” Proceedings of the National Academy of Sciences 114.52 (2017): E11257-E11266. https://www.pnas.org/content/114/52/E11257.long

2016

• Ganna, Andrea, et al. “Ultra-rare disruptive and damaging mutations influence educational attainment in the general population.” Nature Neuroscience 19.12 (2016): 1563. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5127781/

Footnote In addition to software development, the Hail team engages in theoretical, algorithmic, and empirical research inspired by scientific collaboration. Examples include Loss landscapes of regularized linear autoencoders, Secure multi-party linear regression at plaintext speed, and A synthetic-diploid benchmark for accurate variant-calling evaluation.