statistical genetics thesis

INCREASING DIVERSITY WITH STATISTICAL METHODS IN GENETICS STUDIES

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• Affiliation: Gillings School of Global Public Health, Department of Biostatistics
• With the development of genotyping and sequencing technologies, genetics data have become increasingly available, making genetic studies unprecedentedly flourishing. Genetics data are diverse in all means. At genetic variant level, it covers the whole allele frequency spectrum including those ultra-rare variants as the sample size gets larger and larger, but it is still cost-prohibitive to generate sequencing data for every participant. At sample level, we have genetics data across varying genetic ancestry background including recently-admixed individuals, though their sample sizes are still limited. Statistical methods are warranted to effectively extract maximal information from these increasingly diverse yet still limited data, achieving the goal to further promote diversity in genetic studies. In my first dissertation project, I present MagicalRsq, a novel machine-learning based genotype imputation quality calibration. As an estimate of true imputation quality (true R2), MagicalRsq shows better alignment with true R2 compared to the standard state-of-the-art metric Rsq, especially for low frequency or rare variants. MagicalRsq could also achieve 10,000 to 1 million net gains of variants as a post-imputation quality metric compared to the original Rsq, leading to potential more discoveries in downstream association analysis. In my second dissertation project, I present GAUDI, a novel polygenic risk score (PRS) method focusing on recently-admixed individuals. GAUDI outperforms other PRS methods, especially for traits demonstrating ancestral-differential genetic architectures, even compared with methods utilizing summary statistics from much better-powered genome-wide association studies. In my third dissertation project, I extend MagicalRsq to MagicalRsq-X, which allows cross-population model training and is more broadly applicable in complicated real-life scenarios. MagicalRsq-X shows stable performance across different cohorts, and will be valuable for broader real-life scenarios in downstream analysis.
• Biostatistics
• statistical genetics and genomics
• genome-wide association studies
• Bioinformatics
• genetic studies
• polygenic risk score
• genotype imputation
• https://doi.org/10.17615/nsde-d161
• Dissertation
• In Copyright - Educational Use Permitted
• Love, Michael I.
• Mohlke, Karen L.
• Raffield, Laura M.
• Doctor of Philosophy
• University of North Carolina at Chapel Hill Graduate School

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The Fundamentals of Modern Statistical Genetics

• © 2011
• Nan M. Laird 0 ,
• Christoph Lange 1

Department of Biostatistics, Harvard University, Boston, USA

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• Provides cutting edge coverage of current gene mapping approaches grounded in a traditional statistical genetics framework, with emphasis on association studies Provides exercises and solutions to reinforce basic concepts for students at all levels Rigorous coverage of key methods
• Includes supplementary material: sn.pub/extras

Part of the book series: Statistics for Biology and Health (SBH)

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Table of contents (12 chapters)

Front matter, introduction to statistical genetics and background in molecular genetics.

Nan M. Laird, Christoph Lange

Principles of Inheritance: Mendel’s Laws and Genetic Models

Some basic concepts from population genetics, aggregation, heritability and segregation analysis: modeling genetic inheritance without genetic data, the general concepts of gene mapping: linkage, association, linkage disequilibrium and marker maps, basic concepts of linkage analysis, the basics of genetic association analysis, population substructure in association studies, association analysis in family designs, advanced topics.

• Genome Wide Association Studies

Looking Toward the Future

Back matter.

• Gene Mapping
• Statistical Genetics

About this book

From the reviews:

“The book covers the historical perspective, covering the standard models and methods. … The presentation of the material is carefully thought through. There are lots of figures, many in colour, a large number of examples, numerous boxes that highlight particular derivations and computations, and exercises at the ends of the chapters. All topics are clearly discussed with due detail. I would say that, for the budding statistical geneticist, this is a must-have.” (Martin Crowder, International Statistical Review, Vol. 79 (3), 2011)

“A book that focuses on statistical methods for finding links between genes and diseases … is timely. … the authors steer us gently and diligently through material that was developed originally for postgraduate students at the Harvard School of Public Health … . ideal for a statistician intending to research in this area or simply for a curious, sufficiently qualified reader. … a lovely book, and essential reading if you are a budding GWASer, or simply interested in where your next disease will come from.” (G. Wood, Australian & New Zealand Journal of Statistics, Vol. 53 (4), 2011)

“The Fundamentals of Modern Statistical Genetics, by Dr. Nan M. Laird and Dr. Christoph Lange, is a timely reference for both researchers and students. … the book is clearly written, and it is useful for colleagues who are interested in the association analysis. Although the book primarily covers the interesting topic of association analysis, it does touch other interesting topics such as joint linkage and association mapping of complex traits.” (Ruzong Fan, Journal of the American Statistical Association, March, 2013)

Authors and Affiliations

About the authors, bibliographic information.

Book Title : The Fundamentals of Modern Statistical Genetics

Authors : Nan M. Laird, Christoph Lange

Series Title : Statistics for Biology and Health

DOI : https://doi.org/10.1007/978-1-4419-7338-2

Publisher : Springer New York, NY

eBook Packages : Mathematics and Statistics , Mathematics and Statistics (R0)

Copyright Information : The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2011

Hardcover ISBN : 978-1-4419-7337-5 Published: 02 December 2010

Softcover ISBN : 978-1-4614-2775-9 Published: 28 January 2013

eBook ISBN : 978-1-4419-7338-2 Published: 13 December 2010

Series ISSN : 1431-8776

Series E-ISSN : 2197-5671

Edition Number : 1

Number of Pages : XIV, 226

Topics : Statistics for Life Sciences, Medicine, Health Sciences , Human Genetics , Biometrics , Biostatistics , Epidemiology

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Statistical Genetics

Statistical geneticists at SPH develop statistical methods for understanding the genetic basis of human diseases and traits.  These methods involve large-scale data sets from candidate-gene, genome-wide and resequencing studies, using both unrelated and related individuals.  SPH statistical geneticists collaborate with other investigators at SPH and around the world on studies of cancer, heart disease, diabetes, respiratory disease, psychiatric disease, and health-related behaviors (e.g. smoking, diet).  They have close ties to the Program in Quantitative Genomics and Computational Biology and Bioinformatics group at SPH.  Training encompasses basic statistics; Mendelian and population genetics; design and analysis of genetic association studies; gene expression and epigenetic markers; and gene-environment interaction.

Students holding a degree in mathematics, computer science, statistics or a related field and an interest in genetics are invited to apply to our Doctoral or Master’s degree programs.  Faculty in the PGSG advise students in both the Epidemiology and Biostatistics departments. Prospective students can apply to either department. While it is possible to apply to both departments, it is typically not recommended. It is Graduate School policy that an individual may submit no more than three applications during the course of his or her academic career. Prospective students are encouraged to discuss which program will best fit their needs with potential advisors. More details about the application process can be found here .

Postdoctoral training positions are also available, with support coming from individual Principal Investigators or appropriate training grants.  Prospective students or postdoctoral fellows with an interest in statistical genetics at SPH should contact Alkes Price .

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Applying for study | current students | alumni, uw statistical genetics ph.d. alumni, statistical genetics certificate program alumni.

• Statistical Genetics

My research interests include phylogenetics, modeling biological data, statistical analysis of molecular data, and parallel computing. I am particularly interested in reconstructing species phylogenies from multilocus sequences.

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• Functional Magnetic Residence Imaging (fMRI)
• General Statistics
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The Graduate Certificate in Statistical Genetics has paused admissions as of December 2023.

The Graduate Certificate in Statistical Genetics provides opportunities for concentrated education in statistical genetics to graduate students from a variety of disciplines. While primarily focused towards matriculated PhD and MS students at UW, non-matriculated students may also apply. The classes are taught by faculty in Statistics, Biostatistics, and Genome Sciences.

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• Frederick Matsen, Affiliate Associate Professor, Department of Statistics
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• Kenneth Rice, Associate Professor, Department of Biostatistics
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Computational Methods for Population Genetics

Iii: small: novel statistical data analysis approaches for mining human genetics datasets.

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Introduction to Statistical Genetic Analysis  //

Mills, Barban & Tropf

Introducing... 

a comprehensive, modern guide to applied statistical genetic data analysis, accessible to those without a background in molecular biology or genetics.

AVAILABLE NOW

The book is available in a more cost-friendly paperback and is also available for rental as an e-book

ABOUT THE BOOK

Human genetic research is now relevant beyond biology, epidemiology, and the medical sciences, with applications in such fields as psychology, psychiatry, statistics, demography, sociology, and economics. With advances in computing power, the availability of data, and new techniques, it is now possible to integrate large-scale molecular genetic information into research across a broad range of topics. This book offers the first comprehensive introduction to modern applied statistical genetic data analysis that covers theory, data preparation, and analysis of molecular genetic data, with hands-on computer exercises.

It is accessible to students and researchers in any empirically oriented medical, biological, or social science discipline; a background in molecular biology or genetics is not required.

The book first provides foundations for statistical genetic data analysis, including a survey of fundamental concepts, primers on statistics and human evolution, and an introduction to polygenic scores. It then covers the practicalities of working with genetic data, discussing such topics as analytical challenges and data management. Finally, the book presents applications and advanced topics, including polygenic score and gene-environment interaction applications, Mendelian Randomization and instrumental variables, and ethical issues. The software and data used in the book are freely available and can be found on the book's website.

THE AUTHORS

Click Here to Learn More

WHAT THE EXPERTS ARE SAYING

"I am regularly asked to recommend a book that provides a comprehensive overview of statistical genetics methods using accessible language with clear applications to important research questions. Look no further. Mills, Barban, and Tropf provide a superb example of such a book with An Introduction to Statistical Genetic Data Analysis."

JASON BOARDMAN  Professor of Sociology and Health & Society Program Director at the Institute of Behavioral Science, University of Colorado at Boulder

"Want to run some statistical analysis of the torrent of genetic data that is pouring into science these days?  An Introduction to Statistical Genetic Data Analysis is required reading for you. Mills, Barban, and Tropf walk the reader through the basics of what a gene is and march onto advanced data analysis techniques, providing plenty of compelling examples along the way."

DALTON CONLEY Henry Putnam University Professor in Sociology, Princeton University

and author of The Genome Factor

"It is increasingly clear that genetics is not just important for diseases. It contributes to many aspects of human behavior and characteristics. This book is most valuable for those whose basic training was not in statistical genetics, but are starting to incorporate genetic data into their investigations."

AUGUSTINE KONG Professor of Statistical Genetics, University of Oxford

"Contemporary genetic data offers many opportunities, and this book is easily the best available introduction. What is marvelous about the book is how comprehensive and sophisticated it is while remaining clear throughout. The way the book weaves together its explanations with software examples makes it a perfect companion for anyone wanting to better understand what these methods have to offer and how a researcher can actually use them."

JEREMY FREESE  Stanford University

If you have any comments about the book, things you would like to see in the future or on this webpage, please contact us at: melinda.mills [at] nuffield.ox.ac.uk

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Statistical Genetics and advanced analytical methods

Statistical genetics is a quickly developing field involving massive amounts of data (molecular markers, gene expression, proteomics, metabolomics, DNA and RNA sequences, high-throughput phenotyping, etc) that calls for advanced analytical methods. This is one of the main research topics in our group, aiming to develop methodology for sound inference for genetic, genomic and phenotypic data.

We combine a wide expertise in statistics (linear / non-linear models, mixed models and Bayesian statistics) with a diverse educational background (mathematic, statistics, biology, agronomy).

We research methods applied to a wide range of biological data, including simple and complex populations (NAM, MAGIC, etc), and often under a multivariate setting (multiple environments and/or traits). Our research is done within several national and international projects, collaborating with research teams in the academia and industry..

We also participate in training and education via regular courses at the University (BSc, MSc, and PhD), but also via specific courses targeting the academia and industry (genetic linkage mapping, quantitative trait locus (QTL) mapping and association mapping.

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• Perspective
• Published: 13 May 2024

Integrating population genetics, stem cell biology and cellular genomics to study complex human diseases

• Nona Farbehi   ORCID: orcid.org/0000-0001-8461-236X 1 , 2 , 3   na1 ,
• Drew R. Neavin   ORCID: orcid.org/0000-0002-1783-6491 1   na1 ,
• Anna S. E. Cuomo 1 , 4 ,
• Lorenz Studer   ORCID: orcid.org/0000-0003-0741-7987 3 , 5 ,
• Daniel G. MacArthur 4 , 6 &
• Joseph E. Powell   ORCID: orcid.org/0000-0002-5070-4124 1 , 3 , 7  

Nature Genetics volume  56 ,  pages 758–766 ( 2024 ) Cite this article

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• Population genetics
• Transcriptomics

Human pluripotent stem (hPS) cells can, in theory, be differentiated into any cell type, making them a powerful in vitro model for human biology. Recent technological advances have facilitated large-scale hPS cell studies that allow investigation of the genetic regulation of molecular phenotypes and their contribution to high-order phenotypes such as human disease. Integrating hPS cells with single-cell sequencing makes identifying context-dependent genetic effects during cell development or upon experimental manipulation possible. Here we discuss how the intersection of stem cell biology, population genetics and cellular genomics can help resolve the functional consequences of human genetic variation. We examine the critical challenges of integrating these fields and approaches to scaling them cost-effectively and practically. We highlight two areas of human biology that can particularly benefit from population-scale hPS cell studies, elucidating mechanisms underlying complex disease risk loci and evaluating relationships between common genetic variation and pharmacotherapeutic phenotypes.

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Thomson, J. A. Embryonic stem cell lines derived from human blastocysts. Science https://doi.org/10.1126/science.282.5391.1145 (1998).

Takahashi, K. & Yamanaka, S. Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell 126 , 663–676 (2006).

Article   CAS   PubMed   Google Scholar  

Takahashi, K. et al. Induction of pluripotent stem cells from adult human fibroblasts by defined factors. Cell 131 , 861–872 (2007).

Liu, G., David, B. T., Trawczynski, M. & Fessler, R. G. Advances in pluripotent stem cells: history, mechanisms, technologies, and applications. Stem Cell Rev. Rep. 16 , 3–32 (2020).

Article   PubMed   Google Scholar  

Efrat, S. Epigenetic memory: lessons from iPS cells derived from human β cells. Front. Endocrinol. 11 , 614234 (2020).

Article   Google Scholar  

Anderson, R. H. & Francis, K. R. Modeling rare diseases with induced pluripotent stem cell technology. Mol. Cell. Probes 40 , 52–59 (2018).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Spitalieri, P., Talarico, V. R., Murdocca, M., Novelli, G. & Sangiuolo, F. Human induced pluripotent stem cells for monogenic disease modelling and therapy. World J. Stem Cells 8 , 118–135 (2016).

Article   PubMed   PubMed Central   Google Scholar  

Passier, R., Orlova, V. & Mummery, C. Complex tissue and disease modeling using hiPSCs. Cell Stem Cell 18 , 309–321 (2016).

Warren, C. R., Jaquish, C. E. & Cowan, C. A. The NextGen genetic association studies consortium: a foray into in vitro population genetics. Cell Stem Cell 20 , 431–433 (2017).

Visscher, P. M., Brown, M. A., McCarthy, M. I. & Yang, J. Five years of GWAS discovery. Am. J. Hum. Genet. 90 , 7–24 (2012).

Tak, Y. G. & Farnham, P. J. Making sense of GWAS: using epigenomics and genome engineering to understand the functional relevance of SNPs in non-coding regions of the human genome. Epigenetics Chromatin 8 , 57 (2015).

Umans, B. D., Battle, A. & Gilad, Y. Where are the disease-associated eQTLs? Trends Genet. 37 , 109–124 (2021).

Yazar, S. et al. Single-cell eQTL mapping identifies cell type–specific genetic control of autoimmune disease. Science 376 , eabf3041 (2022).

Jerber, J. et al. Population-scale single-cell RNA-seq profiling across dopaminergic neuron differentiation. Nat. Genet. 53 , 304–312 (2021).

Neavin, D. et al. Single cell eQTL analysis identifies cell type-specific genetic control of gene expression in fibroblasts and reprogrammed induced pluripotent stem cells. Genome Biol. 22 , 76 (2021).

Cuomo, A. S. E. et al. Single-cell RNA-sequencing of differentiating iPS cells reveals dynamic genetic effects on gene expression. Nat. Commun. 11 , 810 (2020).

Warren, C. R. et al. Induced pluripotent stem cell differentiation enables functional validation of GWAS variants in metabolic disease. Cell Stem Cell 20 , 547–557 (2017).

Kishore, S. et al. A non-coding disease modifier of pancreatic agenesis identified by genetic correction in a patient-derived iPSC line. Cell Stem Cell 27 , 137–146 (2020).

Magdy, T. et al. RARG variant predictive of doxorubicin-induced cardiotoxicity identifies a cardioprotective therapy. Cell Stem Cell 28 , 2076–2089 (2021).

Bourgeois, S. et al. Towards a functional cure for diabetes using stem cell-derived beta cells: are we there yet? Cells 10 , 191 (2021).

Sharma, A., Sances, S., Workman, M. J. & Svendsen, C. N. Multi-lineage human iPSC-derived platforms for disease modeling and drug discovery. Cell Stem Cell 26 , 309–329 (2020).

Volpato, V. & Webber, C. Addressing variability in iPSC-derived models of human disease: guidelines to promote reproducibility. Dis. Model. Mech. 13 , dmm042317 (2020).

Banovich, N. E. et al. Impact of regulatory variation across human iPSCs and differentiated cells. Genome Res. 28 , 122–131 (2018).

Kilpinen, H. et al. Common genetic variation drives molecular heterogeneity in human iPSCs. Nature 546 , 370–375 (2017).

Panopoulos, A. D. et al. iPSCORE: a resource of 222 iPSC lines enabling functional characterization of genetic variation across a variety of cell types. Stem Cell Rep. 8 , 1086–1100 (2017).

Article   CAS   Google Scholar  

Chen, G., Ning, B. & Shi, T. Single-cell RNA-seq technologies and related computational data analysis. Front. Genet. 10 , 317 (2019).

Elorbany, R. et al. Single-cell sequencing reveals lineage-specific dynamic genetic regulation of gene expression during human cardiomyocyte differentiation. PLoS Genet. 18 , e1009666 (2022).

Ward, M. C., Banovich, N. E., Sarkar, A., Stephens, M. & Gilad, Y. Dynamic effects of genetic variation on gene expression revealed following hypoxic stress in cardiomyocytes. eLife 10 , e57345 (2021).

Shi, Z.-D. et al. Genome editing in hPSCs reveals GATA6 haploinsufficiency and a genetic interaction with GATA4 in human pancreatic development. Cell Stem Cell 20 , 675–688 (2017).

Strober, B. J. et al. Dynamic genetic regulation of gene expression during cellular differentiation. Science 364 , 1287–1290 (2019).

González, F. et al. An iCRISPR platform for rapid, multiplexable, and inducible genome editing in human pluripotent stem cells. Cell Stem Cell 15 , 215–226 (2014).

Barbeira, A. N. et al. Exploiting the GTEx resources to decipher the mechanisms at GWAS loci. Genome Biol. 22 , 49 (2021).

Hamazaki, T., El Rouby, N., Fredette, N. C., Santostefano, K. E. & Terada, N. Concise review: induced pluripotent stem cell research in the era of precision medicine. Stem Cells 35 , 545–550 (2017).

Cuomo, A. S. E. et al. CellRegMap: a statistical framework for mapping context-specific regulatory variants using scRNA-seq. Mol. Syst. Biol. 18 , e10663 (2022).

Cuomo, A. S. E., Nathan, A., Raychaudhuri, S., MacArthur, D. G. & Powell, J. E. Single-cell genomics meets human genetics. Nat. Rev. Genet. 24 , 535–549 (2023).

Mirauta, B. A. et al. Population-scale proteome variation in human induced pluripotent stem cells. eLife 9 , e57390 (2020).

Findley, A. S. et al. Functional dynamic genetic effects on gene regulation are specific to particular cell types and environmental conditions. eLife 10 , e67077 (2021).

Kimura, M. et al. En masse organoid phenotyping informs metabolic-associated genetic susceptibility to NASH. Cell https://doi.org/10.1016/j.cell.2022.09.031 (2022).

Llufrio, E. M., Wang, L., Naser, F. J. & Patti, G. J. Sorting cells alters their redox state and cellular metabolome. Redox Biol. 16 , 381–387 (2018).

Shen, S. et al. Integrating single-cell genomics pipelines to discover mechanisms of stem cell differentiation. Trends Mol. Med. https://doi.org/10.1016/j.molmed.2021.09.006 (2021).

van der Wijst, M. et al. The single-cell eQTLGen consortium. eLife 9 , e52155 (2020).

Soskic, B. et al. Immune disease risk variants regulate gene expression dynamics during CD4 + T cell activation. Nat. Genet. 54 , 817–826 (2022).

Daniszewski, M. et al. Retinal ganglion cell-specific genetic regulation in primary open-angle glaucoma. Cell Genomics 2 , 100142 (2022).

Senabouth, A. et al. Transcriptomic and proteomic retinal pigment epithelium signatures of age-related macular degeneration. Nat. Commun. 13 , 4233 (2022).

Benaglio, P. et al. Mapping genetic effects on cell type-specific chromatin accessibility and annotating complex immune trait variants using single nucleus ATAC-seq in peripheral blood. PLoS Genet. 19 , e1010759 (2023).

Baysoy, A., Bai, Z., Satija, R. & Fan, R. The technological landscape and applications of single-cell multi-omics. Nat. Rev. Mol. Cell Biol. 24 , 695–713 (2023).

Weinshilboum, R. M. & Wang, L. Pharmacogenomics: precision medicine and drug response. Mayo Clin. Proc. 92 , 1711–1722 (2017).

Pirmohamed, M. Personalized pharmacogenomics: predicting efficacy and adverse drug reactions. Annu. Rev. Genom. Hum. Genet. 15 , 349–370 (2014).

Nelson, M. R. et al. The support of human genetic evidence for approved drug indications. Nat. Genet. 47 , 856–860 (2015).

Hay, M., Thomas, D. W., Craighead, J. L., Economides, C. & Rosenthal, J. Clinical development success rates for investigational drugs. Nat. Biotechnol. 32 , 40–51 (2014).

Holmgren, G. et al. Long-term chronic toxicity testing using human pluripotent stem cell-derived hepatocytes. Drug Metab. Dispos. 42 , 1401–1406 (2014).

Kim, J.-H., Kang, M., Jung, J.-H., Lee, S.-J. & Hong, S.-H. Human pluripotent stem cell-derived alveolar epithelial cells as a tool to assess cytotoxicity of particulate matter and cigarette smoke extract. Dev. Reprod. 26 , 155–163 (2022).

Sharma, A. et al. High-throughput screening of tyrosine kinase inhibitor cardiotoxicity with human induced pluripotent stem cells. Sci. Transl. Med. 9 , eaaf2584 (2017).

Han, Y. et al. Identification of SARS-CoV-2 inhibitors using lung and colonic organoids. Nature 589 , 270–275 (2021).

Lam, C. K. & Wu, J. C. Clinical trial in a dish: using patient-derived induced pluripotent stem cells to identify risks of drug-induced cardiotoxicity. Arterioscler. Thromb. Vasc. Biol. 41 , 1019–1031 (2021).

Iwata, R. et al. Mitochondria metabolism sets the species-specific tempo of neuronal development. Science 379 , eabn4705 (2023).

Miller, J. D. et al. Human iPSC-based modeling of late-onset disease via progerin-induced aging. Cell Stem Cell 13 , 691–705 (2013).

Hergenreder, E. et al. Combined small-molecule treatment accelerates maturation of human pluripotent stem cell-derived neurons. Nat. Biotechnol. https://doi.org/10.1038/s41587-023-02031-z (2024).

Fowler, J. L., Ang, L. T. & Loh, K. M. A critical look: challenges in differentiating human pluripotent stem cells into desired cell types and organoids. Wiley Interdiscip. Rev. Dev. Biol. 9 , e368 (2020).

Jiang, S., Feng, W., Chang, C. & Li, G. Modeling human heart development and congenital defects using organoids: how close are we? J. Cardiovasc. Dev. Dis. 9 , 125 (2022).

CAS   PubMed   PubMed Central   Google Scholar  

Tremmel, D. M. et al. Validating expression of beta cell maturation-associated genes in human pancreas development. Front. Cell Dev. Biol. 11 , 1103719 (2023).

Washer, S. J. et al. Single-cell transcriptomics defines an improved, validated monoculture protocol for differentiation of human iPSC to microglia. Sci. Rep. 12 , 19454 (2022).

Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19 , 15 (2018).

Wilson, S. B. et al. DevKidCC allows for robust classification and direct comparisons of kidney organoid datasets. Genome Med. 14 , 19 (2022).

Subramanian, A. et al. Single cell census of human kidney organoids shows reproducibility and diminished off-target cells after transplantation. Nat. Commun. 10 , 5462 (2019).

Kammers, K. et al. Gene and protein expression in human megakaryocytes derived from induced pluripotent stem cells. J. Thromb. Haemost. 19 , 1783–1799 (2021).

De Sousa, P. A. et al. Rapid establishment of the European Bank for induced Pluripotent Stem Cells (EBiSC)—the Hot Start experience. Stem Cell Res. 20 , 105–114 (2017).

Morrison, M. et al. StemBANCC: governing access to material and data in a large stem cell research consortium. Stem Cell Rev. Rep. 11 , 681–687 (2015).

The GTEx Consortium The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369 , 1318–1330 (2020).

Article   PubMed Central   Google Scholar  

Mitchell, J. M., Nemesh, J., Ghosh, S. & Handsaker, R. E. Mapping genetic effects on cellular phenotypes with ‘cell villages’. Preprint at bioRxiv https://doi.org/10.1101/2020.06.29.174383 (2020).

Neavin, D. R. et al. A village in a dish model system for population-scale hiPSC studies. Nat. Commun. 14 , 3240 (2023).

Kang, H. M. et al. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nat. Biotechnol. 36 , 89–94 (2018).

Wells, M. F. et al. Natural variation in gene expression and viral susceptibility revealed by neural progenitor cell villages. Cell Stem Cell 30 , 312–332 (2023).

Neavin, D. et al. Demuxafy : improvement in droplet assignment by integrating multiple single-cell demultiplexing and doublet detection methods. Genome Biol. 25 , 94 (2024).

Xu, J. et al. Genotype-free demultiplexing of pooled single-cell RNA-seq. Genome Biol. 20 , 290 (2019).

Heaton, H. et al. Souporcell: robust clustering of single-cell RNA-seq data by genotype without reference genotypes. Nat. Methods 17 , 615–620 (2020).

Huang, Y., McCarthy, D. J. & Stegle, O. Vireo: Bayesian demultiplexing of pooled single-cell RNA-seq data without genotype reference. Genome Biol. 20 , 273 (2019).

Hindson, B. J. et al. High-throughput droplet digital PCR system for absolute quantitation of DNA copy number. Anal. Chem. 83 , 8604–8610 (2011).

Dong, X. et al. powerEQTL: an R package and shiny application for sample size and power calculation of bulk tissue and single-cell eQTL analysis. Bioinformatics https://doi.org/10.1093/bioinformatics/btab385 (2021).

Schmid, K. T. et al. scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies. Nat. Commun. 12 , 6625 (2021).

Camp, J. G., Platt, R. & Treutlein, B. Mapping human cell phenotypes to genotypes with single-cell genomics. Science 365 , 1401–1405 (2019).

Datlinger, P. et al. Pooled CRISPR screening with single-cell transcriptome readout. Nat. Methods 14 , 297–301 (2017).

Dixit, A. et al. Perturb-Seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167 , 1853–1866 (2016).

Rubin, A. J. et al. Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks. Cell 176 , 361–376 (2019).

Schraivogel, D. et al. Targeted Perturb-seq enables genome-scale genetic screens in single cells. Nat. Methods 17 , 629–635 (2020).

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Acknowledgements

Figures were generated with BioRender.com and further developed by A. Garcia, a scientific illustrator from Bio-Graphics. This research was supported by a National Health and Medical Research Council (NHMRC) Investigator grant (J.E.P., 1175781), research grants from the Australian Research Council (ARC) Special Research Initiative in Stem Cell Science, an ARC Discovery Project (190100825), an EMBO Postdoctoral Fellowship (A.S.E.C.) and an Aligning Science Across Parkinson’s Grant (J.E.P., N.F., D.R.N. and L.S.). J.E.P. is supported by a Fok Family Fellowship.

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These authors contributed equally: Nona Farbehi, Drew R. Neavin.

Authors and Affiliations

Garvan Weizmann Center for Cellular Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia

Nona Farbehi, Drew R. Neavin, Anna S. E. Cuomo & Joseph E. Powell

Graduate School of Biomedical Engineering, University of New South Wales, Sydney, New South Wales, Australia

Nona Farbehi

Aligning Science Across Parkinson’s Collaborative Research Network, Chevy Chase, MD, USA

Nona Farbehi, Lorenz Studer & Joseph E. Powell

Centre for Population Genomics, Garvan Institute of Medical Research, University of New South Wales, Sydney, New South Wales, Australia

Anna S. E. Cuomo & Daniel G. MacArthur

The Center for Stem Cell Biology and Developmental Biology Program, Sloan-Kettering Institute for Cancer Research, New York, NY, USA

Lorenz Studer

Centre for Population Genomics, Murdoch Children’s Research Institute, Melbourne, Victoria, Australia

Daniel G. MacArthur

UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, New South Wales, Australia

Joseph E. Powell

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All authors conceived the topic and wrote and revised the manuscript.

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Correspondence to Joseph E. Powell .

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D.G.M. is a founder with equity in Goldfinch Bio, is a paid advisor to GSK, Insitro, Third Rock Ventures and Foresite Labs, and has received research support from AbbVie, Astellas, Biogen, BioMarin, Eisai, Merck, Pfizer and Sanofi-Genzyme; none of these activities is related to the work presented here. J.E.P. is a founder with equity in Celltellus Laboratory and has received research support from Illumina. The other authors declare no conflict of interest.

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Farbehi, N., Neavin, D.R., Cuomo, A.S.E. et al. Integrating population genetics, stem cell biology and cellular genomics to study complex human diseases. Nat Genet 56 , 758–766 (2024). https://doi.org/10.1038/s41588-024-01731-9

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Received : 24 January 2023

Accepted : 20 March 2024

Published : 13 May 2024

Issue Date : May 2024

DOI : https://doi.org/10.1038/s41588-024-01731-9

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