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505 results on '"Lu, Qiongshi"'

1. ipd: An R Package for Conducting Inference on Predicted Data

Author

Salerno, Stephen, Miao, Jiacheng, Afiaz, Awan, Hoffman, Kentaro, Neufeld, Anna, Lu, Qiongshi, McCormick, Tyler H., and Leek, Jeffrey T.

Subjects
Statistics - Methodology, Statistics - Computation
Abstract

Summary: ipd is an open-source R software package for the downstream modeling of an outcome and its associated features where a potentially sizable portion of the outcome data has been imputed by an artificial intelligence or machine learning (AI/ML) prediction algorithm. The package implements several recent proposed methods for inference on predicted data (IPD) with a single, user-friendly wrapper function, ipd. The package also provides custom print, summary, tidy, glance, and augment methods to facilitate easy model inspection. This document introduces the ipd software package and provides a demonstration of its basic usage. Availability: ipd is freely available on CRAN or as a developer version at our GitHub page: github.com/ipd-tools/ipd. Full documentation, including detailed instructions and a usage `vignette' are available at github.com/ipd-tools/ipd. Contact: jtleek@fredhutch.org and tylermc@uw.edu, Comment: 5 pages, 1 figure

Published
2024

2. Task-Agnostic Machine-Learning-Assisted Inference

Author

Miao, Jiacheng and Lu, Qiongshi

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning, Statistics - Methodology
Abstract

Machine learning (ML) is playing an increasingly important role in scientific research. In conjunction with classical statistical approaches, ML-assisted analytical strategies have shown great promise in accelerating research findings. This has also opened a whole field of methodological research focusing on integrative approaches that leverage both ML and statistics to tackle data science challenges. One type of study that has quickly gained popularity employs ML to predict unobserved outcomes in massive samples, and then uses predicted outcomes in downstream statistical inference. However, existing methods designed to ensure the validity of this type of post-prediction inference are limited to very basic tasks such as linear regression analysis. This is because any extension of these approaches to new, more sophisticated statistical tasks requires task-specific algebraic derivations and software implementations, which ignores the massive library of existing software tools already developed for the same scientific problem given observed data. This severely constrains the scope of application for post-prediction inference. To address this challenge, we introduce a novel statistical framework named PSPS for task-agnostic ML-assisted inference. It provides a post-prediction inference solution that can be easily plugged into almost any established data analysis routines. It delivers valid and efficient inference that is robust to arbitrary choice of ML model, allowing nearly all existing statistical frameworks to be incorporated into the analysis of ML-predicted data. Through extensive experiments, we showcase our method's validity, versatility, and superiority compared to existing approaches. Our software is available at https://github.com/qlu-lab/psps.

Published
2024

5. Assumption-Lean and Data-Adaptive Post-Prediction Inference

Author

Miao, Jiacheng, Miao, Xinran, Wu, Yixuan, Zhao, Jiwei, and Lu, Qiongshi

Subjects
Statistics - Methodology, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

A primary challenge facing modern scientific research is the limited availability of gold-standard data which can be costly, labor-intensive, or invasive to obtain. With the rapid development of machine learning (ML), scientists can now employ ML algorithms to predict gold-standard outcomes with variables that are easier to obtain. However, these predicted outcomes are often used directly in subsequent statistical analyses, ignoring imprecision and heterogeneity introduced by the prediction procedure. This will likely result in false positive findings and invalid scientific conclusions. In this work, we introduce PoSt-Prediction Adaptive inference (PSPA) that allows valid and powerful inference based on ML-predicted data. Its "assumption-lean" property guarantees reliable statistical inference without assumptions on the ML prediction. Its "data-adaptive" feature guarantees an efficiency gain over existing methods, regardless of the accuracy of ML prediction. We demonstrate the statistical superiority and broad applicability of our method through simulations and real-data applications.

Published
2023

14. Alzheimer’s disease genetic risk and cognitive reserve in relationship to long-term cognitive trajectories among cognitively normal individuals

Author

Pettigrew, Corinne, Nazarovs, Jurijs, Soldan, Anja, Singh, Vikas, Wang, Jiangxia, Hohman, Timothy, Dumitrescu, Logan, Libby, Julia, Kunkle, Brian, Gross, Alden L., Johnson, Sterling, Lu, Qiongshi, Engelman, Corinne, Masters, Colin L., Maruff, Paul, Laws, Simon M., Morris, John C., Hassenstab, Jason, Cruchaga, Carlos, Resnick, Susan M., Kitner-Triolo, Melissa H., An, Yang, and Albert, Marilyn

Published
2023
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15. Genetic variants and functional pathways associated with resilience to Alzheimer’s disease

Author

Dumitrescu, Logan, Mahoney, Emily R, Mukherjee, Shubhabrata, Lee, Michael L, Bush, William S, Engelman, Corinne D, Lu, Qiongshi, Fardo, David W, Trittschuh, Emily H, Mez, Jesse, Kaczorowski, Catherine, Saucedo, Hector Hernandez, Widaman, Keith F, Buckley, Rachel, Properzi, Michael, Mormino, Elizabeth, Yang, Hyun-Sik, Harrison, Tessa, Hedden, Trey, Nho, Kwangsik, Andrews, Shea J, Tommet, Doug, Hadad, Niran, Sanders, R Elizabeth, Ruderfer, Douglas M, Gifford, Katherine A, Moore, Annah M, Cambronero, Francis, Zhong, Xiaoyuan, Raghavan, Neha S, Vardarajan, Badri, Initiative, The Alzheimer’s Disease Neuroimaging, Consortium, A4 Study Team Alzheimer’s Disease Genetics, Pericak-Vance, Margaret A, Farrer, Lindsay A, Wang, Li-San, Cruchaga, Carlos, Schellenberg, Gerard, Cox, Nancy J, Haines, Jonathan L, Keene, C Dirk, Saykin, Andrew J, Larson, Eric B, Sperling, Reisa A, Mayeux, Richard, Bennett, David A, Schneider, Julie A, Crane, Paul K, Jefferson, Angela L, and Hohman, Timothy J

Subjects
Biological Psychology, Health Sciences, Psychology, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Neurosciences, Clinical Trials and Supportive Activities, Genetics, Dementia, Aging, Acquired Cognitive Impairment, Behavioral and Social Science, Brain Disorders, Clinical Research, Neurodegenerative, Human Genome, Alzheimer's Disease, Prevention, 2.1 Biological and endogenous factors, Aetiology, Neurological, Good Health and Well Being, Aged, 80 and over, Alzheimer Disease, Brain, Chromosomes, Human, Pair 18, Cognitive Dysfunction, Cognitive Reserve, Female, Genome-Wide Association Study, Genotype, Humans, Male, Polymorphism, Single Nucleotide, Alzheimer's disease, amyloid, resilience, GWAS, reserve, Alzheimer’s Disease Neuroimaging Initiative, Alzheimer’s Disease Genetics Consortium (ADGC), A4 Study Team, Alzheimer’s disease, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery, Biomedical and clinical sciences, Health sciences
Abstract

Approximately 30% of older adults exhibit the neuropathological features of Alzheimer's disease without signs of cognitive impairment. Yet, little is known about the genetic factors that allow these potentially resilient individuals to remain cognitively unimpaired in the face of substantial neuropathology. We performed a large, genome-wide association study (GWAS) of two previously validated metrics of cognitive resilience quantified using a latent variable modelling approach and representing better-than-predicted cognitive performance for a given level of neuropathology. Data were harmonized across 5108 participants from a clinical trial of Alzheimer's disease and three longitudinal cohort studies of cognitive ageing. All analyses were run across all participants and repeated restricting the sample to individuals with unimpaired cognition to identify variants at the earliest stages of disease. As expected, all resilience metrics were genetically correlated with cognitive performance and education attainment traits (P-values < 2.5 × 10-20), and we observed novel correlations with neuropsychiatric conditions (P-values < 7.9 × 10-4). Notably, neither resilience metric was genetically correlated with clinical Alzheimer's disease (P-values > 0.42) nor associated with APOE (P-values > 0.13). In single variant analyses, we observed a genome-wide significant locus among participants with unimpaired cognition on chromosome 18 upstream of ATP8B1 (index single nucleotide polymorphism rs2571244, minor allele frequency = 0.08, P = 2.3 × 10-8). The top variant at this locus (rs2571244) was significantly associated with methylation in prefrontal cortex tissue at multiple CpG sites, including one just upstream of ATPB81 (cg19596477; P = 2 × 10-13). Overall, this comprehensive genetic analysis of resilience implicates a putative role of vascular risk, metabolism, and mental health in protection from the cognitive consequences of neuropathology, while also providing evidence for a novel resilience gene along the bile acid metabolism pathway. Furthermore, the genetic architecture of resilience appears to be distinct from that of clinical Alzheimer's disease, suggesting that a shift in focus to molecular contributors to resilience may identify novel pathways for therapeutic targets.

Published
2020

18. Genome-wide association study of post-traumatic stress disorder reexperiencing symptoms in >165,000 US veterans.

Author

Gelernter, Joel, Sun, Ning, Polimanti, Renato, Pietrzak, Robert, Levey, Daniel, Bryois, Julien, Lu, Qiongshi, Hu, Yiming, Li, Boyang, Radhakrishnan, Krishnan, Aslan, Mihaela, Cheung, Kei-Hoi, Li, Yuli, Rajeevan, Nallakkandi, Sayward, Frederick, Harrington, Kelly, Chen, Quan, Cho, Kelly, Pyarajan, Saiju, Sullivan, Patrick, Quaden, Rachel, Shi, Yunling, Hunter-Zinck, Haley, Gaziano, J, Concato, John, Zhao, Hongyu, and Stein, Murray

Subjects
Adult, Cohort Studies, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Male, Stress Disorders, Post-Traumatic, United States, Veterans, Veterans Health
Abstract

Post-traumatic stress disorder (PTSD) is a major problem among military veterans and civilians alike, yet its pathophysiology remains poorly understood. We performed a genome-wide association study and bioinformatic analyses, which included 146,660 European Americans and 19,983 African Americans in the US Million Veteran Program, to identify genetic risk factors relevant to intrusive reexperiencing of trauma, which is the most characteristic symptom cluster of PTSD. In European Americans, eight distinct significant regions were identified. Three regions had values of P

Published
2019

19. Genome-wide Association Study of Maximum Habitual Alcohol Intake in >140,000 U.S. European and African American Veterans Yields Novel Risk Loci

Author

Gelernter, Joel, Sun, Ning, Polimanti, Renato, Pietrzak, Robert H, Levey, Daniel F, Lu, Qiongshi, Hu, Yiming, Li, Boyang, Radhakrishnan, Krishnan, Aslan, Mihaela, Cheung, Kei-Hoi, Li, Yuli, Rajeevan, Nallakkandi, Sayward, Fred, Harrington, Kelly, Chen, Quan, Cho, Kelly, Honerlaw, Jacqueline, Pyarajan, Saiju, Lencz, Todd, Quaden, Rachel, Shi, Yunling, Hunter-Zinck, Haley, Gaziano, J Michael, Kranzler, Henry R, Concato, John, Zhao, Hongyu, Stein, Murray B, Program, Department of Veterans Affairs Cooperative Studies, and Program, Million Veteran

Subjects
Brain Disorders, Genetics, Human Genome, Substance Misuse, Alcoholism, Alcohol Use and Health, Prevention, Good Health and Well Being, Adolescent, Adult, Black or African American, Aged, Aged, 80 and over, Alcohol Drinking, Alcoholism, Female, Genome-Wide Association Study, Humans, Linear Models, Male, Middle Aged, Receptors, Corticotropin-Releasing Hormone, United States, Veterans, White People, Young Adult, ADH1B, CRHR1, Genome-wide association study, Habitual alcohol use, Million Veteran Program, Department of Veterans Affairs Cooperative Studies Program, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

BackgroundHabitual alcohol use can be an indicator of alcohol dependence, which is associated with a wide range of serious health problems.MethodsWe completed a genome-wide association study in 126,936 European American and 17,029 African American subjects in the Veterans Affairs Million Veteran Program for a quantitative phenotype based on maximum habitual alcohol consumption.ResultsADH1B, on chromosome 4, was the lead locus for both populations: for the European American sample, rs1229984 (p = 4.9 × 10-47); for African American, rs2066702 (p = 2.3 × 10-12). In the European American sample, we identified three additional genome-wide-significant maximum habitual alcohol consumption loci: on chromosome 17, rs77804065 (p = 1.5 × 10-12), at CRHR1 (corticotropin-releasing hormone receptor 1); the protein product of this gene is involved in stress and immune responses; and on chromosomes 8 and 10. European American and African American samples were then meta-analyzed; the associated region at CRHR1 increased in significance to 1.02 × 10-13, and we identified two additional genome-wide significant loci, FGF14 (p = 9.86 × 10-9) (chromosome 13) and a locus on chromosome 11. Besides ADH1B, none of the five loci have prior genome-wide significant support. Post-genome-wide association study analysis identified genetic correlation to other alcohol-related traits, smoking-related traits, and many others. Replications were observed in UK Biobank data. Genetic correlation between maximum habitual alcohol consumption and alcohol dependence was 0.87 (p = 4.78 × 10-9). Enrichment for cell types included dopaminergic and gamma-aminobutyric acidergic neurons in midbrain, and pancreatic delta cells.ConclusionsThe present study supports five novel alcohol-use risk loci, with particularly strong statistical support for CRHR1. Additionally, we provide novel insight regarding the biology of harmful alcohol use.

Published
2019

20. A statistical framework for cross-tissue transcriptome-wide association analysis.

Author

Hu, Yiming, Li, Mo, Lu, Qiongshi, Weng, Haoyi, Wang, Jiawei, Zekavat, Seyedeh M, Yu, Zhaolong, Li, Boyang, Gu, Jianlei, Muchnik, Sydney, Shi, Yu, Kunkle, Brian W, Mukherjee, Shubhabrata, Natarajan, Pradeep, Naj, Adam, Kuzma, Amanda, Zhao, Yi, Crane, Paul K, Alzheimer’s Disease Genetics Consortium,, Lu, Hui, and Zhao, Hongyu

Subjects
Alzheimer’s Disease Genetics Consortium, Humans, Gene Expression Profiling, Gene Expression, Genotype, Polymorphism, Single Nucleotide, Models, Genetic, Genome-Wide Association Study, Transcriptome, Alzheimer’s Disease Genetics Consortium, Polymorphism, Single Nucleotide, Models, Genetic, Medical and Health Sciences, Biological Sciences, Developmental Biology
Abstract

Transcriptome-wide association analysis is a powerful approach to studying the genetic architecture of complex traits. A key component of this approach is to build a model to impute gene expression levels from genotypes by using samples with matched genotypes and gene expression data in a given tissue. However, it is challenging to develop robust and accurate imputation models with a limited sample size for any single tissue. Here, we first introduce a multi-task learning method to jointly impute gene expression in 44 human tissues. Compared with single-tissue methods, our approach achieved an average of 39% improvement in imputation accuracy and generated effective imputation models for an average of 120% more genes. We describe a summary-statistic-based testing framework that combines multiple single-tissue associations into a powerful metric to quantify the overall gene-trait association. We applied our method, called UTMOST (unified test for molecular signatures), to multiple genome-wide-association results and demonstrate its advantages over single-tissue strategies.

Published
2019

27. Post‐GWAS multiomic functional investigation of the TNIP1 locus in Alzheimer's disease highlights a potential role for GPX3

Author

Panyard, Daniel J., primary, Reus, Lianne M., additional, Ali, Muhammad, additional, Liu, Jihua, additional, Deming, Yuetiva K., additional, Lu, Qiongshi, additional, Kollmorgen, Gwendlyn, additional, Carboni, Margherita, additional, Wild, Norbert, additional, Visser, Pieter J., additional, Bertram, Lars, additional, Zetterberg, Henrik, additional, Blennow, Kaj, additional, Gobom, Johan, additional, Western, Dan, additional, Sung, Yun Ju, additional, Carlsson, Cynthia M., additional, Johnson, Sterling C., additional, Asthana, Sanjay, additional, Cruchaga, Carlos, additional, Tijms, Betty M., additional, Engelman, Corinne D., additional, and Snyder, Michael P., additional

Published
2024
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29. Author response: Biobank-wide association scan identifies risk factors for late-onset Alzheimer’s disease and endophenotypes

Author

Yan, Donghui, primary, Hu, Bowen, additional, Darst, Burcu F, additional, Mukherjee, Shubhabrata, additional, Kunkle, Brian W, additional, Deming, Yuetiva, additional, Dumitrescu, Logan, additional, Wang, Yunling, additional, Naj, Adam, additional, Kuzma, Amanda, additional, Zhao, Yi, additional, KANG, HYUNSEUNG, additional, Johnson, Sterling, additional, Carlos, Cruchaga, additional, Hohman, Timothy J, additional, Crane, Paul K, additional, Engelman, Corinne D, additional, and Lu, Qiongshi, additional

Published
2024
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32. Contribution of rare inherited and de novo variants in 2,871 congenital heart disease probands

Author

Jin, Sheng Chih, Homsy, Jason, Zaidi, Samir, Lu, Qiongshi, Morton, Sarah, DePalma, Steven R, Zeng, Xue, Qi, Hongjian, Chang, Weni, Sierant, Michael C, Hung, Wei-Chien, Haider, Shozeb, Zhang, Junhui, Knight, James, Bjornson, Robert D, Castaldi, Christopher, Tikhonoa, Irina R, Bilguvar, Kaya, Mane, Shrikant M, Sanders, Stephan J, Mital, Seema, Russell, Mark W, Gaynor, J William, Deanfield, John, Giardini, Alessandro, Porter, George A, Srivastava, Deepak, Lo, Cecelia W, Shen, Yufeng, Watkins, W Scott, Yandell, Mark, Yost, H Joseph, Tristani-Firouzi, Martin, Newburger, Jane W, Roberts, Amy E, Kim, Richard, Zhao, Hongyu, Kaltman, Jonathan R, Goldmuntz, Elizabeth, Chung, Wendy K, Seidman, Jonathan G, Gelb, Bruce D, Seidman, Christine E, Lifton, Richard P, and Brueckner, Martina

Subjects
Biological Sciences, Genetics, Cardiovascular, Pediatric, Congenital Structural Anomalies, Brain Disorders, Heart Disease, Heart Disease - Coronary Heart Disease, 2.1 Biological and endogenous factors, Aetiology, Adult, Autistic Disorder, Cardiac Myosins, Case-Control Studies, Child, Exome, Female, Gene Expression, Genetic Predisposition to Disease, Genome-Wide Association Study, Growth Differentiation Factor 1, Heart Defects, Congenital, Heterozygote, High-Throughput Nucleotide Sequencing, Homozygote, Humans, Male, Mutation, Myosin Heavy Chains, Pedigree, Risk, Vascular Endothelial Growth Factor Receptor-3, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

Congenital heart disease (CHD) is the leading cause of mortality from birth defects. Here, exome sequencing of a single cohort of 2,871 CHD probands, including 2,645 parent-offspring trios, implicated rare inherited mutations in 1.8%, including a recessive founder mutation in GDF1 accounting for ∼5% of severe CHD in Ashkenazim, recessive genotypes in MYH6 accounting for ∼11% of Shone complex, and dominant FLT4 mutations accounting for 2.3% of Tetralogy of Fallot. De novo mutations (DNMs) accounted for 8% of cases, including ∼3% of isolated CHD patients and ∼28% with both neurodevelopmental and extra-cardiac congenital anomalies. Seven genes surpassed thresholds for genome-wide significance, and 12 genes not previously implicated in CHD had >70% probability of being disease related. DNMs in ∼440 genes were inferred to contribute to CHD. Striking overlap between genes with damaging DNMs in probands with CHD and autism was also found.

Published
2017

33. Analysis of Sex-Specific Gene-by-Cohort and Genetic Correlation-by-Cohort Interaction in Educational and Reproductive Outcomes Using the UK Biobank Data.

Author

Zheng, Boyan, Fletcher, Jason M., Song, Jie, and Lu, Qiongshi

Subjects
ANIMAL sexual behavior, GENETIC correlations, HUMAN phenotype, SOCIAL history, EDUCATIONAL outcomes
Abstract

Synthesizing prior gene-by-cohort (G×C) interaction studies, we theorize that changes in genetic effects by social conditions depend on the level of resource constraints, the distribution and use of resources, structural constraints, and constraints on individual choice. Motivated by the theory, we explored several sex-specific G×C trends across a set of outcomes using 30 birth cohorts of UK Biobank data (N = 400,000). We find that genetic coefficients on years of schooling and secondary educational attainment substantially decrease, but genetic coefficients on college attainments only moderately increase. On the other hand, genetic coefficients for education ranks are stable. Genetic coefficients on reproductive behavior increase for younger cohorts. Additional genetic-correlation-by-cohort analysis shows shifting genetic correlations between education and reproductive behavior. Our results suggest that the G×C patterns are highly heterogenous and that social and genetic factors jointly shape the diversity of human phenotypes. [ABSTRACT FROM AUTHOR]

Published
2024
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34. Exome Sequencing Implicates Impaired GABA Signaling and Neuronal Ion Transport in Trigeminal Neuralgia

Author

Dong, Weilai, Jin, Sheng Chih, Allocco, August, Zeng, Xue, Sheth, Amar H., Panchagnula, Shreyas, Castonguay, Annie, Lorenzo, Louis-Étienne, Islam, Barira, Brindle, Geneviève, Bachand, Karine, Hu, Jamie, Sularz, Agata, Gaillard, Jonathan, Choi, Jungmin, Dunbar, Ashley, Nelson-Williams, Carol, Kiziltug, Emre, Furey, Charuta Gavankar, Conine, Sierra, Duy, Phan Q., Kundishora, Adam J., Loring, Erin, Li, Boyang, Lu, Qiongshi, Zhou, Geyu, Liu, Wei, Li, Xinyue, Sierant, Michael C., Mane, Shrikant, Castaldi, Christopher, López-Giráldez, Francesc, Knight, James R., Sekula, Raymond F., Jr., Simard, J. Marc, Eskandar, Emad N., Gottschalk, Christopher, Moliterno, Jennifer, Günel, Murat, Gerrard, Jason L., Dib-Hajj, Sulayman, Waxman, Stephen G., Barker, Fred G., II, Alper, Seth L., Chahine, Mohamed, Haider, Shozeb, De Koninck, Yves, Lifton, Richard P., and Kahle, Kristopher T.

Published
2020
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38. Associations between common genetic variants and income provide insights about the socioeconomic health gradient

Author

Kweon, Hyeokmoon, primary, Burik, Casper A.P., additional, Ning, Yuchen, additional, Ahlskog, Rafael, additional, Xia, Charley, additional, Abner, Erik, additional, Bao, Yanchun, additional, Bhatta, Laxmi, additional, Faquih, Tariq O., additional, de Feijter, Maud, additional, Fisher, Paul, additional, Gelemanović, Andrea, additional, Giannelis, Alexandros, additional, Hottenga, Jouke-Jan, additional, Khalili, Bita, additional, Lee, Yunsung, additional, Li-Gao, Ruifang, additional, Masso, Jaan, additional, Myhre, Ronny, additional, Palviainen, Teemu, additional, Rietveld, Cornelius A., additional, Teumer, Alexander, additional, Verweij, Renske M., additional, Willoughby, Emily A., additional, Agerbo, Esben, additional, Bergmann, Sven, additional, Boomsma, Dorret I., additional, Børglum, Anders D., additional, Brumpton, Ben M., additional, Davies, Neil Martin, additional, Esko, Tõnu, additional, Gordon, Scott D., additional, Homuth, Georg, additional, Ikram, M. Arfan, additional, Johannesson, Magnus, additional, Kaprio, Jaakko, additional, Kidd, Michael P., additional, Kutalik, Zoltán, additional, Kwong, Alex S.F., additional, Lee, James J., additional, Luik, Annemarie I., additional, Magnus, Per, additional, Marques-Vidal, Pedro, additional, Martin, Nicholas G., additional, Mook-Kanamori, Dennis O., additional, Mortensen, Preben Bo, additional, Oskarsson, Sven, additional, Pedersen, Emil M., additional, Polašek, Ozren, additional, Rosendaal, Frits R., additional, Smart, Melissa C., additional, Snieder, Harold, additional, van der Most, Peter J., additional, Vollenweider, Peter, additional, Völzke, Henry, additional, Willemsen, Gonneke, additional, Beauchamp, Jonathan P., additional, DiPrete, Thomas A., additional, Linnér, Richard Karlsson, additional, Lu, Qiongshi, additional, Morris, Tim T., additional, Okbay, Aysu, additional, Harden, K. Paige, additional, Abdellaoui, Abdel, additional, Hill, W. David, additional, de Vlaming, Ronald, additional, Benjamin, Daniel J., additional, and Koellinger, Philipp D., additional

Published
2024
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40. Exome sequencing implicates genetic disruption of prenatal neuro-gliogenesis in sporadic congenital hydrocephalus

Author

Jin, Sheng Chih, Dong, Weilai, Kundishora, Adam J., Panchagnula, Shreyas, Moreno-De-Luca, Andres, Furey, Charuta G., Allocco, August A., Walker, Rebecca L., Nelson-Williams, Carol, Smith, Hannah, Dunbar, Ashley, Conine, Sierra, Lu, Qiongshi, Zeng, Xue, Sierant, Michael C., Knight, James R., Sullivan, William, Duy, Phan Q., DeSpenza, Tyrone, Reeves, Benjamin C., Karimy, Jason K., Marlier, Arnaud, Castaldi, Christopher, Tikhonova, Irina R., Li, Boyang, Peña, Helena Perez, Broach, James R., Kabachelor, Edith M., Ssenyonga, Peter, Hehnly, Christine, Ge, Li, Keren, Boris, Timberlake, Andrew T., Goto, June, Mangano, Francesco T., Johnston, James M., Butler, William E., Warf, Benjamin C., Smith, Edward R., Schiff, Steven J., Limbrick, Jr, David D., Heuer, Gregory, Jackson, Eric M., Iskandar, Bermans J., Mane, Shrikant, Haider, Shozeb, Guclu, Bulent, Bayri, Yasar, Sahin, Yener, Duncan, Charles C., Apuzzo, Michael L. J., DiLuna, Michael L., Hoffman, Ellen J., Sestan, Nenad, Ment, Laura R., Alper, Seth L., Bilguvar, Kaya, Geschwind, Daniel H., Günel, Murat, Lifton, Richard P., and Kahle, Kristopher T.

Published
2020
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41. Mutations disrupting neuritogenesis genes confer risk for cerebral palsy

Author

Jin, Sheng Chih, Lewis, Sara A., Bakhtiari, Somayeh, Zeng, Xue, Sierant, Michael C., Shetty, Sheetal, Nordlie, Sandra M., Elie, Aureliane, Corbett, Mark A., Norton, Bethany Y., van Eyk, Clare L., Haider, Shozeb, Guida, Brandon S., Magee, Helen, Liu, James, Pastore, Stephen, Vincent, John B., Brunstrom-Hernandez, Janice, Papavasileiou, Antigone, Fahey, Michael C., Berry, Jesia G., Harper, Kelly, Zhou, Chongchen, Zhang, Junhui, Li, Boyang, Zhao, Hongyu, Heim, Jennifer, Webber, Dani L., Frank, Mahalia S. B., Xia, Lei, Xu, Yiran, Zhu, Dengna, Zhang, Bohao, Sheth, Amar H., Knight, James R., Castaldi, Christopher, Tikhonova, Irina R., López-Giráldez, Francesc, Keren, Boris, Whalen, Sandra, Buratti, Julien, Doummar, Diane, Cho, Megan, Retterer, Kyle, Millan, Francisca, Wang, Yangong, Waugh, Jeff L., Rodan, Lance, Cohen, Julie S., Fatemi, Ali, Lin, Angela E., Phillips, John P., Feyma, Timothy, MacLennan, Suzanna C., Vaughan, Spencer, Crompton, Kylie E., Reid, Susan M., Reddihough, Dinah S., Shang, Qing, Gao, Chao, Novak, Iona, Badawi, Nadia, Wilson, Yana A., McIntyre, Sarah J., Mane, Shrikant M., Wang, Xiaoyang, Amor, David J., Zarnescu, Daniela C., Lu, Qiongshi, Xing, Qinghe, Zhu, Changlian, Bilguvar, Kaya, Padilla-Lopez, Sergio, Lifton, Richard P., Gecz, Jozef, MacLennan, Alastair H., and Kruer, Michael C.

Published
2020
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