Title |
High-throughput discovery of novel developmental phenotypes
|
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Published in |
Nature, September 2016
|
DOI | 10.1038/nature19356 |
Pubmed ID | |
Authors |
Matthew McKay, Barbara Urban, Caroline Lund, Erin Froeter, Taylor LaCasse, Adrienne Mehalow, Emily Gordon, Leah Rae Donahue, Robert Taft, Peter Kutney, Stephanie Dion, Leslie Goodwin, Susan Kales, Rachel Urban, Kristina Palmer, Fabien Pertuy, Deborah Bitz, Bruno Weber, Patrice Goetz-Reiner, Hughes Jacobs, Elise Le Marchand, Amal El Amri, Leila El Fertak, Hamid Ennah, Dalila Ali-Hadji, Abdel Ayadi, Marie Wattenhofer-Donze, Sylvie Jacquot, Philippe André, Marie-Christine Birling, Guillaume Pavlovic, Tania Sorg, Iva Morse, Frank Benso, Michelle E. Stewart, Carol Copley, Jackie Harrison, Samantha Joynson, Ruolin Guo, Dawei Qu, Shoshana Spring, Lisa Yu, Jacob Ellegood, Lily Morikawa, Xueyuan Shang, Pat Feugas, Amie Creighton, Patricia Castellanos Penton, Ozge Danisment, Nicola Griggs, Catherine L. Tudor, Angela L. Green, Cecilia Icoresi Mazzeo, Emma Siragher, Charlotte Lillistone, Elizabeth Tuck, Diane Gleeson, Debarati Sethi, Tanya Bayzetinova, Jonathan Burvill, Bishoy Habib, Lauren Weavers, Ryea Maswood, Evelina Miklejewska, Michael Woods, Evelyn Grau, Stuart Newman, Caroline Sinclair, Ellen Brown, Shinya Ayabe, Mizuho Iwama, Ayumi Murakami |
Abstract |
Approximately one-third of all mammalian genes are essential for life. Phenotypes resulting from knockouts of these genes in mice have provided tremendous insight into gene function and congenital disorders. As part of the International Mouse Phenotyping Consortium effort to generate and phenotypically characterize 5,000 knockout mouse lines, here we identify 410 lethal genes during the production of the first 1,751 unique gene knockouts. Using a standardized phenotyping platform that incorporates high-resolution 3D imaging, we identify phenotypes at multiple time points for previously uncharacterized genes and additional phenotypes for genes with previously reported mutant phenotypes. Unexpectedly, our analysis reveals that incomplete penetrance and variable expressivity are common even on a defined genetic background. In addition, we show that human disease genes are enriched for essential genes, thus providing a dataset that facilitates the prioritization and validation of mutations identified in clinical sequencing efforts. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 50 | 21% |
United Kingdom | 23 | 10% |
Japan | 11 | 5% |
France | 11 | 5% |
Canada | 10 | 4% |
Germany | 6 | 3% |
Spain | 5 | 2% |
Australia | 5 | 2% |
India | 5 | 2% |
Other | 29 | 12% |
Unknown | 81 | 34% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 140 | 59% |
Scientists | 86 | 36% |
Science communicators (journalists, bloggers, editors) | 5 | 2% |
Practitioners (doctors, other healthcare professionals) | 5 | 2% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 4 | <1% |
Canada | 3 | <1% |
United Kingdom | 3 | <1% |
France | 2 | <1% |
Italy | 2 | <1% |
China | 2 | <1% |
India | 1 | <1% |
Finland | 1 | <1% |
Hungary | 1 | <1% |
Other | 4 | <1% |
Unknown | 893 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 204 | 22% |
Researcher | 194 | 21% |
Student > Master | 75 | 8% |
Student > Bachelor | 75 | 8% |
Student > Doctoral Student | 51 | 6% |
Other | 159 | 17% |
Unknown | 158 | 17% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 311 | 34% |
Agricultural and Biological Sciences | 206 | 22% |
Medicine and Dentistry | 59 | 6% |
Neuroscience | 57 | 6% |
Computer Science | 16 | 2% |
Other | 82 | 9% |
Unknown | 185 | 20% |