Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Peter Carbonetto"'
Autor:
Peter Carbonetto, Kaixuan Luo, Abhishek Sarkar, Anthony Hung, Karl Tayeb, Sebastian Pott, Matthew Stephens
Publikováno v:
Genome Biology, Vol 24, Iss 1, Pp 1-37 (2023)
Abstract Parts-based representations, such as non-negative matrix factorization and topic modeling, have been used to identify structure from single-cell sequencing data sets, in particular structure that is not as well captured by clustering or othe
Externí odkaz:
https://doaj.org/article/4270b099569c4995a25bdf1893936866
Autor:
Selene M. Clay, Nathan Schoettler, Andrew M. Goldstein, Peter Carbonetto, Matthew Dapas, Matthew C. Altman, Mario G. Rosasco, James E. Gern, Daniel J. Jackson, Hae Kyung Im, Matthew Stephens, Dan L. Nicolae, Carole Ober
Publikováno v:
Genome Medicine, Vol 14, Iss 1, Pp 1-16 (2022)
Abstract Background Genome-wide association studies of asthma have revealed robust associations with variation across the human leukocyte antigen (HLA) complex with independent associations in the HLA class I and class II regions for both childhood-o
Externí odkaz:
https://doaj.org/article/5ad2bffb561c45329d8d724aa87835f6
Publikováno v:
PLoS Genetics, Vol 18, Iss 7, p e1010299 (2022)
In recent work, Wang et al introduced the "Sum of Single Effects" (SuSiE) model, and showed that it provides a simple and efficient approach to fine-mapping genetic variants from individual-level data. Here we present new methods for fitting the SuSi
Externí odkaz:
https://doaj.org/article/95dbe388f9bf465cbf4ab2da092f423f
Publikováno v:
F1000Research, Vol 8 (2019)
Making scientific analyses reproducible, well documented, and easily shareable is crucial to maximizing their impact and ensuring that others can build on them. However, accomplishing these goals is not easy, requiring careful attention to organizati
Externí odkaz:
https://doaj.org/article/1e789e14047d4ad080773629aae125af
Autor:
Eunjung Han, Peter Carbonetto, Ross E. Curtis, Yong Wang, Julie M. Granka, Jake Byrnes, Keith Noto, Amir R. Kermany, Natalie M. Myres, Mathew J. Barber, Kristin A. Rand, Shiya Song, Theodore Roman, Erin Battat, Eyal Elyashiv, Harendra Guturu, Eurie L. Hong, Kenneth G. Chahine, Catherine A. Ball
Publikováno v:
Nature Communications, Vol 8, Iss 1, Pp 1-12 (2017)
Genetic data has led to great advances in our understanding of human evolution and dispersal, but information on more recent events is limited. Here, the authors analyse genotypes from 770,000 US individuals to map the fine-scale population structure
Externí odkaz:
https://doaj.org/article/e99a0ac04ddd404b84d2e573c01d9e36
Autor:
Luisa F Pallares, Peter Carbonetto, Shyam Gopalakrishnan, Clarissa C Parker, Cheryl L Ackert-Bicknell, Abraham A Palmer, Diethard Tautz
Publikováno v:
PLoS Genetics, Vol 11, Iss 11, p e1005607 (2015)
The vertebrate cranium is a prime example of the high evolvability of complex traits. While evidence of genes and developmental pathways underlying craniofacial shape determination is accumulating, we are still far from understanding how such variati
Externí odkaz:
https://doaj.org/article/4ed86157c4ce48ca9ee852f190a718c5
Publikováno v:
PLoS Genetics, Vol 9, Iss 2, p e1003264 (2013)
Both linear mixed models (LMMs) and sparse regression models are widely used in genetics applications, including, recently, polygenic modeling in genome-wide association studies. These two approaches make very different assumptions, so are expected t
Externí odkaz:
https://doaj.org/article/2ffb3e1803984039951d2d478c7437cd
Autor:
Peter Carbonetto, Matthew Stephens
Publikováno v:
PLoS Genetics, Vol 9, Iss 10, p e1003770 (2013)
Pathway analyses of genome-wide association studies aggregate information over sets of related genes, such as genes in common pathways, to identify gene sets that are enriched for variants associated with disease. We develop a model-based approach to
Externí odkaz:
https://doaj.org/article/e746f17553b3466391ea4ce57d8e1449
We introduce mvSuSiE, a multi-trait fine-mapping method for identifying putative causal variants from genetic association data (individual-level or summary data). mvSuSiE learns patterns of shared genetic effects from data, and exploits these pattern
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f33b41dfa739d0eaa0ceec704d1920b7
https://doi.org/10.1101/2023.04.14.536893
https://doi.org/10.1101/2023.04.14.536893
Autor:
Peter Carbonetto, Kaixuan Luo, Abhishek Sarkar, Anthony Hung, Karl Tayeb, Sebastian Pott, Matthew Stephens
Publikováno v:
bioRxiv
“Parts-based” representations of data, such as non-negative matrix factorization and topic modeling, have been used to identify structure from single-cell sequencing data sets, in particular structure that is not as well captured by clustering or
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e92bc5db2eb0e8abfddf2c30b02b21d7
https://europepmc.org/articles/PMC10028846/
https://europepmc.org/articles/PMC10028846/