Computational Approaches to Phenotyping: High-Throughput Phenomics

Lussier, Yves A.; Yang, Liu

doi:10.1513/pats.200607-142jg

Cited by 54 publications

(46 citation statements)

References 62 publications

(35 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…51 Text mining has been proposed for creating deep phenotypes from electronic medical records and pathway analysis strategies have been proposed for mining relational databases of gene-disease associations, further refining deep phenotypes and uncovering new associations. 52 Finally, next-generation sequencing technologies promise to interrogate the genome at the resolution of a single base pair. Deep resequencing, in which a candidate region is sequenced in samples of cases and controls, has successfully identified genes that appear to contain an excess, or "accumulation," of rare sequence changes in a range of phenotypes.…”

Section: Future Direction Of the Genomic Analysis Of Strokementioning

confidence: 99%

Advances in Genomic Analysis of Stroke

Lanktree

Dichgans²,

Hegele³

2010

Stroke

View full text Add to dashboard Cite

Abstract-As a result of technological advances, the genomic analysis of stroke has shifted from candidate gene association studies to genome-wide association studies (GWAS). Agnostic GWAS evaluate up to 90% of common genetic variation in a single experiment, creating an improved framework for identifying novel genetic leads for biochemical and cellular mechanisms underlying stroke. Given the ubiquity of the GWAS approach, it has become essential for stroke researchers and clinicians to be able to interpret GWAS results. Thus, we review the basic elements of design, methods, presentation, and interpretation of GWAS in the context of stroke research. In 8 recent stroke GWAS reports, no single locus has been identified in 2 GWAS at a genome-wide level of significance. Additionally, no significant association signal between stroke and a locus with previous evidence from candidate gene studies of stroke has been identified yet. Some caveats of the approach and future directions for stroke genomics are discussed, including the use of intermediate phenotypes, Mendelian randomization, phenomics, and deep resequencing. Intelligent, appropriately powered, multidisciplinary studies incorporating knowledge from clinical medicine, epidemiology, genetics, and molecular biology will be required to fully characterize the genomic contributors to stroke. (Stroke. 2010;41:825-832.)

show abstract

Section: Future Direction Of the Genomic Analysis Of Strokementioning

confidence: 99%

Advances in Genomic Analysis of Stroke

Lanktree

Dichgans²,

Hegele³

2010

Stroke

View full text Add to dashboard Cite

show abstract

“…In fact, using a plethora of genetic information from high-throughput technologies such as mRNA microarray and GWAS, it is now feasible to ''reverse phenotype'' (whereby associations with genetic markers are used to define phenotype groupings) (9,10) and classify multiple traits based on the similarity of their genetic organization. There is a need to develop innovative computational and informatics tools and high-throughput discovery technologies for both gene network and phenomic research (11) given the large numbers of multiple correlated traits that are common in cohorts performing GWAS. Age-related osteoporotic fractures are common in the United States and represent a major public health threat that is likely to increase in importance as the population ages.…”

mentioning

confidence: 99%

Identification of homogeneous genetic architecture of multiple genetically correlated traits by block clustering of genome-wide associations

Gupta

Cheung

Hsu

et al. 2011

Journal of Bone and Mineral Research

View full text Add to dashboard Cite

Genome-wide association studies (GWAS) using high-density genotyping platforms offer an unbiased strategy to identify new candidate genes for osteoporosis. It is imperative to be able to clearly distinguish signal from noise by focusing on the best phenotype in a genetic study. We performed GWAS of multiple phenotypes associated with fractures [bone mineral density (BMD), bone quantitative ultrasound (QUS), bone geometry, and muscle mass] with approximately 433,000 single-nucleotide polymorphisms (SNPs) and created a database of resulting associations. We performed analysis of GWAS data from 23 phenotypes by a novel modification of a block clustering algorithm followed by gene-set enrichment analysis. A data matrix of standardized regression coefficients was partitioned along both axes-SNPs and phenotypes. Each partition represents a distinct cluster of SNPs that have similar effects over a particular set of phenotypes. Application of this method to our data shows several SNP-phenotype connections. We found a strong cluster of association coefficients of high magnitude for 10 traits (BMD at several skeletal sites, ultrasound measures, cross-sectional bone area, and section modulus of femoral neck and shaft). These clustered traits were highly genetically correlated. Gene-set enrichment analyses indicated the augmentation of genes that cluster with the 10 osteoporosis-related traits in pathways such as aldosterone signaling in epithelial cells, role of osteoblasts, osteoclasts, and chondrocytes in rheumatoid arthritis, and Parkinson signaling. In addition to several known candidate genes, we also identified PRKCH and SCNN1B as potential candidate genes for multiple bone traits. In conclusion, our mining of GWAS results revealed the similarity of association results between bone strength phenotypes that may be attributed to pleiotropic effects of genes. This knowledge may prove helpful in identifying novel genes and pathways that underlie several correlated phenotypes, as well as in deciphering genetic and phenotypic modularity underlying osteoporosis risk. ß

show abstract

“…Studies of this nature are also inherently large in scale, making it additionally necessary that methods employed for measuring phenotypes in this context be high in throughput 3,4 . In establishing methods for phenomics-scale research, tradeoffs between throughput and resolution come into play.…”

Section: Introductionmentioning

confidence: 99%

Using Flatbed Scanners to Collect High-resolution Time-lapsed Images of the Arabidopsis Root Gravitropic Response

Smith

Niewohner

Dewey

et al. 2014

JoVE

View full text Add to dashboard Cite

Research efforts in biology increasingly require use of methodologies that enable high-volume collection of high-resolution data. A challenge laboratories can face is the development and attainment of these methods. Observation of phenotypes in a process of interest is a typical objective of research labs studying gene function and this is often achieved through image capture. A particular process that is amenable to observation using imaging approaches is the corrective growth of a seedling root that has been displaced from alignment with the gravity vector. Imaging platforms used to measure the root gravitropic response can be expensive, relatively low in throughput, and/or labor intensive. These issues have been addressed by developing a high-throughput image capture method using inexpensive, yet high-resolution, flatbed scanners. Using this method, images can be captured every few minutes at 4,800 dpi. The current setup enables collection of 216 individual responses per day. The image data collected is of ample quality for image analysis applications.

show abstract

Computational Approaches to Phenotyping: High-Throughput Phenomics

Cited by 54 publications

References 62 publications

Advances in Genomic Analysis of Stroke

Advances in Genomic Analysis of Stroke

Identification of homogeneous genetic architecture of multiple genetically correlated traits by block clustering of genome-wide associations

Using Flatbed Scanners to Collect High-resolution Time-lapsed Images of the Arabidopsis Root Gravitropic Response

Contact Info

Product

Resources

About