Selection device for candidate sequence information for similarity determination, selection method, and use for such device and method
US-2015379197-A1 · Dec 31, 2015 · US
A framework for determining the relative effect of genetic variants
US2016357903A1 · US · A1
| Field | Value |
|---|---|
| Publication number | US-2016357903-A1 |
| Application number | US-201415023355-A |
| Country | US |
| Kind code | A1 |
| Filing date | Sep 20, 2014 |
| Priority date | Sep 20, 2013 |
| Publication date | Dec 8, 2016 |
| Grant date | — |
How to read this patent
A practical reading order for non-experts. Skip the full description unless you need deep technical detail.
- Title
What the patent document calls the invention.
- Abstract
A short plain-language summary of the technical disclosure.
- Assignees and inventors
Who owns or filed the patent and who is credited as inventor.
- Key dates
Filing, priority, publication, and grant dates set the timeline.
- First independent claim
The legal scope of protection — read this for what is actually claimed.
- CPC / IPC classifications
Technology tags used to group this patent with similar filings.
- Citations and related patents
Prior art links and similar publications in this corpus.
Abstract
Official abstract text for this publication.
Current methods for annotating and interpreting human genetic variation typically exploit only a single information type (e.g., conservation) and/or are restricted in scope (e.g., to missense changes). Here, a method for objectively integrating many diverse annotations into a single measure (integrated deleteriousness score, or C-score) for each variant is described. The method may be implemented as a support vector machine (SVM) trained to differentiate high-frequency human-derived alleles from simulated variants. C-scores were precomputed for all 8.6 billion possible human single-nucleotide variants and allow scoring of short insertions-deletions. C-scores correlate with allelic diversity, annotations of functionality, pathogenicity, disease severity, experimentally measured regulatory effects and complex trait associations, and they highly rank known pathogenic variants within individual genomes. The ability of CADD to prioritize functional, deleterious and pathogenic variants across many functional categories, effect sizes and genetic architectures is unmatched by any current single-annotation method.
First claim
Opening claim text (preview).
1 . A method performed by a computing system for determining the relative effect of a genetic variant comprising: applying a machine learning model to a dataset, wherein the dataset comprises one or more genetic variants, each of which is associated with values or states of each of a set of annotations; and calculating an integrated deleteriousness score for each of the one or more genetic variants; wherein the integrated deleteriousness score of each genetic variant is used to determine the relative effect of said genetic variant when compared to a set of reference deleteriousness scores. 2 . The method of claim 1 , wherein the machine learning model is a support vector machine (SVM) model. 3 . The method of claim 2 , wherein the SVM model is trained to distinguish between a set of simulated variants and a set of observed variants. 4 . The method of claim 2 , wherein the SVM model is trained using a linear kernel on features derived from an annotation matrix. 5 . The method of claim 4 , wherein the SVM model fits a hyperplane defined by: 0 = β 0 + ∑ i = 1 166 β i X i + ∑ i = 1 5 ∑ j = 1 5 γ ij 1 { ith Ref category and j th Alt category } + ∑ i = 1 21 ∑ j = 1 21 δ ij 1 { ith oAA category and jth nAA category } + ∑ i = 1 11 τ i W i + ∑ i = 1 17 ∑
Assignees
Inventors
Classifications
ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding · CPC title
- G06F19/22Primary
Physics · mapped topic
Physics · mapped topic
with arrangements for mixing one gas and one liquid · CPC title
Unsupervised data analysis · CPC title
Patent family
Related publications grouped by family.
External sources
Frequently asked questions
Answers are generated from the same data shown on this page.
- What does patent US2016357903A1 cover?
- Current methods for annotating and interpreting human genetic variation typically exploit only a single information type (e.g., conservation) and/or are restricted in scope (e.g., to missense changes). Here, a method for objectively integrating many diverse annotations into a single measure (integrated deleteriousness score, or C-score) for each variant is described. The method may be implement…
- Who is the assignee on this patent?
- Univ Washington Through Its Center For Commercialization, Hudsonsalpha Inst For Biotechnology
- What technology area does this patent fall under?
- Primary CPC classification G06F19/22. Mapped technology areas include Physics.
- When was this patent published?
- Publication date Thu Dec 08 2016 00:00:00 GMT+0000 (Coordinated Universal Time) (A1). Legal status and post-grant events are not shown on this page.
- What related patents are in patentsdb?
- We list 8 related publications on this page (citations in our corpus or others sharing the same primary CPC).