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Polygenic score: Measurement & Applications
In genetics, a polygenic score (PGS) is a number that summarizes the estimated effect of many genetic variants on an individual's phenotype. The PGS is also called the polygenic index (PGI) or genome-wide score; in the context of disease risk, it is called a polygenic risk score (PRS or PR score) or genetic risk score. The score reflects an individual's…
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Polygenic score topic overview
The analysis highlights Measurement and Applications as prominent areas in the source structure around Polygenic score.
Related topics
54
Source areas
5
Connected nodes
59
Extracted relationships
99
Concept neighborhoods
20
Bridge connections
59
What this topic covers Research coverage
Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.
Application to humans · 22 topics
Overview · 16 topics
Background · 8 topics
Calculation with genome-wide association study · 6 topics
Applications in non-human species · 2 topics
Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.
Key facts & relationships
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Explore all related topics Closing gaps
A structured outline of related entities, concepts and subtopics. Open any item to build a new map centered on it.
Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
How Polygenic score connects Entity context
Entity context groups extracted connections by relationship type. Counts show recurrence in the analyzed source; the examples are research signals, not claims about importance.
The extracted context around Polygenic score shows recurring relationship patterns in the source. For example, Polygenic score → Additionally, Although, Because, Conceptually, Despite, EBV, For, GEBV, GEBVs, In, Mendelian, PGS, Polygenic, PRS, SNP-based, SNPs, The, While Another extracted example is Polygenic score → Celiac, Diabetes, DNA, Familial Hypercholesterolemia, For, Likewise, Moreover, Polygenic, Population, Recognizing, Several, The, This, Thus, Type, Unlike, While. Use these groups to spot repeated connection types before inspecting the individual relationships.
Polygenic score
Top relations
has application· 18
Polygenic score → Additionally, Although, Because, Conceptually, Despite, EBV, For, GEBV, GEBVs, In, Mendelian, PGS, Polygenic, PRS, SNP-based, SNPs, The, While
Relationships are represented as Subject → Predicate → Object triples. Confidence describes extraction strength, not the real-world importance of a relationship; lower-confidence rows may be contextual rather than explicit.
TTTA extracted 99 structured relationships around Polygenic score. Examples in this analysis include learning algorithms for genomic prediction → instance of → They are an active area of research spanning topics and poorer predictive performance in individuals of non-European ancestry limit widespread use → instance of → The clinical utility may therefore still be large even if average measures of prediction performance are moderate.Although issues. The table shows each extracted connection, where it came from and its confidence.
Subject
Predicate
Object
Confidence
Src
learning algorithms for genomic prediction
instance of
They are an active area of research spanning topics
0.80
text
poorer predictive performance in individuals of non-European ancestry limit widespread use
instance of
The clinical utility may therefore still be large even if average measures of prediction performance are moderate.Although issues
0.80
text
several authors have noted that some causal variants for some conditions
instance of
The clinical utility may therefore still be large even if average measures of prediction performance are moderate.Although issues
0.80
text
but not others
instance of
The clinical utility may therefore still be large even if average measures of prediction performance are moderate.Although issues
0.80
text
are shared between Europeans
instance of
The clinical utility may therefore still be large even if average measures of prediction performance are moderate.Although issues
0.80
text
other groups across different continents for
instance of
The clinical utility may therefore still be large even if average measures of prediction performance are moderate.Although issues
0.80
text
age
instance of
including additional information
0.80
text
sex often greatly improves the predictions
instance of
including additional information
0.80
text
height
instance of
as a means to assess group differences in a trait
0.80
text
or to examine changes in a trait over time due to natural selection indicative of a soft selective sweep
instance of
as a means to assess group differences in a trait
0.80
text
Polygenic score
has application
In
0.60
section
Polygenic score
has application
Additionally
0.60
section
No triples extracted yet.
Related concept clusters Concept neighborhoods
Concept neighborhoods group terms that sit near one another in the extracted graph. They are useful for discovering adjacent language and context, but proximity does not by itself imply a factual relationship.
The concept neighborhoods around Polygenic score bring nearby vocabulary together. In this analysis, examples include Scores, Risk and Score. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
Polygenic score
Scores
Risk
Score
Disease
Humans
Clinical
Diseases
May
Study
Used
Traits
Individual
polygenic score
Scores
Risk
Score
Across
Disease
Individual
Humans
May
Snps
Trait
Clinical
Diseases
complex diseases
Many
Risk
Traits
May
Genetic
Human
Polygenic
Typically
Effect
Variants
Scores
Genetics
genome-wide association study
Association
Genome-wide
Study
Scores
Studies
Prediction
Across
Disease
Prs
Size
Effect
Humans
genetic architecture
Risk
Trait
Polygenic
Individual
Variants
Many
Diseases
Score
Disease
Traits
Prs
Use
genetic counseling
Risk
Trait
Polygenic
Individual
Variants
Many
Diseases
Score
Disease
Traits
Prs
Use
genetic code
Risk
Trait
Polygenic
Individual
Variants
Many
Diseases
Score
Disease
Traits
Prs
Use
genome-wide association studies
Association
Genome-wide
Study
Scores
Studies
Methods
Disease
Prs
Size
Effect
Humans
Clinical
Connections between topic areas Semantic bridges
Semantic bridges connect parts of the graph that would otherwise be more separate. A bridge is a structural signal in this analysis, not a claim that the two topic areas are equivalent.
For Polygenic score, one of the stronger structural bridges in this analysis connects Polygenic score with Application to humans. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
Number of nodes, edges, triples, density and central hubs. Use it to gauge the size and connectivity of the map.
Polygenic score
Nodes60
Edges59
Triples99
Avg. degree1.97
Density0.033333
Components1
Source & methodology
TTTA analyzes the structure around Polygenic score to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Measurement & Applications, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
