Related Experiment Video
Updated: Feb 4, 2026

05:26
Author Spotlight: Oral Candida Diagnosis to Advance Clinical Treatment Regimen for pSS Patients
Published on: March 1, 2024
2.0K
KAT6A Syndrome: genotype-phenotype correlation in 76 patients with pathogenic KAT6A variants
Joanna Kennedy1,2, David Goudie3, Edward Blair4,5
1Clinical Genetics, University Hospitals Bristol, Southwell St, Bristol, UK.
Summary
Pathogenic variants in KAT6A cause syndromic developmental delay. This study expands the known genetic and clinical features of KAT6A syndrome, aiding in management.
Area of Science:
- Genetics
- Developmental Biology
- Clinical Medicine
Background:
- Pathogenic variants in KAT6A are a recently identified cause of syndromic developmental delay.
- The full clinical spectrum and variability of KAT6A syndrome are not yet well-defined.
Purpose of the Study:
- To expand the understanding of the genotypic and phenotypic spectrum of KAT6A syndrome.
- To identify genotype-phenotype correlations and novel clinical associations.
Main Methods:
- Data collection from treating clinicians, an online family survey, and literature review.
- Identification and analysis of unreported and published cases of KAT6A pathogenic variants.
Main Results:
- Identified 52 new cases, totaling 76 published cases.
- Expanded the genotypic spectrum to include missense and splicing mutations.
- Found genotype-phenotype correlations, with late-truncating variants linked to core features and highlighted increased risk for gastrointestinal obstruction.
Conclusions:
- The genotypic and phenotypic spectrum of KAT6A syndrome is expanded.
- Clinical management guidelines for KAT6A syndrome are outlined.
Keywords:
KAT6A syndrome; chromatin modifiers; intellectual disabilitygenetic diagnosisphenotypic spectrumMore Related Videos
Related Concept Videos
Correlations
36.1K
Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
36.1K
Correlation and Causation
42.8K
Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
42.8K
Histone Variants at the Centromere
5.1K
Histone variants are the histone proteins with structural and sequence variations. These variants may be regarded as “mutant” forms that replace their canonical histone counterparts in the nucleosomes. Specific post-translational modifications on the histone variants enable further chromatin complexity and regulate tissue-specific gene expression. The most common histone variants are from histone H2A, H2B, and linker histone H1 families. However, several variants of histone H3...
5.1K
Correlation
15.2K
In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
15.2K
Correlation and Regression
3.4K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
3.4K
Coefficient of Correlation
8.7K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
8.7K

