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Ann Neurol: Columbia University found that: genetic risk score can effectively predict Alzheimer's disease

 Last Update: 2021-08-02
 Source: Internet
 Author: User

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At present, for patients with LOAD (late-onset AD) or LOAD-endophenotypes, efforts have been made in the development of Polygenic risk scores (PRSs), and good sensitivity and specificity have been achieved in detecting disease states.
Improved the diagnosis algorithm when the recognized variables such as gender, age and APOE are correlated
.

diagnosis

PRS uses summary statistics from a large GWAS work ("training" data set) to construct an informative predictor variable for the same result or related endogenous phenotype in an independent data set ("validation" data set)
.

One major limitation is that when the discovery and validation data sets do not share the same ancestral background, the performance of PRS will be hindered

GWAS uses European ancestry GWAS to construct the PRS of other ethnic groups, and the results are not very satisfactory

Because of their unique genetic background, the incidence and prevalence of the disease is high (twice that of non-Hispanic whites)

In the autopsy cohort, CH-PRS alone reached AUC=72%, which increased to AUC=83% in the full model
.

In the replicating CH cohort, higher CH-PRS was significantly associated with clinical LOAD (OR=1.
61, 95%CI=1.
19-2.
17).
In longitudinal subsamples, CH-PRS was significantly predicted to be converted to LOAD (HR= 1.
93, CI=1.
70-2.
20)
.

EUR-PRS and AA-PRS achieve lower prediction accuracy (AUC=58% and 53%)

The important significance of such research lies in the discovery: the rich diversity of genetic research is essential to provide effective PRS to analyze the LOAD risk of different populations
.

The rich diversity of genetic research is essential to provide effective PRS to analyze the LOAD risk of different populations
.

The rich diversity of genetic research is essential to provide effective PRS to analyze the LOAD risk of different populations

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