Genetic Ancestry Classification in POAG

We classified POAG cases and controls with a previously published algorithm developed in the VA (17 ) and applied to the MVP as previously described (13 ). Ancestry groups were defined using the Harmonized Ancestry and Race/Ethnicity (HARE) algorithm (18 (link)), which classifies an individual’s HARE group based on the correspondence of their self-identified race/ethnicity and genetically inferred ancestry.

Partial Protocol Preview
This section provides a glimpse into the protocol.
The remaining content is hidden due to licensing restrictions, but the full text is available at the following link: Access Free Full Text.

Waksmunski A.R., Kinzy T.G., Cruz L.A., Nealon C.L., Halladay C.W., Anthony S.A., Greenberg P.B., Sullivan J.M., Wu W.C., Iyengar S.K., Crawford D.C., Peachey N.S, & Bailey J.N. (2023). Diversity is key for cross-ancestry transferability of glaucoma genetic risk scores in Hispanic Veterans in the Million Veteran Program. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 28, 413-424.

Publication 2023

Ethnicity Poag Race group

Corresponding Organization :

Other organizations : Cornell University, Case Western Reserve University, VA Northeast Ohio Healthcare System, Providence VA Medical Center, Brown University, Providence College, VA Western New York Healthcare System, Cleveland Clinic Lerner College of Medicine, Cleveland Clinic, Cleveland Eye Clinic

Top 5 similar protocols

Variable analysis

independent variables

Classification of POAG cases and controls using a previously published algorithm developed in the VA

dependent variables

Not explicitly mentioned

control variables

Ancestry groups defined using the Harmonized Ancestry and Race/Ethnicity (HARE) algorithm
HARE group based on the correspondence of self-identified race/ethnicity and genetically inferred ancestry

controls

No positive or negative controls were explicitly mentioned in the provided information.

Annotations

Based on most similar protocols

Etiam vel ipsum. Morbi facilisis vestibulum nisl. Praesent cursus laoreet felis. Integer adipiscing pretium orci. Nulla facilisi. Quisque posuere bibendum purus. Nulla quam mauris, cursus eget, convallis ac, molestie non, enim. Aliquam congue. Quisque sagittis nonummy sapien. Proin molestie sem vitae urna. Maecenas lorem.

As authors may omit details in methods from publication, our AI will look for missing critical information across the 5 most similar protocols.

About PubCompare

Our mission is to provide scientists with the largest repository of trustworthy protocols and intelligent analytical tools, thereby offering them extensive information to design robust protocols aimed at minimizing the risk of failures.

We believe that the most crucial aspect is to grant scientists access to a wide range of reliable sources and new useful tools that surpass human capabilities.

However, we trust in allowing scientists to determine how to construct their own protocols based on this information, as they are the experts in their field.

Ready to get started?

Revolutionizing how scientists
search and build protocols!