Computational Analysis of Benign Variants in the ABCA4 Gene

• Kejae Fletcher, Computer Science, University of Delaware

• Esther Biswas, Molecular Bioscience, Biochemistry, Chemistry, University of Delaware

ABCA4, ATP-binding cassette subfamily A member, is a transporter that expels
harmful retinal substances from photoreceptor cells, a process which is crucial for visual
functionality. Variants, or changes, to the ABCA4 gene often lead to blinding disorders such as
Stargardt disease and cone-rod dystrophy. Despite extensive research, the functional
consequences of certain ABCA4 variants remain ambiguous. Classification of these variants as
benign or pathogenic is necessary as this allows more accurate patient diagnosis and care. This
study investigates the most common variants, ABCA4 missense, particularly focusing on those
classified as benign.

Utilizing a refined dataset from the ClinVar database, this research categorizes
missense variants classified as benign. We employ statistical methods to analyze allele
frequencies and phenotypic data derived from patient records to identify patterns and potential
subgroups among these benign variants. Our approach includes a detailed phenotypic
assessment, integrating both genetic and clinical data to enhance the understanding of these
variants and their implications.

Benign variants were mapped to the ABCA4 protein and were shown to be in specific
regions or domains. Computational analysis revealed that many variants had allele frequencies
uncharacteristic of benign phenotypes. Other clinical data proved to be lacking suggesting the
need for more coverage on these types of variants.

The patterns identified in this study show that it is possible to use computational
methods to further analyze ABCA4 variants. This could lead to improved classification of
ABCA4 variants and aid in the development of personalized treatment plans for affected
individuals. By extending our understanding of benign ABCA4 variants, this research contributes
to more accurate genetic counseling and enhanced patient care strategies.