TY - GEN
T1 - Data-driven trait heritability-based extraction of human facial phenotypes
AU - Yuan, Meng
AU - Goovaerts, Seppe
AU - Hoskens, Hanne
AU - Richmond, Stephen
AU - Walsh, Susan
AU - Shriver, Mark D.
AU - Shaffer, John R.
AU - Marazita, Mary L.
AU - Weinberg, Seth M.
AU - Peeters, Hilde
AU - Claes, Peter
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - A genome-wide association study (GWAS) of a complex, multi-dimensional morphological trait, such as the human face, typically relies on predefined and simplified phenotypic measurements, such as inter-landmark distances and angles. These measures are predominantly designed by human experts based on perceived biological or clinical knowledge. To avoid use handcrafted phenotypes (i.e., a priori expert-identified phenotypes), alternative automatically extracted phenotypic descriptors, such as features derived from dimension reduction techniques (e.g., principal component analysis), are employed. While the features generated by such computational algorithms capture the geometric variations of the biological shape, they are not necessarily genetically relevant. Therefore, genetically informed data-driven phenotyping is desirable. Here, we propose an approach where phenotyping is done through a data-driven optimization of trait heritability, defined as the degree of variation in a phenotypic trait in a population that is due to genetic variation. The resulting phenotyping process consists of two steps: 1) constructing a feature space that models shape variations using dimension reduction techniques, and 2) searching for directions in the feature space exhibiting high trait heritability using a genetic search algorithm (i.e., heuristic inspired by natural selection). We show that the phenotypes resulting from the proposed trait heritability-optimized training differ from those of principal components in the following aspects: 1) higher trait heritability, 2) higher SNP heritability, and 3) identification of the same number of independent genetic loci with a smaller number of effective traits. Our results demonstrate that data-driven trait heritability-based optimization enables the automatic extraction of genetically relevant phenotypes, as shown by their increased power in genome-wide association scans.
AB - A genome-wide association study (GWAS) of a complex, multi-dimensional morphological trait, such as the human face, typically relies on predefined and simplified phenotypic measurements, such as inter-landmark distances and angles. These measures are predominantly designed by human experts based on perceived biological or clinical knowledge. To avoid use handcrafted phenotypes (i.e., a priori expert-identified phenotypes), alternative automatically extracted phenotypic descriptors, such as features derived from dimension reduction techniques (e.g., principal component analysis), are employed. While the features generated by such computational algorithms capture the geometric variations of the biological shape, they are not necessarily genetically relevant. Therefore, genetically informed data-driven phenotyping is desirable. Here, we propose an approach where phenotyping is done through a data-driven optimization of trait heritability, defined as the degree of variation in a phenotypic trait in a population that is due to genetic variation. The resulting phenotyping process consists of two steps: 1) constructing a feature space that models shape variations using dimension reduction techniques, and 2) searching for directions in the feature space exhibiting high trait heritability using a genetic search algorithm (i.e., heuristic inspired by natural selection). We show that the phenotypes resulting from the proposed trait heritability-optimized training differ from those of principal components in the following aspects: 1) higher trait heritability, 2) higher SNP heritability, and 3) identification of the same number of independent genetic loci with a smaller number of effective traits. Our results demonstrate that data-driven trait heritability-based optimization enables the automatic extraction of genetically relevant phenotypes, as shown by their increased power in genome-wide association scans.
UR - http://www.scopus.com/inward/record.url?scp=85184926734&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85184926734&partnerID=8YFLogxK
U2 - 10.1109/BIBM58861.2023.10385885
DO - 10.1109/BIBM58861.2023.10385885
M3 - Conference contribution
AN - SCOPUS:85184926734
T3 - Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
SP - 312
EP - 319
BT - Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
A2 - Jiang, Xingpeng
A2 - Wang, Haiying
A2 - Alhajj, Reda
A2 - Hu, Xiaohua
A2 - Engel, Felix
A2 - Mahmud, Mufti
A2 - Pisanti, Nadia
A2 - Cui, Xuefeng
A2 - Song, Hong
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
Y2 - 5 December 2023 through 8 December 2023
ER -