Classification of facial growth pattern at an early age and identification of the genetic basis in two Korean populations

Human development is characterized by distinct developmental processes, particularly during adolescence, and the speed and direction of craniofacial development differs for each person. Therefore, assuming that the speed and direction of development differed between individuals, we calculated the changes in age-dependent facial measurements over time for each individual and for each indicator and have divided these patterns of change into five broad categories. An index was calculated based on the positioning of the landmarks in the facial profile photo, according to a method widely used for current facial analyses. GWAS analysis of facial development patterns was performed by recoding each of the five growth patterns as individual values.

Despite the significant difference from the existing approach such as GWAS analysis of facial measurements or facial deformities, we were able to obtain GWAS results repeatedly associated with facial features. A total of 97 meaningful indicators were identified, including indicators related to craniofacial development in 19 domains. Since probabilistic effects and differences in facial phenotypes need to be confirmed by replication analysis of identified loci, we divided the collection into two groups, and genetic influences in each group were analyzed for stochastic effects by replication analysis and were confirmed by replication indicators. .

In the nose, the width of the nostrils most often increased with age, and the length of the nose showed the greatest tendency to increase along the entire vertical axis. Around the lips, many individuals showed increasing or maintaining patterns, while the patterns associated with the mouth were distributed differently in each population, indicating the degree of difference in growth patterns between individuals.

The length of H1 and H2 show the most decreasing patterns with aging. H5 and H6 have the most increasing patterns with aging. The most notable changes were observed for skin and subcutaneous bone.

For POP1 and POP2, as shown in Table 1, H3 most often exhibits the most increasing patterns, while H4 most often exhibits the most decreasing patterns. Because as they grow, the elasticity of adjacent tissues under the eyes decreases due to growth, resulting in the appearance of sagging eye tails. Moreover, H8, H9 and H10 seemed to be very significant indicators, which seemed to influence each other. H8 is a diagonal length on the left side of the nose, which tends to increase with age. H9 is the width of the nose, which tends to increase with age as the lower lateral cartilage and the skin surrounding the ends of the septum weaken, losing their elasticity.

Among the lengths of the vertical axis, many indicators showed similar increase patterns to those of the horizontal area, and the indicators of the vertical axis of the face seem to influence each other during growth. The vertical lengths of the eyes, V1 and V2, showed the greatest tendency to an increasing pattern. The length of the nose, measured by V5-V8, generally increases until the age of 20. Nose features are well known and include major changes, such as long, drooping tips19. The bony base that supports the nose in youth, a pair of nasal bones, and the ascending process of the maxilla are responsible for many of the soft tissue changes seen in the nose during aging.20.

The model frequencies measured for the EAFG showed that, although we used independent populations, our results were replicated in each population. As shown in Table 1, approximately 70% of facial development patterns were reproduced in each group. The index with the highest frequency was replicated in each group, indicating a common pattern between the populations. Although a few indices showed a different trend, these single indices clustered into broad categories (increase or decrease).

However, no analysis model exists for facial growth, and the classification of facial growth as a clustering pattern of visual expressions is limiting. In this study, we analyzed by applying the –assoc option provided by the PLINK software, and the results of this analysis are based on statistical models called the likelihood ratio test and the Wald test. The reason for applying this analysis is that the phenotype we are targeting is not a general quantitative phenotype, but multinomial variables called facial growth pattern. The currently available method for genome-wide analysis of these variables and several SNPs was the statistical model provided by the PLINK software. Therefore, the significance between the SNP and the phenotype discovered in this study can be understood as a result of analyzing whether the SNP has the explanatory power to explain the phenotype. Some genetic studies based on multinomial variables, and among them we can check an example of application of the same likelihood ratio test as ours21.22.

Moreover, the number of samples cannot be considered representative of all Koreans. However, this study represents the first attempt to classify the facial growth pattern, and when data from two independent groups collected at the same time are analyzed and compared, the common finding (pattern frequency coincides more than 70%) the overrides these limitations.

Most facial changes occur before the age of 18, but facial growth and reshaping has been shown to continue throughout life. The facial skeleton is generally thought to develop continuously throughout life23, which results in the gradual increase of certain facial anthropometric measurements with age, such as anterior nasal cavity and facial width. Some measurements increase significantly with aging, but some measurements are reduced. Chin length becomes shorter as the mandible of the face recedes due to aging, resulting in a shorter overall face length.

Some extrinsic variables such as sex24body mass25 are known to affect facial morphology. The main influence of gender on facial phenotypes has been reported to be the nasal area and upper face, and body mass index (BMI) has been reported as a characteristic of facial width.24. Obesity-related sites such as cheeks and neck were excluded from the measurement. Thus, the effect of the degree of obesity in this study is thought to be relatively small.

GWAS results provide a hypothesis-free approach to identify important genetic variations that underlie differences in craniofacial shape within populations26. A total of 97 significant or suggestive SNPs in 19 gene regions and loci that have previously been associated with facial morphology were identified in this study. For 19 loci showing significant and suggestive phenotypic associations, substantial literature was identified associating these loci with facial development, as shown in Fig. 3. In current work, we have found 10 suggestive SNPs in the horizontal region: FOXK127, IGSF1028, FAM161A29, POU3F230, DYNC11131, SFSWAP32, TRIM2933, RAPGEF134, PCDH735and CXCR436. We also found 9 suggestive SNPs in the vertical region: ZSWIM637, CSN338, ATXN139, COL18A140, TCSPS941, CTNNA342, ASTN243, TUSC344and MTCL145. Genetic annotations from the UCSC database (https://genome.ucsc.edu) was used to predict the functional effects of the variants. Genes reported to affect embryonic development from the UCSC database included CXCR446 in the horizontal region and CSN338 and TUSC344 in the vertical region. Genes reported to affect cranial growth and brain development included ZSWIM6, ATXN138, ASTN243and MTCL145 in the vertical region. Additionally, genes related to molecular mechanisms in the regulation of skeletal muscle and cartilage included FOXK127, RAPGEF134and IGSF1028 in the horizontal region, and COL18A140 and TCSPS941 in the vertical region. Genes associated with retinal circuit components and sensory organ growth have been FAM161A29, POU3F230and SFSWAP32 in the horizontal region. Finally, genes related to frontonasal and dysmorphic facial features were PCDH735, TRIM2933and DYNC11131 in the horizontal region and CTNNA342 in the vertical region.

picture 3

The drawings indicate the facial phenotypes and genes associated with the 5 identified EAFG patterns using significant genome-wide associations.

The authors generated an interesting report using two-dimensional images to perform a GWAS study of face shape focused not on fixed time points, but on ontogenetic growth trajectories. To my knowledge, this is a new analysis. It’s also done in an interesting way, using a mix of photo types. The argument in this article is based on using this for the purpose of being able to improve the accuracy of age progression for missing children, but there is very little discussion of this and it is a very intriguing fundamental scientific question.

Facial growth patterns that occur in childhood remain poorly understood, and estimating facial growth simply by photographic analysis or using existing facial indicators can be difficult. Additionally, an individual’s unique facial morphologies can be difficult to quantify using simple photographic indicator analysis. The characteristics of facial growth must take into account the differences in the innate genetic makeup of each individual. This study is significant because we have classified and characterized facial growth patterns that occur in childhood and will contribute to research on facial growth, facial recognition, and potentially aid in locating missing children in the future. This study can serve as a foundation for understanding facial morphology and can be extended to various research areas exploring facial growth, including forensic science for adults and children.

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