Browsing by Author "Marazita, Mary L."
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Item Effects of Male Facial Masculinity on Perceived Attractiveness(Springer Nature, 2021) Ekrami, Omid; Claes, Peter; Shriver, Mark D.; Weinberg, Seth M.; Marazita, Mary L.; Walsh, Susan; Van Dongen, Stefan; Biology, School of ScienceStudies suggest that high levels of masculinity in men can be a signal of 'better genes' as well as low parental investment. It is the trade-off between these two qualities that has led to the hypothesis that women's preferences for male masculinity are condition-dependent, yet, not all studies support this hypothesis. In addition, there is evidence that more average faces would be perceived as more attractive. Here we study the variation in masculinity preferences of a cohort of heterosexual women (n=769), using manipulated 3D faces of male subjects. We used linear mixed models to test for effects of various covariates such as relationship status, use of hormonal contraception, sociosexual orientation and self-perceived attractiveness on preference for masculinity. Our results show that women's sociosexual orientation has a positive correlation with masculinity preference while using hormonal contraception decreases this preference. None of the other covariates displayed any significant effect on masculinity preference. The initial level of masculinity of the faces (very low, low, average, high and very high) was also shown to affect this preference, where we found a significant preference for higher masculinity in the very low and average group, while no preference was found in the other groups. Our findings support the notion that condition-dependent variables have very small effects, if any, on women's preference for masculinity in men.Item Fluctuating Asymmetry and Sexual Dimorphism in Human Facial Morphology: A Multi-Variate Study(MDPI, 2021-02) Ekrami, Omid; Claes, Peter; Van Assche, Ellen; Shriver, Mark D.; Weinberg, Seth M.; Marazita, Mary L.; Walsh, Susan; Van Dongen, Stefan; Biology, School of ScienceBackground: Fluctuating asymmetry is often used as an indicator of developmental instability, and is proposed as a signal of genetic quality. The display of prominent masculine phenotypic features, which are a direct result of high androgen levels, is also believed to be a sign of genetic quality, as these hormones may act as immunosuppressants. Fluctuating asymmetry and masculinity are therefore expected to covary. However, there is lack of strong evidence in the literature regarding this hypothesis. Materials and methods: In this study, we examined a large dataset of high-density 3D facial scans of 1260 adults (630 males and 630 females). We mapped a high-density 3D facial mask onto the facial scans in order to obtain a high number of quasi-landmarks on the faces. Multi-dimensional measures of fluctuating asymmetry were extracted from the landmarks using Principal Component Analysis, and masculinity/femininity scores were obtained for each face using Partial Least Squares. The possible correlation between these two qualities was then examined using Pearson's coefficient and Canonical Correlation Analysis. Results: We found no correlation between fluctuating asymmetry and masculinity in men. However, a weak but significant correlation was found between average fluctuating asymmetry and masculinity in women, in which feminine faces had higher levels of fluctuating asymmetry on average. This correlation could possibly point to genetic quality as an underlying mechanism for both asymmetry and masculinity; however, it might also be driven by other fitness or life history traits, such as fertility. Conclusions: Our results question the idea that fluctuating asymmetry and masculinity should be (more strongly) correlated in men, which is in line with the recent literature. Future studies should possibly focus more on the evolutionary relevance of the observed correlation in women.Item Genome scans of facial features in East Africans and cross-population comparisons reveal novel associations(Public Library of Science, 2021-08-19) Liu, Chenxing; Lee, Myoung Keun; Naqvi, Sahin; Hoskens, Hanne; Liu, Dongjing; White, Julie D.; Indencleef, Karlijne; Matthews, Harold; Eller, Ryan J.; Li, Jiarui; Mohammed, Jaaved; Swigut, Tomek; Richmond, Stephen; Manyama, Mange; Hallgrímsson, Benedikt; Spritz, Richard A.; Feingold, Eleanor; Marazita, Mary L.; Wysocka, Joanna; Walsh, Susan; Shriver, Mark D.; Claes, Peter; Weinberg, Seth M.; Shaffer, John R.; Biology, School of ScienceFacial morphology is highly variable, both within and among human populations, and a sizable portion of this variation is attributable to genetics. Previous genome scans have revealed more than 100 genetic loci associated with different aspects of normal-range facial variation. Most of these loci have been detected in Europeans, with few studies focusing on other ancestral groups. Consequently, the degree to which facial traits share a common genetic basis across diverse sets of humans remains largely unknown. We therefore investigated the genetic basis of facial morphology in an East African cohort. We applied an open-ended data-driven phenotyping approach to a sample of 2,595 3D facial images collected on Tanzanian children. This approach segments the face into hierarchically arranged, multivariate features that capture the shape variation after adjusting for age, sex, height, weight, facial size and population stratification. Genome scans of these multivariate shape phenotypes revealed significant (p < 2.5 × 10-8) signals at 20 loci, which were enriched for active chromatin elements in human cranial neural crest cells and embryonic craniofacial tissue, consistent with an early developmental origin of the facial variation. Two of these associations were in highly conserved regions showing craniofacial-specific enhancer activity during embryological development (5q31.1 and 12q21.31). Six of the 20 loci surpassed a stricter threshold accounting for multiple phenotypes with study-wide significance (p < 6.25 × 10-10). Cross-population comparisons indicated 10 association signals were shared with Europeans (seven sharing the same associated SNP), and facilitated fine-mapping of causal variants at previously reported loci. Taken together, these results may point to both shared and population-specific components to the genetic architecture of facial variation.Item Insights into the genetic architecture of the human face(Springer Nature, 2021) White, Julie D.; Indencleef, Karlijne; Naqvi, Sahin; Eller, Ryan J.; Hoskens, Hanne; Roosenboom, Jasmien; Lee, Myoung Keun; Li, Jiarui; Mohammed, Jaaved; Richmond, Stephen; Quillen, Ellen E.; Norton, Heather L.; Feingold, Eleanor; Swigut, Tomek; Marazita, Mary L.; Peeters, Hilde; Hens, Greet; Shaffer, John R.; Wysocka, Joanna; Walsh, Susan; Weinberg, Seth M.; Shriver, Mark D.; Claes, Peter; Biology, School of ScienceThe human face is complex and multipartite, and characterization of its genetic architecture remains challenging. Using a multivariate genome-wide association study meta-analysis of 8,246 European individuals, we identified 203 genome-wide-significant signals (120 also study-wide significant) associated with normal-range facial variation. Follow-up analyses indicate that the regions surrounding these signals are enriched for enhancer activity in cranial neural crest cells and craniofacial tissues, several regions harbor multiple signals with associations to different facial phenotypes, and there is evidence for potential coordinated actions of variants. In summary, our analyses provide insights into the understanding of how complex morphological traits are shaped by both individual and coordinated genetic actions.Item A Multivariate Approach to Determine the Dimensionality of Human Facial Asymmetry(MDPI, 2020-03) Ekrami, Omid; Claes, Peter; White, Julie D.; Weinberg, Seth M.; Marazita, Mary L.; Walsh, Susan; Shriver, Mark D.; Van Dongen, Stefan; Biology, School of ScienceMany studies have suggested that developmental instability (DI) could lead to asymmetric development, otherwise known as fluctuating asymmetry (FA). Several attempts to unravel the biological meaning of FA have been made, yet the main step in estimating FA is to remove the effects of directional asymmetry (DA), which is defined as the average bilateral asymmetry at the population level. Here, we demonstrate in a multivariate context that the conventional method of DA correction does not adequately compensate for the effects of DA in other dimensions of asymmetry. This appears to be due to the presence of between-individual variation along the DA dimension. Consequently, we propose to decompose asymmetry into its different orthogonal dimensions, where we introduce a new measure of asymmetry, namely fluctuating directional asymmetry (F-DA). This measure describes individual variation in the dimension of DA, and can be used to adequately correct the asymmetry measurements for the presence of DA. We provide evidence that this measure can be useful in disentangling the different dimensions of asymmetry, and further studies on this measure can provide valuable insight into the underlying biological processes leading to these different asymmetry dimensions.Item netMUG: a novel network-guided multi-view clustering workflow for dissecting genetic and facial heterogeneity(bioRxiv, 2023-05-05) Li, Zuqi; Melograna, Federico; Hoskens, Hanne; Duroux, Diane; Marazita, Mary L.; Walsh, Susan; Weinberg, Seth M.; Shriver, Mark D.; Müller-Myhsok, Bertram; Claes, Peter; Van Steen, Kristel; Biology, School of ScienceMulti-view data offer advantages over single-view data for characterizing individuals, which is crucial in precision medicine toward personalized prevention, diagnosis, or treatment follow-up. Here, we develop a network-guided multi-view clustering framework named netMUG to identify actionable subgroups of individuals. This pipeline first adopts sparse multiple canonical correlation analysis to select multi-view features possibly informed by extraneous data, which are then used to construct individual-specific networks (ISNs). Finally, the individual subtypes are automatically derived by hierarchical clustering on these network representations. We applied netMUG to a dataset containing genomic data and facial images to obtain BMI-informed multi-view strata and showed how it could be used for a refined obesity characterization. Benchmark analysis of netMUG on synthetic data with known strata of individuals indicated its superior performance compared with both baseline and benchmark methods for multi-view clustering. In addition, the real-data analysis revealed subgroups strongly linked to BMI and genetic and facial determinants of these classes. NetMUG provides a powerful strategy, exploiting individual-specific networks to identify meaningful and actionable strata. Moreover, the implementation is easy to generalize to accommodate heterogeneous data sources or highlight data structures.Item Optimized phenotyping of complex morphological traits: enhancing discovery of common and rare genetic variants(Oxford University Press, 2025) Yuan, Meng; Goovaerts, Seppe; Lee, Myoung K.; Devine, Jay; Richmond, Stephen; Walsh, Susan; Shriver, Mark D.; Shaffer, John R.; Marazita, Mary L.; Peeters, Hilde; Weinberg, Seth M.; Claes, Peter; Biology, School of ScienceGenotype-phenotype (G-P) analyses for complex morphological traits typically utilize simple, predetermined anatomical measures or features derived via unsupervised dimension reduction techniques (e.g. principal component analysis (PCA) or eigen-shapes). Despite the popularity of these approaches, they do not necessarily reveal axes of phenotypic variation that are genetically relevant. Therefore, we introduce a framework to optimize phenotyping for G-P analyses, such as genome-wide association studies (GWAS) of common variants or rare variant association studies (RVAS) of rare variants. Our strategy is two-fold: (i) we construct a multidimensional feature space spanning a wide range of phenotypic variation, and (ii) within this feature space, we use an optimization algorithm to search for directions or feature combinations that are genetically enriched. To test our approach, we examine human facial shape in the context of GWAS and RVAS. In GWAS, we optimize for phenotypes exhibiting high heritability, estimated from either family data or genomic relatedness measured in unrelated individuals. In RVAS, we optimize for the skewness of phenotype distributions, aiming to detect commingled distributions that suggest single or few genomic loci with major effects. We compare our approach with eigen-shapes as baseline in GWAS involving 8246 individuals of European ancestry and in gene-based tests of rare variants with a subset of 1906 individuals. After applying linkage disequilibrium score regression to our GWAS results, heritability-enriched phenotypes yielded the highest SNP heritability, followed by eigen-shapes, while commingling-based traits displayed the lowest SNP heritability. Heritability-enriched phenotypes also exhibited higher discovery rates, identifying the same number of independent genomic loci as eigen-shapes with a smaller effective number of traits. For RVAS, commingling-based traits resulted in more genes passing the exome-wide significance threshold than eigen-shapes, while heritability-enriched phenotypes lead to only a few associations. Overall, our results demonstrate that optimized phenotyping allows for the extraction of genetically relevant traits that can specifically enhance discovery efforts of common and rare variants, as evidenced by their increased power in facial GWAS and RVAS.Item Syndrome-informed phenotyping identifies a polygenic background for achondroplasia-like facial variation in the general population(Springer Nature, 2024-12-02) Vanneste, Michiel; Hoskens, Hanne; Goovaerts, Seppe; Matthews, Harold; Devine, Jay; Aponte, Jose D.; Cole, Joanne; Shriver, Mark; Marazita, Mary L.; Weinberg, Seth M.; Walsh, Susan; Richmond, Stephen; Klein, Ophir D.; Spritz, Richard A.; Peeters, Hilde; Hallgrímsson, Benedikt; Claes, Peter; Biology, School of ScienceHuman craniofacial shape is highly variable yet highly heritable with numerous genetic variants interacting through multiple layers of development. Here, we hypothesize that Mendelian phenotypes represent the extremes of a phenotypic spectrum and, using achondroplasia as an example, we introduce a syndrome-informed phenotyping approach to identify genomic loci associated with achondroplasia-like facial variation in the general population. We compare three-dimensional facial scans from 43 individuals with achondroplasia and 8246 controls to calculate achondroplasia-like facial scores. Multivariate GWAS of the control scores reveals a polygenic basis for facial variation along an achondroplasia-specific shape axis, identifying genes primarily involved in skeletal development. Jointly modeling these genes in two independent control samples, both human and mouse, shows craniofacial effects approximating the characteristic achondroplasia phenotype. These findings suggest that both complex and Mendelian genetic variation act on the same developmentally determined axes of facial variation, providing insights into the genetic intersection of complex traits and Mendelian disorders.Item The Human Phenotype Ontology in 2024: phenotypes around the world(Oxford University Press, 2024) Gargano, Michael A.; Matentzoglu, Nicolas; Coleman, Ben; Addo-Lartey, Eunice B.; Anagnostopoulos, Anna V.; Anderton, Joel; Avillach, Paul; Bagley, Anita M.; Bakštein, Eduard; Balhoff, James P.; Baynam, Gareth; Bello, Susan M.; Berk, Michael; Bertram, Holli; Bishop, Somer; Blau, Hannah; Bodenstein, David F.; Botas, Pablo; Boztug, Kaan; Čady, Jolana; Callahan, Tiffany J.; Cameron, Rhiannon; Carbon, Seth J.; Castellanos, Francisco; Caufield, J. Harry; Chan, Lauren E.; Chute, Christopher G.; Cruz-Rojo, Jaime; Dahan-Oliel, Noémi; Davids, Jon R.; de Dieuleveult, Maud; de Souza, Vinicius; de Vries, Bert B. A.; de Vries, Esther; DePaulo, J. Raymond; Derfalvi, Beata; Dhombres, Ferdinand; Diaz-Byrd, Claudia; Dingemans, Alexander J. M.; Donadille, Bruno; Duyzend, Michael; Elfeky, Reem; Essaid, Shahim; Fabrizzi, Carolina; Fico, Giovanna; Firth, Helen V.; Freudenberg-Hua, Yun; Fullerton, Janice M.; Gabriel, Davera L.; Gilmour, Kimberly; Giordano, Jessica; Goes, Fernando S.; Gore Moses, Rachel; Green, Ian; Griese, Matthias; Groza, Tudor; Gu, Weihong; Guthrie, Julia; Gyori, Benjamin; Hamosh, Ada; Hanauer, Marc; Hanušová, Kateřina; He, Yongqun Oliver; Hegde, Harshad; Helbig, Ingo; Holasová, Kateřina; Hoyt, Charles Tapley; Huang, Shangzhi; Hurwitz, Eric; Jacobsen, Julius O. B.; Jiang, Xiaofeng; Joseph, Lisa; Keramatian, Kamyar; King, Bryan; Knoflach, Katrin; Koolen, David A.; Kraus, Megan L.; Kroll, Carlo; Kusters, Maaike; Ladewig, Markus S.; Lagorce, David; Lai, Meng-Chuan; Lapunzina, Pablo; Laraway, Bryan; Lewis-Smith, David; Li, Xiarong; Lucano, Caterina; Majd, Marzieh; Marazita, Mary L.; Martinez-Glez, Victor; McHenry, Toby H.; McInnis, Melvin G.; McMurry, Julie A.; Mihulová, Michaela; Millett, Caitlin E.; Mitchell, Philip B.; Moslerová, Veronika; Narutomi, Kenji; Nematollahi, Shahrzad; Nevado, Julian; Nierenberg, Andrew A.; Novák Čajbiková, Nikola; Nurnberger, John I., Jr.; Ogishima, Soichi; Olson, Daniel; Ortiz, Abigail; Pachajoa, Harry; Perez de Nanclares, Guiomar; Peters, Amy; Putman, Tim; Rapp, Christina K.; Rath, Ana; Reese, Justin; Rekerle, Lauren; Roberts, Angharad M.; Roy, Suzy; Sanders, Stephan J.; Schuetz, Catharina; Schulte, Eva C.; Schulze, Thomas G.; Schwarz, Martin; Scott, Katie; Seelow, Dominik; Seitz, Berthold; Shen, Yiping; Similuk, Morgan N.; Simon, Eric S.; Singh, Balwinder; Smedley, Damian; Smith, Cynthia L.; Smolinsky, Jake T.; Sperry, Sarah; Stafford, Elizabeth; Stefancsik, Ray; Steinhaus, Robin; Strawbridge, Rebecca; Sundaramurthi, Jagadish Chandrabose; Talapova, Polina; Tenorio Castano, Jair A.; Tesner, Pavel; Thomas, Rhys H.; Thurm, Audrey; Turnovec, Marek; van Gijn, Marielle E.; Vasilevsky, Nicole A.; Vlčková, Markéta; Walden, Anita; Wang, Kai; Wapner, Ron; Ware, James S.; Wiafe, Addo A.; Wiafe, Samuel A.; Wiggins, Lisa D.; Williams, Andrew E.; Wu, Chen; Wyrwoll, Margot J.; Xiong, Hui; Yalin, Nefize; Yamamoto, Yasunori; Yatham, Lakshmi N.; Yocum, Anastasia K.; Young, Allan H.; Yüksel, Zafer; Zandi, Peter P.; Zankl, Andreas; Zarante, Ignacio; Zvolský, Miroslav; Toro, Sabrina; Carmody, Leigh C.; Harris, Nomi L.; Munoz-Torres, Monica C.; Danis, Daniel; Mungall, Christopher J.; Köhler, Sebastian; Haendel, Melissa A.; Robinson, Peter N.; Psychiatry, School of MedicineThe Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs.Item The Intersection of the Genetic Architectures of Orofacial Clefts and Normal Facial Variation(Frontiers Media, 2021-02-22) Indencleef, Karlijne; Hoskens, Hanne; Lee, Myoung Keun; White, Julie D.; Liu, Chenxing; Eller, Ryan J.; Naqvi, Sahin; Wehby, George L.; Moreno Uribe, Lina M.; Hecht, Jacqueline T.; Long, Ross E., Jr.; Christensen, Kaare; Deleyiannis, Frederic W.; Walsh, Susan; Shriver, Mark D.; Richmond, Stephen; Wysocka, Joanna; Peeters, Hilde; Shaffer, John R.; Marazita, Mary L.; Hens, Greet; Weinberg, Seth M.; Claes, Peter; Biology, School of ScienceUnaffected relatives of individuals with non-syndromic cleft lip with or without cleft palate (NSCL/P) show distinctive facial features. The presence of this facial endophenotype is potentially an expression of underlying genetic susceptibility to NSCL/P in the larger unselected population. To explore this hypothesis, we first partitioned the face into 63 partially overlapping regions representing global-to-local facial morphology and then defined endophenotypic traits by contrasting the 3D facial images from 264 unaffected parents of individuals with NSCL/P versus 3,171 controls. We observed distinct facial features between parents and controls across 59 global-to-local facial segments at nominal significance (p ≤ 0.05) and 52 segments at Bonferroni corrected significance (p < 1.2 × 10–3), respectively. Next, we quantified these distinct facial features as univariate traits in another dataset of 8,246 unaffected European individuals and performed a genome-wide association study. We identified 29 independent genetic loci that were associated (p < 5 × 10–8) with at least one of the tested endophenotypic traits, and nine genetic loci also passed the study-wide threshold (p < 8.47 × 10–10). Of the 29 loci, 22 were in proximity of loci previously associated with normal facial variation, 18 were near genes that show strong evidence in orofacial clefting (OFC), and another 10 showed some evidence in OFC. Additionally, polygenic risk scores for NSCL/P showed associations with the endophenotypic traits. This study thus supports the hypothesis of a shared genetic architecture of normal facial development and OFC.