Common Genetic Variants Influence Whorls in Fingerprint Patterns

Ho, Yvonne; Evans, David M.; Montgomery, Grant W.; Henders, Anjali K.; Kemp, John P.; Timpson, Nicholas J.; Pourcain, Beaté St; Heath, Andrew C.; Madden, Pamela A. F.; Loesch, Danuta Z.; McNevin, Dennis; Daniel, Runa; Smith, George Davey; Martin, Nicholas G.; Medland, Sarah E.

doi:10.1016/j.jid.2015.10.062

Cited by 22 publications

(19 citation statements)

References 13 publications

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“…Genetic studies of both fingerprints pattern and facial morphology suggest a strong influence of genetic factors in the determination of these features. Some genes that were reported to express in low to medium levels in the skin were found to play a key role in the determination of facial feature [31,32]. Though the direct information of the influence of the genetic markers on specific fingerprint and facial phenotypes may be a key to revealing the possible relationship that exist between the two features [31,32].…”

Section: Classes Canon and Variationsmentioning

confidence: 99%

“…Some genes that were reported to express in low to medium levels in the skin were found to play a key role in the determination of facial feature [31,32]. Though the direct information of the influence of the genetic markers on specific fingerprint and facial phenotypes may be a key to revealing the possible relationship that exist between the two features [31,32]. It might be suggested that the correlation between the fingerprint and facial variables was based on a characteristic of the fingerprints features (quantitative variables) which may be controlled differently from the fingerprint patterns (qualitative variables) during embryogenesis.…”

Section: Classes Canon and Variationsmentioning

confidence: 99%

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Evaluation of Facial Proportions and Their Association with Thumbprint Patterns among Hausa Ethnic Group

Adamu

Ojo

Danborno

et al. 2017

Journal of Anthropology

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Background. Evolutionary forces such as founder effect resulted in reproductive isolation and reduced genetic diversity may have led to ethnic variation in the facial appearance and other features like fingerprints pattern. Aim. To determine the pattern of facial proportion based on neoclassical facial canon. The associations between facial proportions and thumbprint patterns were also investigated. Subject and Methods. A total of 534 subjects of 18-25 years of age participated. Direct sensing and photographs methods were used to determine fingerprint and facial features, respectively. Fisher's Exact test was used to test for association between variables. Results. It was observed that in both males and females there was no (0%) occurrence of classical canon of facial proportion. There was also no association between sex and facial proportions. A significant association was found in between thumbprint patterns and vertical class III neoclassical facial proportion only when the frequency of both left and right thumbprint patterns was considered a single entity. There is no significant association between the thumbprint patterns of the right and left thumbs with vertical horizontal facial proportions in male and female participants. It was observed that right and left thumbs have more tendency of significance with facial proportion in males and females, respectively. Conclusion. Fingerprint pattern and its associated features may be controlled by a different mechanism such that the two may correlate differently with other features as the case may be with facial features.

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Section: Classes Canon and Variationsmentioning

confidence: 99%

Section: Classes Canon and Variationsmentioning

confidence: 99%

Evaluation of Facial Proportions and Their Association with Thumbprint Patterns among Hausa Ethnic Group

Adamu

Ojo

Danborno

et al. 2017

Journal of Anthropology

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“…Leveraging our cell type specific map of regulatory elements active in the human neocortex ( Fig. 4D and table S7, see materials and methods), (29) we used a partitioned heritability approach based on LD score regression (47) to identify cell types enriched for variants influencing brain volume, cognition, or causing risk for neuropsychiatric disease (48)(49)(50)(51)(52)(53)(54)(55)(56)(57)(58)(59)(60)(61)(62)(63). We found that variants influencing adult intracranial volume were specifically enriched in the regulatory elements of cycling progenitors (PgS, PgG2M), pinpointing a specific cell type and state likely associated with neural progenitor expansion ( Fig.…”

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confidence: 99%

A single cell transcriptomic analysis of human neocortical development

Polioudakis

Torre-Ubieta

Langerman³

et al. 2018

Preprint

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Defining the number, proportion, or lineage of distinct cell types in the developing human brain is an important goal of modern brain research. We defined single cell transcriptomic profiles for 40,000 cells at mid-gestation to identify cell types in the developing human neocortex. We define expression profiles corresponding to all known major cell types at this developmental period and identify multiple transcription factors and co-factors expressed in specific cell types, providing an unprecedented resource for understanding human neocortical development including the first single-cell characterization of human subplate neurons. We characterize major developmental trajectories during early neurogenesis, showing that cell type differentiation occurs on a continuum that involves transitions that tie cell cycle progression with early cell fate decisions. We use these data to deconvolute regulatory networks and map neuropsychiatric disease genes to specific cell types, implicating dysregulation of specific cell types, as the mechanistic underpinnings of several neurodevelopmental disorders. Together these results provide an extensive catalog of cell types in human neocortex and extend our understanding of early cortical development, human brain evolution and the cellular basis of neuropsychiatric disease.One Sentence Summary: Comprehensive single cell transcriptomes in developing human cortex inform models of cell diversity, differentiation and disease risk. Main text:The human cortex is composed of billions of cells estimated to encompass hundreds or thousands of distinct cell types each with unique functions (1, 2). It is reasonable to assume that to understand a system as complex as the human cortex, it is necessary to understand its components (3,4). Ground breaking work in mouse revealed the power of single-cell transcriptomics for providing a framework for understanding the complexity and heterogeneity of cell types in the brain (5-10). The availability of high quality tissue and advances in single-cell transcriptomic technologies now permit us to catalog the cell type diversity of the human cortex in a comprehensive and unbiased manner (11). Despite the enormous progress that has been made in characterizing early cortical development (1,(12)(13)(14)(15)(16), many of the molecular mechanisms underpinning the generation, differentiation, and development of the diverse types of cells remain largely unknown (17). Molecular taxonomies of cortical cell types from developing human brains enable us to understand the mechanisms of neurogenesis and how the remarkable cellular diversity found in the human cortex is achieved (18-22). Developing knowledge of neocortical cell types and their transcriptional programs during this epoch is a key step in understanding mechanistic dysregulation in neurodevelopmental disorders with cell type resolution. Several recent studies have taken a first step in this direction, analyzing several hundred, or a few thousand cells from developing human brain (18-22). However, advances in te...

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“…In previous studies, the features of both fingerprint patterns and facial morphology were reported to be strongly influenced by genetic factors. Some genes that were reported in low to medium levels in the skin were found to play a key role in the determination of facial features [31,32]. Some level of association was also documented between thumbprint patterns and facial proportions [4].…”

Section: Discussionmentioning

confidence: 89%

Prediction of Nasolabial Distance using Thumbprint Ridge Density among the Hausa Population of Nigeria

2017

AJFSFM

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www.nauss.edu.sa http://ajfsfm.nauss.edu.sa Arab Journal of Forensic Sciences & Forensic Medicine ture the facial images. Pearson's correlation analysis was used to measure the linear dependence between thumbprint ridge density and Nasolabial distances. Stepwise linear multiple regression analysis was used to predict nasolabial distances from thumbprint ridge density. SPSS statistical software version 20 was used for the statistical analysis and a p-value of < 0.05 was set as a level of significance. Results showed that in males left proximal ridge count correlates negatively with nasal length, philtrum length, upper lip length and mouth width. The upper lip length and nasal width correlate negatively with left ulnar ridge count and right radial ridge counts, respectively. A positive correlation was observed between ulnar ridge count with nasal length and lower vermilion width. In females, a positive correlation was observed only between lower vermilion width and left proximal ridge counts. Left proximal ridge counts predict most of the nasolabial distances in

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Common Genetic Variants Influence Whorls in Fingerprint Patterns

Cited by 22 publications

References 13 publications

Evaluation of Facial Proportions and Their Association with Thumbprint Patterns among Hausa Ethnic Group

Evaluation of Facial Proportions and Their Association with Thumbprint Patterns among Hausa Ethnic Group

A single cell transcriptomic analysis of human neocortical development

Prediction of Nasolabial Distance using Thumbprint Ridge Density among the Hausa Population of Nigeria

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