Search

Aging is a major risk factor for late-onset Alzheimer’s disease (LOAD). How aging contributes to the development of LOAD remains elusive. In this study, we examined multiple large-scale transcriptomic data from both normal aging and LOAD brains to understand the molecular interconnection between aging and LOAD. We found that shared gene expression changes between aging and LOAD are mostly seen in the hippocampal and several cortical regions. In the hippocampus, the expression of phosphoprotein, alternative splicing and cytoskeleton genes are commonly changed in both aging and AD, while synapse, ion transport, and synaptic vesicle genes are commonly down-regulated. Aging-specific changes are associated with acetylation and methylation, while LOAD-specific changes are more related to glycoprotein (both up- and down-regulations), inflammatory response (up-regulation), myelin sheath and lipoprotein (down-regulation). We also found that normal aging brain transcriptomes from relatively young donors (45-70 years old) clustered into several subgroups and some subgroups showed gene expression changes highly similar to those seen in LOAD brains. Using brain transcriptomic data from another cohort of older individuals (> 70 years), we found that samples from cognitively normal older individuals clustered with the “healthy aging” subgroup while AD samples mainly clustered with the “AD similar” subgroups. This may imply that individuals in the healthy aging subgroup will likely remain cognitively normal when they become older and vice versa. In summary, our results suggest that on the transcriptome level, aging and LOAD have strong interconnections in some brain regions in a subpopulation of cognitively normal aging individuals. This supports the theory that the initiation of LOAD occurs decades earlier than the manifestation of clinical phenotype and it may be essential to closely study the “normal brain aging” to identify the very early molecular events that may lead to LOAD development.

A key goal of geroscience research is to identify effective interventions to extend human healthspan, the years of healthy life. Currently, majority of the geroprotectors are found by screening compounds in model organisms; whether they will be effective in humans is largely unknown. Here we present a new strategy called ANDRU (aging network based drug discovery) to help the discovery of human geroprotectors. It first identifies human aging subnetworks that putatively function at the interface between aging and age-related diseases; it then screens for pharmacological interventions that may “reverse” the age-associated transcriptional changes occurred in these subnetworks. We applied ANDRU to human adipose gene expression data from the Genotype Tissue Expression (GTEx) project. For the top 31 identified compounds, 19 of them showed at least some evidence supporting their function in improving metabolic traits or lifespan, which include type 2 diabetes drugs such as pioglitazone. As the query aging genes were refined to the ones with more intimate links to diseases, ANDRU identified more meaningful drug hits than the general approach without considering the underlying network structures. In summary, ANDRU represents a promising human data-driven strategy that may speed up the discovery of interventions to extend human healthspan. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s11357-019-00106-x) contains supplementary material, which is available to authorized users.

The duration of pregnancy is influenced by fetal and maternal genetic and non-genetic factors. Here we report a fetal genome-wide association meta-analysis of gestational duration, and early preterm, preterm, and postterm birth in 84,689 infants. One locus on chromosome 2q13 is associated with gestational duration; the association is replicated in 9,291 additional infants (combined P = 3.96 × 10−14). Analysis of 15,588 mother-child pairs shows that the association is driven by fetal rather than maternal genotype. Functional experiments show that the lead SNP, rs7594852, alters the binding of the HIC1 transcriptional repressor. Genes at the locus include several interleukin 1 family members with roles in pro-inflammatory pathways that are central to the process of parturition. Further understanding of the underlying mechanisms will be of great public health importance, since giving birth either before or after the window of term gestation is associated with increased morbidity and mortality., Gestational duration depends on both maternal and fetal genetic influences. Here, the authors perform a fetal genome-wide association meta-analysis and find that a locus on 2q13 is associated with pregnancy duration and further show that the lead SNP rs7594852 changes the binding properties of transcriptional repressor HIC1.

A large number of genetic variants associated with human longevity have been reported but how they play their functions remains elusive. We performed an integrative analysis on 113 genome-wide significant longevity and 14,529 age-related disease variants in the context of putative gene expression regulation. We found that most of the longevity allele types were different from the genotype of disease alleles when they were localized at the same chromosomal positions. Longevity variants were about eight times more likely to be associated with gene expression than randomly selected variants. The directions of the gene expression association were more likely to be opposite between longevity and disease variants when the association occurred to the same gene. Many longevity variants likely function through down-regulating inflammatory response and up-regulating healthy lipid metabolisms. In conclusion, this work helps to elucidate the potential mechanisms of longevity variants for follow-up studies to discover methods to extend human healthspan.

BACKGROUND: A growing body of evidence links maternal exposure to particulate matter < 2.5 μM in diameter (PM(2.5)) and deviations in fetal growth. Several studies suggest that the placenta plays a critical role in conveying the effects of maternal PM(2.5) exposure to the developing fetus. These include observed associations between air pollutants and candidate placental features, such as mitochondrial DNA content, DNA methylation and telomere length. However, gaps remain in delineating the pathways linking the placenta to air pollution-related health effects, including a comprehensive profiling of placental processes impacted by maternal PM(2.5) exposure. In this study, we examined alterations in a placental transcriptome-wide network in relation to maternal PM(2.5) exposure prior to and during pregnancy and infant birthweight. METHODS: We evaluated PM(2.5) exposure and placental RNA-sequencing data among study participants enrolled in the Rhode Island Child Health Study (RICHS). Daily residential PM(2.5) levels were estimated using a hybrid model incorporating land-use regression and satellite remote sensing data. Distributed lag models were implemented to assess the impact on infant birthweight due to PM(2.5) weekly averages ranging from 12 weeks prior to gestation until birth. Correlations were assessed between PM(2.5) levels averaged across the identified window of susceptibility and a placental transcriptome-wide gene coexpression network previously generated using the WGCNA R package. RESULTS: We identified a sensitive window spanning 12 weeks prior to and 13 weeks into gestation during which maternal PM(2.5) exposure is significantly associated with reduced infant birthweight. Two placental coexpression modules enriched for genes involved in amino acid transport and cellular respiration were correlated with infant birthweight as well as maternal PM(2.5) exposure levels averaged across the identified growth restriction window. CONCLUSION: Our findings suggest that maternal PM(2.5) exposure may alter placental programming of fetal growth, with potential implications for downstream health effects, including susceptibility to cardiometabolic health outcomes and viral infections.

Heavy metal exposures, such as cadmium, can have negative effects on infant birth weight (BW)—among other developmental outcomes—with placental dysfunction potentially playing a role in these effects. In this study, we examined how differential placental expression of long non-coding RNAs (lncRNAs) may be associated with cadmium levels in placenta and whether differences in the expression of those lncRNAs were associated with fetal growth. In the Rhode Island Child Health Study, we used data from Illumina HiSeq whole transcriptome RNA sequencing (n = 199) to examine association between lncRNA expression and measures of infant BW as well as placental cadmium concentrations controlled for appropriate covariates. Of the 1191 lncRNAs sequenced, 46 demonstrated associations (q

A key goal of aging research was to understand mechanisms underlying healthy aging and develop methods to promote the human healthspan. One approach is to identify gene regulations unique to healthy aging compared with aging in the general population (i.e., “common” aging). Here, we leveraged Genotype‐Tissue Expression (GTEx) project data to investigate “healthy” and “common” aging gene expression regulations at a tissue level in humans and their interconnection with diseases. Using GTEx donors' disease annotations, we defined a “healthy” aging cohort for each tissue. We then compared the age‐associated genes derived from this cohort with age‐associated genes from the “common” aging cohort which included all GTEx donors; we also compared the “healthy” and “common” aging gene expressions with various disease‐associated gene expressions to elucidate the relationships among “healthy,” “common” aging and disease. Our analyses showed that 1. GTEx “healthy” and “common” aging shared a large number of gene regulations; 2. Despite the substantial commonality, “healthy” and “common” aging genes also showed distinct function enrichment, and “common” aging genes had a higher enrichment for disease genes; 3. Disease‐associated gene regulations were overall different from aging gene regulations. However, for genes regulated by both, their regulation directions were largely consistent, implying some aging processes could increase the susceptibility to disease development; and 4. Possible protective mechanisms were associated with some “healthy” aging gene regulations. In summary, our work highlights several unique features of GTEx “healthy” aging program. This new knowledge could potentially be used to develop interventions to promote the human healthspan., A key goal of aging research was to study mechanisms underlying healthy aging and based on them to identify effective methods to promote human healthspan. Here, we leveraged transcriptome data from GTEx to investigate gene expression profiles of both “healthy” and “common” aging in humans and its connection with diseases. Our results highlighted unique features of “healthy” aging and elucidated an intimate intertwined relationship among “healthy” aging, “common” aging, and age‐related diseases.

Background: Intrauterine metal exposures and aberrations in placental processes are known contributors to being born small for gestational age (SGA). However, studies to date have largely focused on independent effects, failing to account for potential interdependence among these markers. Objectives: We evaluated the inter-relationship between multi-metal indices and placental gene network modules related to SGA status to highlight potential molecular pathways through which in utero multi-metal exposure impacts fetal growth. Methods: Weighted quantile sum (WQS) regression was performed using a panel of 16 trace metals measured in post-partum maternal toe nails collected from the Rhode Island Child Health Study (RICHS, n = 195), and confirmation of the derived SGA-related multi-metal index was conducted using Bayesian kernel machine regression (BKMR). We leveraged existing placental weighted gene coexpression network data to examine associations between the SGA multi-metal index and placental gene expression. Expression of select genes were assessed using RT-PCR in an independent birth cohort, the New Hampshire Birth Cohort Study (NHBCS, n = 237). Results: We identified a multi-metal index, predominated by arsenic (As) and cadmium (Cd), that was positively associated with SGA status (Odds ratio = 2.73 [1.04, 7.18]). This index was also associated with the expression of placental gene modules involved in “gene expression” (β = −0.02 [−0.04, −0.01]) and “metabolic hormone secretion” (β = 0.02 [0.00, 0.05]). We validated the association between cadmium exposure and the expression of GRHL1 and INHBA, genes in the “metabolic hormone secretion” module, in NHBCS. Conclusion: We present a novel approach that integrates the application of advanced bioinformatics and biostatistics methods to delineate potential placental pathways through which trace metal exposures impact fetal growth. Keywords: Multi-metal exposure, Placenta, Gene coexpression network, Birth weight

Background A growing body of evidence links maternal exposure to particulate matter Methods We evaluated PM2.5 exposure and placental RNA-sequencing data among study participants enrolled in the Rhode Island Child Health Study (RICHS). Daily residential PM2.5 levels were estimated using a hybrid model incorporating land-use regression and satellite remote sensing data. Distributed lag models were implemented to assess the impact on infant birthweight due to PM2.5 weekly averages ranging from 12 weeks prior to gestation until birth. Correlations were assessed between PM2.5 levels averaged across the identified window of susceptibility and a placental transcriptome-wide gene coexpression network previously generated using the WGCNA R package. Results We identified a sensitive window spanning 12 weeks prior to and 13 weeks into gestation during which maternal PM2.5 exposure is significantly associated with reduced infant birthweight. Two placental coexpression modules enriched for genes involved in amino acid transport and cellular respiration were correlated with infant birthweight as well as maternal PM2.5 exposure levels averaged across the identified growth restriction window. Conclusion Our findings suggest that maternal PM2.5 exposure may alter placental programming of fetal growth, with potential implications for downstream health effects, including susceptibility to cardiometabolic health outcomes and viral infections.

Epidemiologic studies support that at least part of the risk of chronic diseases in childhood and even adulthood may have an in utero origin, and the placenta is a key organ that plays a pivotal role in fetal growth and development. The transcriptomes of 159 human placenta tissues were profiled by genome-wide RNA sequencing (Illumina High-Seq 2500), and linked to fetal genotypes assessed by a high density single nucleotide polymorphism (SNP) genotyping array (Illumina MegaEx). Expression quantitative trait loci (eQTLs) across all annotated transcripts were mapped and examined for enrichment for disease susceptibility loci annotated in the genome-wide association studies (GWAS) catalog. We discovered 3218 cis- and 35 trans-eQTLs at ≤10% false discovery rate in human placentas. Among the 16 439 known disease loci of genome-wide significance, 835 were placental eSNPs (enrichment fold = 1.68, P = 7.41e-42). Stronger effect sizes were observed between GWAS SNPs and gene expression in placentas than what has been reported in other tissues, such as the correlation between asthma risk allele, rs7216389-T and Gasdermin-B (GSDMB) in placenta (r2=27%) versus lung (r2=6%). Finally, our results suggest the placental eQTLs may mediate the function of GWAS loci on postnatal disease susceptibility. Results suggest that transcripts in placenta are under tight genetic control, and that placental gene networks may influence postnatal risk of multiple human diseases lending support for the Developmental Origins of Health and Disease.

Preterm birth (PTB) contributes significantly to infant mortality and morbidity with lifelong impact. Few robust genetic factors of PTB have been identified. Such ‘missing heritability' may be partly due to gene × environment interactions (G × E), which is largely unexplored. Here we conduct genome-wide G × E analyses of PTB in 1,733 African-American women (698 mothers of PTB; 1,035 of term birth) from the Boston Birth Cohort. We show that maternal COL24A1 variants have a significant genome-wide interaction with maternal pre-pregnancy overweight/obesity on PTB risk, with rs11161721 (PG × E=1.8 × 10−8; empirical PG × E=1.2 × 10−8) as the top hit. This interaction is replicated in African-American mothers (PG × E=0.01) from an independent cohort and in meta-analysis (PG × E=3.6 × 10−9), but is not replicated in Caucasians. In adipose tissue, rs11161721 is significantly associated with altered COL24A1 expression. Our findings may provide new insight into the aetiology of PTB and improve our ability to predict and prevent PTB., Preterm birth (PTB) has high prevalence and PTB infants have greater risk for mortality. Here, Hong and colleagues perform a genome-wide gene × environment interaction analysis and find that maternal COL24A1 variants have a significant interaction with maternal pre-pregnancy obesity in increasing PTB risk.

Birth weight (BW) variation is influenced by fetal and maternal genetic and non-genetic factors, and has been reproducibly associated with future cardio-metabolic health outcomes. These associations have been proposed to reflect the lifelong consequences of an adverse intrauterine environment. In earlier work, we demonstrated that much of the negative correlation between BW and adult cardio-metabolic traits could instead be attributable to shared genetic effects. However, that work and other previous studies did not systematically distinguish the direct effects of an individual’s own genotype on BW and subsequent disease risk from indirect effects of their mother’s correlated genotype, mediated by the intrauterine environment. Here, we describe expanded genome-wide association analyses of own BW (n=321,223) and offspring BW (n=230,069 mothers), which identified 278 independent association signals influencing BW (214 novel). We used structural equation modelling to decompose the contributions of direct fetal and indirect maternal genetic influences on BW, implicating fetal- and maternal-specific mechanisms. We used Mendelian randomization to explore the causal relationships between factors influencing BW through fetal or maternal routes, for example, glycemic traits and blood pressure. Direct fetal genotype effects dominate the shared genetic contribution to the association between lower BW and higher type 2 diabetes risk, whereas the relationship between lower BW and higher later blood pressure (BP) is driven by a combination of indirect maternal and direct fetal genetic effects: indirect effects of maternal BP-raising genotypes act to reduce offspring BW, but only direct fetal genotype effects (once inherited) increase the offspring’s later BP. Instrumental variable analysis using maternal BW-lowering genotypes to proxy for an adverse intrauterine environment provided no evidence that it causally raises offspring BP. In successfully separating fetal from maternal genetic effects, this work represents an important advance in genetic studies of perinatal outcomes, and shows that the association between lower BW and higher adult BP is attributable to genetic effects, and not to intrauterine programming.

The duration of pregnancy is influenced by fetal and maternal genetic and non-genetic factors. We conducted a fetal genome-wide association meta-analysis of gestational duration, and early preterm, preterm, and postterm birth in 84,689 infants. One locus on chromosome 2q13 was associated with gestational duration; the association was replicated in 9,291 additional infants (combined P = 3.96 × 10−14). Analysis of 15,536 mother-child pairs showed that the association was driven by fetal rather than maternal genotype. Functional experiments showed that the lead SNP, rs7594852, alters the binding of the HIC1 transcriptional repressor. Genes at the locus include several interleukin 1 family members with roles in pro-inflammatory pathways that are central to the process of parturition. Further understanding of the underlying mechanisms will be of great public health importance, since giving birth either before or after the window of term gestation is associated with increased morbidity and mortality.

The associations between diseases/traits and copy number variants (CNVs) have not been systematically investigated in genome-wide association studies (GWASs), primarily due to a lack of robust and accurate tools for CNV genotyping. Herein, we propose a novel ensemble learning framework, ensembleCNV, to detect and genotype CNVs using single nucleotide polymorphism (SNP) array data. EnsembleCNV a) identifies and eliminates batch effects at raw data level; b) assembles individual CNV calls into CNV regions (CNVRs) from multiple existing callers with complementary strengths by a heuristic algorithm; c) re-genotypes each CNVR with local likelihood model adjusted by global information across multiple CNVRs; d) refines CNVR boundaries by local correlation structure in copy number intensities; e) provides direct CNV genotyping accompanied with confidence score, directly accessible for downstream quality control and association analysis. Benchmarked on two large datasets, ensembleCNV outperformed competing methods and achieved a high call rate (93.3%) and reproducibility (98.6%), while concurrently achieving high sensitivity by capturing 85% of common CNVs documented in the 1000 Genomes Project. Given this CNV call rate and accuracy, which are comparable to SNP genotyping, we suggest ensembleCNV holds significant promise for performing genome-wide CNV association studies and investigating how CNVs predispose to human diseases.

Smoking is a major cause of respiratory conditions. To date, the genetic pleiotropy between smoking behavior and lung function/chronic obstructive pulmonary disease (COPD) have not been systematically explored. We leverage large data sets of smoking behavior, lung function and COPD, and addressed two questions, (1) whether the genetic predisposition of nicotine dependence influence COPD risk and lung function; and (2) the genetic pleiotropy follow causal or independent model. We found the genetic predisposition of nicotine dependence was associated with COPD risk, even after adjusting for smoking behavior, indicating genetic pleiotropy and independent model. Two known nicotine dependent loci (15q25.1 and 19q13.2) were associated with smoking adjusted lung function, and 15q25.1 reached genome-wide significance. At various suggestive p-value thresholds, the smoking adjusted lung function traits share association signals with cigarettes per day and former smoking, substantially greater than random chance. Empirical data showed the genetic pleiotropy between nicotine dependence and COPD or lung function. The basis of pleiotropic effect is rather complex, attributable to a large number of genetic variants, and many variants functions through independent model, where the pleiotropic variants directly affect lung function, not mediated by influencing subjects’ smoking behavior.

// Jialiang Yang 1 , Jing Qiu 2 , Kejing Wang 2 , Lijuan Zhu 2 , Jingjing Fan 2 , Deyin Zheng 3 , Xiaodi Meng 4 , Jiasheng Yang 5 , Lihong Peng 1 , Yu Fu 2 , Dahan Zhang 6 , Shouneng Peng 7 , Haiyun Huang 2 and Yi Zhang 2 1 College of Information Engineering, Changsha Medical University, Changsha 410219, P. R. China 2 Department of Mathematics/Network Engineering/Bioscience and Bioengineering/Library, Hebei University of Science and Technology, Shijiazhuang 050018, P. R. China 3 Department of Mathematics, Hangzhou Normal University, Hangzhou 311121, P. R. China 4 Department of Food Science, Fujian Agriculture and Forestry University, Fuzhou 35002, P. R. China 5 Department of Civil and Environmental Engineering, National University of Singapore, Singapore 117576, Singapore 6 Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, P. R. China 7 Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA Correspondence to: Yi Zhang, email: zhaqi1972@163.com Haiyun Huang, email: jialiang.yang@mssm.edu Keywords: bioinformatics, human obesity, obesity-related diseases, protein interaction network, gene expression Received: May 03, 2017 Accepted: June 05, 2017 Published: July 22, 2017 ABSTRACT Obesity is a primary risk factor for many diseases such as certain cancers. In this study, we have developed three algorithms including a random-walk based method OBNet, a shortest-path based method OBsp and a direct-overlap method OBoverlap, to reveal obesity-disease connections at protein-interaction subnetworks corresponding to thousands of biological functions and pathways. Through literature mining, we also curated an obesity-associated disease list, by which we compared the methods. As a result, OBNet outperforms other two methods. OBNet can predict whether a disease is obesity-related based on its associated genes. Meanwhile, OBNet identifies extensive connections between obesity genes and genes associated with a few diseases at various functional modules and pathways. Using breast cancer and Type 2 diabetes as two examples, OBNet identifies meaningful genes that may play key roles in connecting obesity and the two diseases. For example, TGFB1 and VEGFA are inferred to be the top two key genes mediating obesity-breast cancer connection in modules associated with brain development. Finally, the top modules identified by OBNet in breast cancer significantly overlap with modules identified from TCGA breast cancer gene expression study, revealing the power of OBNet in identifying biological processes involved in the disease.

Preterm birth (PTB) affects one in six Black babies in the United States. Epigenetics is believed to play a role in PTB; however, only a limited number of epigenetic studies of PTB have been reported, most of which have focused on cord blood DNA methylation (DNAm) and/or were conducted in white populations. Here we conducted, by far, the largest epigenome-wide DNAm analysis in 300 Black women who delivered early spontaneous preterm (sPTB, n = 150) or full-term babies (n = 150) and replicated the findings in an independent set of Black mother-newborn pairs from the Boston Birth Cohort. DNAm in maternal blood and/or cord blood was measured using the Illumina HumanMethylation450 BeadChip. We identified 45 DNAm loci in maternal blood associated with early sPTB, with a false discovery rate (FDR) CYTIP and LINC00114 genes, respectively. Both loci had comparable associations with early sPTB and early medically-indicated PTB, but attenuated associations with late sPTB. These associations could not be explained by cell composition, gestational complications, and/or nearby maternal genetic variants. Analyses in the newborns of the 110 Black women showed that cord blood methylation levels at both loci had no associations with PTB. The findings from this study underscore the role of maternal DNAm in PTB risk, and provide a set of maternal loci that may serve as biomarkers for PTB. Longitudinal studies are needed to clarify temporal relationships between maternal DNAm and PTB risk.

GWAS identified variants associated with birth weight (BW), childhood obesity (CO) and childhood BMI (CBMI), and placenta is a critical organ for fetal development and postnatal health. We examined the role of placental transcriptome and eQTLs in mediating the genetic causes for BW, CO and CBMI, and applied integrative analysis (Colocalization and MetaXcan). GWAS loci associated with BW, CO, and CBMI were substantially enriched for placenta eQTLs (6.76, 4.83 and 2.26 folds, respectively). Importantly, compared to eQTLs of adult tissues, only placental eQTLs contribute significantly to both anthropometry outcomes at birth (BW) and childhood phenotypes (CO/CBMI). Eight, six and one transcripts colocalized with BW, CO and CBMI risk loci, respectively. Our study reveals that placental transcription in utero likely plays a key role in determining postnatal body size, and as such may hold new possibilities for therapeutic interventions to prevent childhood obesity., Author summary Genetic studies (e.g GWAS) revealed substantial heritability on birth weight (BW), childhood obesity (CO) and childhood body mass index (CBMI), however, the etiological mechanisms and relevant tissue(s) underlying these traits/conditions are not clear. We incorporated the data from largest GWASes to date and placenta expressional quantitative trait loci (eQTL) that have been newly published, and showed the variants associated with BW, CO and CBMI were substantially enriched for placenta eQTLs (6.76, 4.83 and 2.26 folds, respectively). Importantly, compared to eQTLs in 7 adult tissues such as adipose and liver, only eQTLs in the placenta were found to contribute significantly not only to anthropometry outcomes at birth (BW) but also to childhood phenotypes (CO/CBMI). Further, we employed COLOC and MetaXcan analyses and identified placenta transcripts potential mediate the genetic effect of BW/CO/CBMI GWAS loci. In summary, our study strongly supports a key role for the placenta in determining BW, CO and CMBI at the molecular level, and pinpointed genes whose expression levels in placenta potentially influences BW and CO risk.

It is a long standing question as to which genes define the characteristic facial features among different ethnic groups. In this study, we use Uyghurs, an ancient admixed population to query the genetic bases why Europeans and Han Chinese look different. Facial traits were analyzed based on high-dense 3D facial images; numerous biometric spaces were examined for divergent facial features between European and Han Chinese, ranging from inter-landmark distances to dense shape geometrics. Genome-wide association analyses were conducted on a discovery panel of Uyghurs. Six significant loci were identified four of which, rs1868752, rs118078182, rs60159418 at or near UBASH3B, COL23A1, PCDH7 and rs17868256 were replicated in independent cohorts of Uyghurs or Southern Han Chinese. A quantitative model was developed to predict 3D faces based on 277 top GWAS SNPs. In hypothetic forensic scenarios, this model was found to significantly enhance the verification rate, suggesting a practical potential of related research.

Human facial morphology is a combination of many complex traits. Little is known about the genetic basis of common facial morphological variation. Existing association studies have largely used simple landmark-distances as surrogates for the complex morphological phenotypes of the face. However, this can result in decreased statistical power and unclear inference of shape changes. In this study, we applied a new image registration approach that automatically identified the salient landmarks and aligned the sample faces using high density pixel points. Based on this high density registration, three different phenotype data schemes were used to test the association between the common facial morphological variation and 10 candidate SNPs, and their performances were compared. The first scheme used traditional landmark-distances; the second relied on the geometric analysis of 15 landmarks and the third used geometric analysis of a dense registration of ∼30,000 3D points. We found that the two geometric approaches were highly consistent in their detection of morphological changes. The geometric method using dense registration further demonstrated superiority in the fine inference of shape changes and 3D face modeling. Several candidate SNPs showed potential associations with different facial features. In particular, one SNP, a known risk factor of non-syndromic cleft lips/palates, rs642961 in the IRF6 gene, was validated to strongly predict normal lip shape variation in female Han Chinese. This study further demonstrated that dense face registration may substantially improve the detection and characterization of genetic association in common facial variation., Author Summary Heritability of human facial appearance is an intriguing question to the general public and researchers. Although it is known that some facial features are highly heritable, the exact genetic basis is unknown. Previous studies used simple linear measurements such as landmark distances, to evaluate the facial shape variation. Such approaches, although easy to carry out, may lack statistical power and miss complex morphological changes. In this study, we utilized a new 3D face registration method that enables subtle differences to be detected at high resolution 3D images. Based on this, we tried to test and characterize the associations of 10 candidate genetic variants to common facial morphological variations. Different types of phenotype data were extracted and compared in the association tests. Our results show that geometry based data performed better than simple distance based data. Furthermore, high density geometric data outstood the others in capturing small shape changes and modeling the 3D face visualization. Interestingly, a genetic variant from IRF6 gene, which is also a well-known risk factor of non-syndrome cleft lip, was found to strongly predispose the mouth shape in Han Chinese females.