Novel risk factors for craniofacial microsomia and assessment of their utility in clinic diagnosis

2021 ◽  
Author(s):  
Xiaopeng Xu ◽  
Bingqing Wang ◽  
Zhuoyuan Jiang ◽  
Qi Chen ◽  
Ke Mao ◽  
...  

Abstract Craniofacial microsomia (CFM, OMIM%164 210) is one of the most common congenital facial abnormalities worldwide, but it’s genetic risk factors and environmental threats are poorly investigated, as well as their interaction, making the diagnosis and prenatal screening of CFM impossible. We perform a comprehensive association study on the largest CFM cohort of 6074 samples. We identify 15 significant (P < 5 × 10−8) associated genomic loci (including eight previously reported) and decipher 107 candidates based on multi-omics data. Gene Ontology term enrichment found that these candidates are mainly enriched in neural crest cell (NCC) development and hypoxic environment. Single-cell RNA-seq data of mouse embryo demonstrate that nine of them show dramatic expression change during early cranial NCC development whose dysplasia is involved in pathogeny of CFM. Furthermore, we construct a well-performed CFM risk-predicting model based on polygenic risk score (PRS) method and estimate seven environmental risk factors that interacting with PRS. Single-nucleotide polymorphism-based PRS is significantly associated with CFM [P = 7.22 × 10−58, odds ratio = 3.15, 95% confidence interval (CI) 2.74–3.63], and the top fifth percentile has a 6.8-fold CFM risk comparing with the 10th percentile. Father’s smoking increases CFM risk as evidenced by interaction parameter of −0.324 (95% CI −0.578 to −0.070, P = 0.011) with PRS. In conclusion, the newly identified risk loci will significantly improve our understandings of genetics contribution to CFM. The risk prediction model is promising for CFM prediction, and father’s smoking is a key environmental risk factor for CFM through interacting with genetic factors.

2019 ◽  
Vol 21 (10) ◽  
Author(s):  
Simona A. Stilo ◽  
Robin M. Murray

Abstract Purpose of Review We review recent developments on risk factors in schizophrenia. Recent Findings The way we think about schizophrenia today is profoundly different from the way this illness was seen in the twentieth century. We now know that the etiology of schizophrenia is multifactorial and reflects an interaction between genetic vulnerability and environmental contributors. Environmental risk factors such as pregnancy and birth complications, childhood trauma, migration, social isolation, urbanicity, and substance abuse, alone and in combination, acting at a number of levels over time, influence the individual’s likelihood to develop the disorder. Summary Environmental risk factors together with the identification of a polygenic risk score for schizophrenia, research on gene–environment interaction and environment–environment interaction have hugely increased our knowledge of the disorder.


2018 ◽  
Author(s):  
Alexandra C. Gillett ◽  
Evangelos Vassos ◽  
Cathryn M. Lewis

1.Abstract1.1.ObjectiveStratified medicine requires models of disease risk incorporating genetic and environmental factors. These may combine estimates from different studies and models must be easily updatable when new estimates become available. The logit scale is often used in genetic and environmental association studies however the liability scale is used for polygenic risk scores and measures of heritability, but combining parameters across studies requires a common scale for the estimates.1.2.MethodsWe present equations to approximate the relationship between univariate effect size estimates on the logit scale and the liability scale, allowing model parameters to be translated between scales.1.3.ResultsThese equations are used to build a risk score on the liability scale, using effect size estimates originally estimated on the logit scale. Such a score can then be used in a joint effects model to estimate the risk of disease, and this is demonstrated for schizophrenia using a polygenic risk score and environmental risk factors.1.4.ConclusionThis straightforward method allows conversion of model parameters between the logit and liability scales, and may be a key tool to integrate risk estimates into a comprehensive risk model, particularly for joint models with environmental and genetic risk factors.


2010 ◽  
Author(s):  
Thomas A. Wills ◽  
Pallav Pokhrel ◽  
Frederick X. Gibbons ◽  
James D. Sargent ◽  
Mike Stoolmiller

2012 ◽  
Author(s):  
M. Pugliatti ◽  
I. Casetta ◽  
J. Drulovic ◽  
E. Granieri ◽  
T. Holmøy ◽  
...  

2019 ◽  
Author(s):  
I-Chao Liu ◽  
Shu-Fen Liao ◽  
Lawrence Shih-Hsin ◽  
Susan Shur-Fen Gau ◽  
Wen-Chung Lee ◽  
...  

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