scholarly journals The PAX1 locus at 20p11 is a modifier for bilateral cleft lip only

2020 ◽  
Author(s):  
Sarah W. Curtis ◽  
Daniel Chang ◽  
Myoung Keun Lee ◽  
John R. Shaffer ◽  
Karlijne Indencleef ◽  
...  
Keyword(s):  
Complex Traits ◽  
Genetic Architecture ◽  
Cleft Lip ◽  
Mixed Model ◽  
Cleft Lip And Palate ◽  
Phenotypic Heterogeneity ◽  
Mixed Model Approach ◽  
Genome Wide ◽  
A Genome ◽  
Model Approach

AbstractNonsyndromic orofacial clefts (OFCs) are the most common craniofacial birth defect in humans and, like many complex traits, OFCs are phenotypically and etiologically heterogenous. The phenotypic heterogeneity of OFCs extends beyond the structures affected by the cleft (e.g., cleft lip (CL) and cleft lip and palate (CLP) to other features, such as the severity of the cleft. Here, we focus on bilateral and unilateral clefts as one dimension of OFC severity. Unilateral clefts are more frequent than bilateral clefts for both CL and CLP, but the genetic architecture of these subtypes is not well understood, and it is not known if genetic variants predispose for the formation of one subtype over another. Therefore, we tested for subtype-specific genetic associations in 44 bilateral CL (BCL) cases, 434 unilateral CL (UCL) cases, 530 bilateral CLP cases (BCLP), 1123 unilateral CLP (UCLP) cases, and unrelated controls (N = 1626), using the mixed-model approach implemented in GENESIS. While no novel loci were found in subtype-specific analyses comparing cases to controls, the genetic architecture of UCL was distinct compared to BCL, with 43.8% of suggestive loci (p < 1.0×10−5) having non-overlapping confidence intervals between the two subtypes. To further understand the genetic risk factors for severity differences, we then performed a genome-wide scan for modifiers using a similar mixed-model approach and found one genome-wide significant modifier locus on 20p11 (p = 7.53×10−9), 300kb downstream of PAX1, associated with higher odds of BCL compared to UCL, which also replicated in an independent cohort (p = 0.0018) and showed no effect in BCLP (p>0.05). We further found that SNPs at this locus were associated with normal human nasal shape. Taken together, these results suggest bilateral and unilateral clefts may have differences in their genetic architecture, especially between CL and CLP. Moreover, our results suggest BCL, the rarest form of OFC, may be genetically distinct from the other OFC subtypes. This expands our understanding of genetic modifiers for subtypes of OFCs and further elucidates the genetic mechanisms behind the phenotypic heterogeneity in OFCs.

scholarly journals A systematic genetic analysis and visualization of phenotypic heterogeneity among orofacial cleft GWAS signals

2018 ◽  
Author(s):  
Jenna C. Carlson ◽  
Deepti Anand ◽  
Azeez Butali ◽  
Carmen J. Buxo ◽  
Kaare Christensen ◽  
...  
Keyword(s):  
Complex Traits ◽  
Cleft Lip ◽  
Cleft Lip And Palate ◽  
Expression Patterns ◽  
Complex Trait ◽  
Genetic Effects ◽  
Phenotypic Heterogeneity ◽  
New Associations ◽  
Orofacial Clefting ◽  
Shared Genetic

AbstractPhenotypic heterogeneity is a hallmark of complex traits, and genetic studies of such traits may focus on them as a single diagnostic entity or by analyzing specific components. For example, in orofacial clefting (OFC), three subtypes – cleft lip (CL), cleft lip and palate (CLP), and cleft palate (CP) have been studied separately and in combination. To further dissect the genetic architecture of OFCs and how a given associated locus may be contributing to distinct subtypes of a trait we developed a framework for quantifying and interpreting evidence of subtype-specific or shared genetic effects in complex traits. We applied this technique to create a “cleft map” of the association of 30 genetic loci with three OFC subtypes. In addition to new associations, we found loci with subtype-specific effects (e.g., GRHL3 (CP), WNT5A (CLP)), as well as loci associated with two or all three subtypes. We cross-referenced these results with mouse craniofacial gene expression datasets, which identified additional promising candidate genes. However, we found no strong correlation between OFC subtypes and expression patterns. In aggregate, the cleft map revealed that neither subtype-specific nor shared genetic effects operate in isolation in OFC architecture. Our approach can be easily applied to any complex trait with distinct phenotypic subgroups.

scholarly journals A mixed-model approach for genome-wide association studies of correlated traits in structured populations

2012 ◽  
Vol 44 (9) ◽  
pp. 1066-1071 ◽  
Author(s):  
Arthur Korte ◽  
Bjarni J Vilhjálmsson ◽  
Vincent Segura ◽  
Alexander Platt ◽  
Quan Long ◽  
...  
Keyword(s):  
Mixed Model ◽  
Association Studies ◽  
Structured Populations ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Correlated Traits ◽  
Mixed Model Approach ◽  
Genome Wide ◽  
Model Approach

scholarly journals Detection of Epistatic and Gene-Environment Interactions Underlying Three Quality Traits in Rice Using High-Throughput Genome-Wide Data

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Haiming Xu ◽  
Beibei Jiang ◽  
Yujie Cao ◽  
Yingxin Zhang ◽  
Xiaodeng Zhan ◽  
...  
Keyword(s):  
Linear Model ◽  
Complex Traits ◽  
Genetic Architecture ◽  
Mixed Linear Model ◽  
Processing Unit ◽  
Environment Interaction ◽  
Quality Traits ◽  
Gene Environment ◽  
Genome Wide ◽  
Model Approach

With development of sequencing technology, dense single nucleotide polymorphisms (SNPs) have been available, enabling uncovering genetic architecture of complex traits by genome-wide association study (GWAS). However, the current GWAS strategy usually ignores epistatic and gene-environment interactions due to absence of appropriate methodology and heavy computational burden. This study proposed a new GWAS strategy by combining the graphics processing unit- (GPU-) based generalized multifactor dimensionality reduction (GMDR) algorithm with mixed linear model approach. The reliability and efficiency of the analytical methods were verified through Monte Carlo simulations, suggesting that a population size of nearly 150 recombinant inbred lines (RILs) had a reasonable resolution for the scenarios considered. Further, a GWAS was conducted with the above two-step strategy to investigate the additive, epistatic, and gene-environment associations between 701,867 SNPs and three important quality traits, gelatinization temperature, amylose content, and gel consistency, in a RIL population with 138 individuals derived from super-hybrid rice Xieyou9308 in two environments. Four significant SNPs were identified with additive, epistatic, and gene-environment interaction effects. Our study showed that the mixed linear model approach combining with the GPU-based GMDR algorithm is a feasible strategy for implementing GWAS to uncover genetic architecture of crop complex traits.

A Mixed Model Approach to Identify Yeast Transcriptional Regulatory Motifs via Microarray Experiments

2004 ◽  
Vol 3 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Xiang Yu ◽  
Tzu-Ming Chu ◽  
Greg Gibson ◽  
Russell D Wolfinger
Keyword(s):  
Regulatory Networks ◽  
Mixed Model ◽  
Regulatory Structure ◽  
Modeling Framework ◽  
Binding Interactions ◽  
Mixed Model Approach ◽  
Genome Wide ◽  
Transcriptional Regulatory ◽  
A Genome ◽  
Tf Gene

A genome-wide location analysis method has been introduced as a means to simultaneously study protein-DNA binding interactions for a large number of genes on a microarray platform. Identification of interactions between transcription factors (TF) and genes provide insight into the mechanisms that regulate a variety of cellular responses. Drawing proper inferences from the experimental data is key to finding statistically significant TF-gene binding interactions. We describe how the analysis and interpretation of genome-wide location data can be fit into a traditional statistical modeling framework that considers the data across all arrays and formulizes appropriate hypothesis tests. The approach is illustrated with data from a yeast transcription factor binding experiment that illustrates how identified TF-gene interactions can enhance initial exploration of transcriptional regulatory networks. Examples of five kinds of transcriptional regulatory structure are also demonstrated. Some stark differences with previously published results are explored.

scholarly journals The ABCC4 gene is associated with pyometra in golden retriever dogs

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Maja Arendt ◽  
Aime Ambrosen ◽  
Tove Fall ◽  
Marcin Kierczak ◽  
Katarina Tengvall ◽  
...  
Keyword(s):  
Risk Factors ◽  
Genetic Risk ◽  
Mixed Model ◽  
Genome Wide Association Study ◽  
Genetic Risk Factors ◽  
Disease Development ◽  
Mixed Model Approach ◽  
Genome Wide ◽  
A Genome ◽  
Increased Risk

AbstractPyometra is one of the most common diseases in female dogs, presenting as purulent inflammation and bacterial infection of the uterus. On average 20% of intact female dogs are affected before 10 years of age, a proportion that varies greatly between breeds (3–66%). The clear breed predisposition suggests that genetic risk factors are involved in disease development. To identify genetic risk factors associated with the disease, we performed a genome-wide association study (GWAS) in golden retrievers, a breed with increased risk of developing pyometra (risk ratio: 3.3). We applied a mixed model approach comparing 98 cases, and 96 healthy controls and identified an associated locus on chromosome 22 (p = 1.2 × 10–6, passing Bonferroni corrected significance). This locus contained five significantly associated SNPs positioned within introns of the ATP-binding cassette transporter 4 (ABCC4) gene. This gene encodes a transmembrane transporter that is important for prostaglandin transport. Next generation sequencing and genotyping of cases and controls subsequently identified four missense SNPs within the ABCC4 gene. One missense SNP at chr22:45,893,198 (p.Met787Val) showed complete linkage disequilibrium with the associated GWAS SNPs suggesting a potential role in disease development. Another locus on chromosome 18 overlapping the TESMIN gene, is also potentially implicated in the development of the disease.

scholarly journals Gene-Gene Interactions among SPRYs for Nonsyndromic Cleft Lip/Palate

2018 ◽  
Vol 98 (2) ◽  
pp. 180-185 ◽  
Author(s):  
R. Zhou ◽  
M. Wang ◽  
W. Li ◽  
S. Wang ◽  
Z. Zhou ◽  
...  
Keyword(s):  
Association Study ◽  
Genetic Architecture ◽  
Cleft Lip ◽  
Genome Wide Association Study ◽  
Conditional Logistic Regression ◽  
Genome Wide Association ◽  
Gene Interactions ◽  
Genome Wide ◽  
A Genome ◽  
Biological Studies

Nonsyndromic cleft lip with or without cleft palate (NSCL/P) is a common birth defect with a complex genetic architecture. Gene-gene interactions have been increasingly regarded as contributing to the etiology of NSCL/P. A recent genome-wide association study revealed that a novel single-nucleotide polymorphism at SPRY1 in 4q28.1 showed a significant association with NSCL/P. In the current study, we explored the role of 3 SPRY genes in the etiology of NSCL/P by detecting gene-gene interactions: SPRY1, SPRY2, and SPRY4—with SPRY3 excluded due to its special location on the X chromosome. We selected markers in 3 SPRY genes to test for gene-gene interactions using 1,908 case-parent trios recruited from an international consortium established for a genome-wide association study of nonsyndromic oral clefts. As the trios came from populations with different ancestries, subgroup analyses were conducted among Europeans and Asians. Cordell’s method based on conditional logistic regression models was applied to test for potential gene-gene interactions via the statistical package TRIO in R software. Gene-gene interaction analyses yielded 10 pairs of SNPs in Europeans and 6 pairs in Asians that achieved significance after Bonferroni correction. The significant interactions were confirmed in the 10,000-permutation tests (empirical P = 0.003 for the most significant interaction). The study identified gene-gene interactions among SPRY genes among 1,908 NSCL/P trios, which revealed the importance of potential gene-gene interactions for understanding the genetic architecture of NSCL/P. The evidence of gene-gene interactions in this study also provided clues for future biological studies to further investigate the mechanism of how SPRY genes participate in the development of NSCL/P.

scholarly journals An efficient multi-locus mixed model framework for the detection of small and linked QTLs in F2

2018 ◽  
Vol 20 (5) ◽  
pp. 1913-1924 ◽  
Author(s):  
Yang-Jun Wen ◽  
Ya-Wen Zhang ◽  
Jin Zhang ◽  
Jian-Ying Feng ◽  
Jim M Dunwell ◽  
...  
Keyword(s):  
Complex Traits ◽  
Mixed Model ◽  
Adaptive Lasso ◽  
P Value ◽  
Ratio Test ◽  
Model Framework ◽  
Genome Scanning ◽  
Locus Model ◽  
Genome Wide ◽  
A Genome

Abstract In the genetic system that regulates complex traits, metabolites, gene expression levels, RNA editing levels and DNA methylation, a series of small and linked genes exist. To date, however, little is known about how to design an efficient framework for the detection of these kinds of genes. In this article, we propose a genome-wide composite interval mapping (GCIM) in F2. First, controlling polygenic background via selecting markers in the genome scanning of linkage analysis was replaced by estimating polygenic variance in a genome-wide association study. This can control large, middle and minor polygenic backgrounds in genome scanning. Then, additive and dominant effects for each putative quantitative trait locus (QTL) were separately scanned so that a negative logarithm P-value curve against genome position could be separately obtained for each kind of effect. In each curve, all the peaks were identified as potential QTLs. Thus, almost all the small-effect and linked QTLs are included in a multi-locus model. Finally, adaptive least absolute shrinkage and selection operator (adaptive lasso) was used to estimate all the effects in the multi-locus model, and all the nonzero effects were further identified by likelihood ratio test for true QTL identification. This method was used to reanalyze four rice traits. Among 25 known genes detected in this study, 16 small-effect genes were identified only by GCIM. To further demonstrate GCIM, a series of Monte Carlo simulation experiments was performed. As a result, GCIM is demonstrated to be more powerful than the widely used methods for the detection of closely linked and small-effect QTLs.

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