Estimation of Genetic Correlation via Linkage Disequilibrium Score Regression and Genomic Restricted Maximum Likelihood.

Genetic correlation is a key population parameter that describes the shared genetic architecture of complex traits and diseases. It can be estimated by current state-of-art methods, i.e., linkage disequilibrium score regression (LDSC) and genomic restricted maximum likelihood (GREML). The massively reduced computing burden of LDSC compared to GREML makes it an attractive tool, although the accuracy (i.e., magnitude of standard errors) of LDSC estimates has not been thoroughly studied. In simulation, we show that the accuracy of GREML is generally higher than that of LDSC. When there is genetic heterogeneity between the actual sample and reference data from which LD scores are estimated, the accuracy of LDSC decreases further. In real data analyses estimating the genetic correlation between schizophrenia (SCZ) and body mass index, we show that GREML estimates based on ∼150,000 individuals give a higher accuracy than LDSC estimates based on ∼400,000 individuals (from combined meta-data). A GREML genomic partitioning analysis reveals that the genetic correlation between SCZ and height is significantly negative for regulatory regions, which whole genome or LDSC approach has less power to detect. We conclude that LDSC estimates should be carefully interpreted as there can be uncertainty about homogeneity among combined meta-datasets. We suggest that any interesting findings from massive LDSC analysis for a large number of complex traits should be followed up, where possible, with more detailed analyses with GREML methods, even if sample sizes are lesser.

Joint analysis of psychiatric disorders increases accuracy of risk prediction for schizophrenia, bipolar disorder, and major depressive disorder.

Genetic risk prediction has several potential applications in medical research and clinical practice and could be used, for example, to stratify a heterogeneous population of patients by their predicted genetic risk. However, for polygenic traits, such as psychiatric disorders, the accuracy of risk prediction is low. Here we use a multivariate linear mixed model and apply multi-trait genomic best linear unbiased prediction for genetic risk prediction. This method exploits correlations between disorders and simultaneously evaluates individual risk for each disorder. We show that the multivariate approach significantly increases the prediction accuracy for schizophrenia, bipolar disorder, and major depressive disorder in the discovery as well as in independent validation datasets. By grouping SNPs based on genome annotation and fitting multiple random effects, we show that the prediction accuracy could be further improved. The gain in prediction accuracy of the multivariate approach is equivalent to an increase in sample size of 34% for schizophrenia, 68% for bipolar disorder, and 76% for major depressive disorders using single trait models. Because our approach can be readily applied to any number of GWAS datasets of correlated traits, it is a flexible and powerful tool to maximize prediction accuracy. With current sample size, risk predictors are not useful in a clinical setting but already are a valuable research tool, for example in experimental designs comparing cases with high and low polygenic risk.

Simultaneous discovery, estimation and prediction analysis of complex traits using a bayesian mixture model.

Gene discovery, estimation of heritability captured by SNP arrays, inference on genetic architecture and prediction analyses of complex traits are usually performed using different statistical models and methods, leading to inefficiency and loss of power. Here we use a Bayesian mixture model that simultaneously allows variant discovery, estimation of genetic variance explained by all variants and prediction of unobserved phenotypes in new samples. We apply the method to simulated data of quantitative traits and Welcome Trust Case Control Consortium (WTCCC) data on disease and show that it provides accurate estimates of SNP-based heritability, produces unbiased estimators of risk in new samples, and that it can estimate genetic architecture by partitioning variation across hundreds to thousands of SNPs. We estimated that, depending on the trait, 2,633 to 9,411 SNPs explain all of the SNP-based heritability in the WTCCC diseases. The majority of those SNPs (>96%) had small effects, confirming a substantial polygenic component to common diseases. The proportion of the SNP-based variance explained by large effects (each SNP explaining 1% of the variance) varied markedly between diseases, ranging from almost zero for bipolar disorder to 72% for type 1 diabetes. Prediction analyses demonstrate that for diseases with major loci, such as type 1 diabetes and rheumatoid arthritis, Bayesian methods outperform profile scoring or mixed model approaches.

Performance of risk prediction for inflammatory bowel disease based on genotyping platform and genomic risk score method.

BACKGROUND: Predicting risk of disease from genotypes is being increasingly proposed for a variety of diagnostic and prognostic purposes. Genome-wide association studies (GWAS) have identified a large number of genome-wide significant susceptibility loci for Crohn's disease (CD) and ulcerative colitis (UC), two subtypes of inflammatory bowel disease (IBD). Recent studies have demonstrated that including only loci that are significantly associated with disease in the prediction model has low predictive power and that power can substantially be improved using a polygenic approach. METHODS: We performed a comprehensive analysis of risk prediction models using large case-control cohorts genotyped for 909,763 GWAS SNPs or 123,437 SNPs on the custom designed Immunochip using four prediction methods (polygenic score, best linear genomic prediction, elastic-net regularization and a Bayesian mixture model). We used the area under the curve (AUC) to assess prediction performance for discovery populations with different sample sizes and number of SNPs within cross-validation. RESULTS: On average, the Bayesian mixture approach had the best prediction performance. Using cross-validation we found little differences in prediction performance between GWAS and Immunochip, despite the GWAS array providing a 10 times larger effective genome-wide coverage. The prediction performance using Immunochip is largely due to the power of the initial GWAS for its marker selection and its low cost that enabled larger sample sizes. The predictive ability of the genomic risk score based on Immunochip was replicated in external data, with AUC of 0.75 for CD and 0.70 for UC. CD patients with higher risk scores demonstrated clinical characteristics typically associated with a more severe disease course including ileal location and earlier age at diagnosis. CONCLUSIONS: Our analyses demonstrate that the power of genomic risk prediction for IBD is mainly due to strongly associated SNPs with considerable effect sizes. Additional SNPs that are only tagged by high-density GWAS arrays and low or rare-variants over-represented in the high-density region on the Immunochip contribute little to prediction accuracy. Although a quantitative assessment of IBD risk for an individual is not currently possible, we show sufficient power of genomic risk scores to stratify IBD risk among individuals at diagnosis.

CYP39A1 polymorphism is associated with toxicity during intensive induction chemotherapy in patients with advanced head and neck cancer.

BACKGROUND: Induction chemotherapy incorporating docetaxel, cisplatin and 5- fluorouracil before radiotherapy may improve the outcome of patients with advanced head and neck cancer. Nevertheless, the addition of docetaxel increases hematological toxicity and infectious complications. Therefore, genetic markers predicting toxicity and efficacy of this treatment regimen may help to identify patients, who would have the most benefit from this intensive treatment. METHODS: A cohort of 78 patients with advanced head and neck cancer treated with induction chemotherapy was assessed for clinical outcome and toxicity during treatment with curative intention. Genetic polymorphisms primary associated with treatment efficacy (ERCC2-rs13181, rs1799793, ERCC1-rs3212986, rs11615, XRCC1-rs25487) or with docetaxel caused toxicity (CYP39A1-rs7761731, SLCO1B3-rs11045585) were evaluated in all patients. The results of these analyses were correlated with the clinical outcome of the patients (loco regional control, progression free survival, overall survival) and treatment related toxicity during induction chemotherapy. RESULTS: Median progression free survival and overall survival was 20 and 31 months in an intention to treat analysis, respectively. Overall response rate to induction chemotherapy was high with 78.1 % of all patients. None of the polymorphisms tested was associated with the clinical outcome of the patients. Genotype A of the CYP39A1 rs7761731 polymorphism was associated with a higher incidence of leucopenia and infections or death during induction chemotherapy. CONCLUSIONS: Intensive induction chemotherapy results in a high response rate in the majority of patients. None of the polymorphisms tested was associated with the clinical outcome of the patients. The CYP39A1 polymorphism rs7761731 may help to identify patients at high risk for treatment related toxicity.

Anti-Hu Antibody Associated Paraneoplastic Cerebellar Degeneration in Head and Neck Cancer.

BACKGROUND: Paraneoplastic syndromes are most frequently associated with small cell lung carcinoma, hematologic and gynecologic malignancies while reports in head and neck cancer are rare. CASE PRESENTATION: We present the case of a 60-year old female patient who developed paraneoplastic cerebellar degeneration upon locoregional recurrence of a poorly differentiated spindle cell carcinoma of the nasal cavity and paranasal sinus. The neurological symptoms, especially ataxia, stabilized after resection of tumor recurrence and concomitant chemoradiotherapy whereas anti-Hu-antibodies remained positive. Despite the unfavorable prognosis of paraneoplastic neurological disorders associated with onconeural antibodies, the patient achieved long-standing stabilization of neurological symptoms. CONCLUSION: We report the first patient with anti-Hu antibodies and paraneoplastic cerebellar degeneration associated with a spindle cell carcinoma of the head and neck. We recommend that evaluation of neurological symptoms in patients with this tumor entity should also include paraneoplastic syndromes as differential diagnoses and suggest early extensive screening for onconeural antibodies.

CT-scans of cochlear implant patients with characteristics of Pendred syndrome.

BACKGROUND: Sensorineural hearing loss (SNHL) in newborns is estimated with an incidence around 1:10,000 per year and is divided into syndromic and non-syndromic forms. In case of present retrocochlear function' cochlear implantation allows speech and cognitive development in affected children, comparable to that of normal hearing children. Pathogenesis of SNHL remains unclear in many cases. Imaging of the temporal bone, such as computed tomography (CT) and magnetic resonance imaging (MRI), can reveal conspicuous findings, e.g. enlarged vestibular aqueduct (EVA) and Mondini malformation (MM) of the cochlea. These malformations can be a clinical sign for Pendred syndrome. METHODS: We screened CT scans of 75 cochlear implant patients for EVA and MM. RESULTS: Six patients were observed to have either EVA alone (n=3), or MM alone (n=2), or a combination of both (n=1). Further malformations of the temporal bone could be found within the whole group, as well. CONCLUSION: Our results confirm the general opinion on EVA and MM, being commonly found in patients with SNHL. A possible association with Pendred syndrome needs to be confirmed by genetic investigations with search for mutations in the SLC26A4 gene and further clinical tests, such as Perchlorate test for surveillance of thyroid function.

Strategies and utility of imputed SNP genotypes for genomic analysis in dairy cattle.

BACKGROUND: We investigated strategies and factors affecting accuracy of imputing genotypes from lower-density SNP panels (Illumina 3K, 7K, Affymetrix 15K and 25K, and evenly spaced subsets) up to one medium (Illumina 50K) and one high-density (Illumina 800K) SNP panel. We also evaluated the utility of imputed genotypes on the accuracy of genomic selection using Australian Holstein-Friesian cattle data from 2727 and 845 animals genotyped with 50K and 800K SNP chip, respectively. Animals were divided into reference and test sets (genotyped with higher and lower density SNP panels, respectively) for evaluating the accuracies of imputation. For the accuracy of genomic selection, a comparison of direct genetic values (DGV) was made by dividing the data into training and validation sets under a range of imputation scenarios. RESULTS: Of the three methods compared for imputation, IMPUTE2 outperformed Beagle and fastPhase for almost all scenarios. Higher SNP densities in the test animals, larger reference sets and higher relatedness between test and reference animals increased the accuracy of imputation. 50K specific genotypes were imputed with moderate allelic error rates from 15K (2.85%) and 25K (2.75%) genotypes. Using IMPUTE2, SNP genotypes up to 800K were imputed with low allelic error rate (0.79% genome-wide) from 50K genotypes, and with moderate error rate from 3K (4.78%) and 7K (2.00%) genotypes. The error rate of imputing up to 800K from 3K or 7K was further reduced when an additional middle tier of 50K genotypes was incorporated in a 3-tiered framework. Accuracies of DGV for five production traits using imputed 50K genotypes were close to those obtained with the actual 50K genotypes and higher compared to using 3K or 7K genotypes. The loss in accuracy of DGV was small when most of the training animals also had imputed (50K) genotypes. Additional gains in DGV accuracies were small when SNP densities increased from 50K to imputed 800K. CONCLUSION: Population-based genotype imputation can be used to predict and combine genotypes from different low, medium and high-density SNP chips with a high level of accuracy. Imputing genotypes from low-density SNP panels to at least 50K SNP density increases the accuracy of genomic selection.

Human skeletal muscle: transition between fast and slow fibre types.

Human skeletal muscles consist of different fibre types: slow fibres (slow twitch or type I) containing the myosin heavy chain isoform (MHC)-I and fast fibres (fast twitch or type II) containing MHC-IIa (type IIA) or MHC-IId (type IID). The following order of decreasing kinetics is known: type IID > type IIA >> type I. This order is especially based on the kinetics of stretch activation, which is the most discriminative property among fibre types. In this study we tested if hybrid fibres containing both MHC-IIa and MHC-I (type C fibres) provide a transition in kinetics between fast (type IIA) and slow fibres (type I). Our data of stretch activation kinetics suggest that type C fibres, with different ratios of MHC-IIa and MHC-I, do not provide a continuous transition. Instead, a specialized group of slow fibres, which we called "transition fibres", seems to provide a transition. Apart of their kinetics of stretch activation, which is most close to that of type IIA, the transition fibres are characterized by large cross-sectional areas and low maximal tensions. The molecular cause for the mechanical properties of the transition fibres is unknown. It is possible that the transition fibres contain an unknown slow MHC isoform, which cannot be separated by biochemical methods. Alternatively, or in addition, isoforms of myofibrillar proteins, other than MHC, and posttranslational modifications of myofibrillar proteins could play a role regarding the characteristics of the transition fibres.

Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits.

We develop a Bayesian model (BayesRR-RC) that provides robust SNP-heritability estimation, an alternative to marker discovery, and accurate genomic prediction, taking 22 seconds per iteration to estimate 8.4 million SNP-effects and 78 SNP-heritability parameters in the UK Biobank. We find that only ≤10% of the genetic variation captured for height, body mass index, cardiovascular disease, and type 2 diabetes is attributable to proximal regulatory regions within 10kb upstream of genes, while 12-25% is attributed to coding regions, 32-44% to introns, and 22-28% to distal 10-500kb upstream regions. Up to 24% of all cis and coding regions of each chromosome are associated with each trait, with over 3,100 independent exonic and intronic regions and over 5,400 independent regulatory regions having ≥95% probability of contributing ≥0.001% to the genetic variance of these four traits. Our open-source software (GMRM) provides a scalable alternative to current approaches for biobank data.

Accuracy of direct genomic values in Holstein bulls and cows using subsets of SNP markers.

BACKGROUND: At the current price, the use of high-density single nucleotide polymorphisms (SNP) genotyping assays in genomic selection of dairy cattle is limited to applications involving elite sires and dams. The objective of this study was to evaluate the use of low-density assays to predict direct genomic value (DGV) on five milk production traits, an overall conformation trait, a survival index, and two profit index traits (APR, ASI). METHODS: Dense SNP genotypes were available for 42,576 SNP for 2,114 Holstein bulls and 510 cows. A subset of 1,847 bulls born between 1955 and 2004 was used as a training set to fit models with various sets of pre-selected SNP. A group of 297 bulls born between 2001 and 2004 and all cows born between 1992 and 2004 were used to evaluate the accuracy of DGV prediction. Ridge regression (RR) and partial least squares regression (PLSR) were used to derive prediction equations and to rank SNP based on the absolute value of the regression coefficients. Four alternative strategies were applied to select subset of SNP, namely: subsets of the highest ranked SNP for each individual trait, or a single subset of evenly spaced SNP, where SNP were selected based on their rank for ASI, APR or minor allele frequency within intervals of approximately equal length. RESULTS: RR and PLSR performed very similarly to predict DGV, with PLSR performing better for low-density assays and RR for higher-density SNP sets. When using all SNP, DGV predictions for production traits, which have a higher heritability, were more accurate (0.52-0.64) than for survival (0.19-0.20), which has a low heritability. The gain in accuracy using subsets that included the highest ranked SNP for each trait was marginal (5-6%) over a common set of evenly spaced SNP when at least 3,000 SNP were used. Subsets containing 3,000 SNP provided more than 90% of the accuracy that could be achieved with a high-density assay for cows, and 80% of the high-density assay for young bulls. CONCLUSIONS: Accurate genomic evaluation of the broader bull and cow population can be achieved with a single genotyping assays containing ~ 3,000 to 5,000 evenly spaced SNP.

Simultaneous Vestibular Schwannoma Resection and Cochlear Implantation Using Electrically Evoked Auditory Brainstem Response Audiometry for Decision-making.

OBJECTIVE: The aim of the study was to evaluate the predictive value regarding postoperative hearing benefit of electrically evoked auditory brainstem response audiometry in sporadic vestibular schwannoma patients undergoing simultaneous tumor resection and cochlear implantation. DESIGN: Patients were included in a prospective study conducted between October 2016 and January 2019. SETTING: The study was conducted at a tertiary care center. PARTICIPANTS: Subjects with unilateral sporadic vestibular schwannoma were screened for study participation. Patients underwent translabyrinthine vestibular schwannoma resection and cochlear implantation simultaneously. INTERVENTION: Electrically evoked brainstem response audiometry was performed during surgery before and after tumor removal using an intracochlear test electrode to objectively evaluate nerve conduction. MAIN OUTCOME MEASURE: Electrically evoked brainstem response audiometry results were correlated with postoperative sound field audiometry, word recognition tests, and speech reception thresholds. Quality of life was assessed before and 12 months after translabyrinthine tumor removal and cochlear implantation. RESULTS: Five patients, three male and two female, were included in the study and followed for at least 1 year after implantation. Three of the five patients are daily cochlear implant users with open set speech recognition. Two individuals with negative intraoperative electrically evoked auditory brainstem response results showed no auditory perception with cochlear implant. CONCLUSIONS: Simultaneous translabyrinthine vestibular schwannoma resection and cochlear implantation with intraoperative electrically evoked auditory brainstem response measurements is a feasible and promising option for sporadic vestibular schwannoma patients. Preservation of electrically evoked auditory brainstem responses seems to predict good subsequent hearing outcomes.

A comparison of five methods to predict genomic breeding values of dairy bulls from genome-wide SNP markers.

BACKGROUND: Genomic selection (GS) uses molecular breeding values (MBV) derived from dense markers across the entire genome for selection of young animals. The accuracy of MBV prediction is important for a successful application of GS. Recently, several methods have been proposed to estimate MBV. Initial simulation studies have shown that these methods can accurately predict MBV. In this study we compared the accuracies and possible bias of five different regression methods in an empirical application in dairy cattle. METHODS: Genotypes of 7,372 SNP and highly accurate EBV of 1,945 dairy bulls were used to predict MBV for protein percentage (PPT) and a profit index (Australian Selection Index, ASI). Marker effects were estimated by least squares regression (FR-LS), Bayesian regression (Bayes-R), random regression best linear unbiased prediction (RR-BLUP), partial least squares regression (PLSR) and nonparametric support vector regression (SVR) in a training set of 1,239 bulls. Accuracy and bias of MBV prediction were calculated from cross-validation of the training set and tested against a test team of 706 young bulls. RESULTS: For both traits, FR-LS using a subset of SNP was significantly less accurate than all other methods which used all SNP. Accuracies obtained by Bayes-R, RR-BLUP, PLSR and SVR were very similar for ASI (0.39-0.45) and for PPT (0.55-0.61). Overall, SVR gave the highest accuracy.All methods resulted in biased MBV predictions for ASI, for PPT only RR-BLUP and SVR predictions were unbiased. A significant decrease in accuracy of prediction of ASI was seen in young test cohorts of bulls compared to the accuracy derived from cross-validation of the training set. This reduction was not apparent for PPT. Combining MBV predictions with pedigree based predictions gave 1.05 - 1.34 times higher accuracies compared to predictions based on pedigree alone. Some methods have largely different computational requirements, with PLSR and RR-BLUP requiring the least computing time. CONCLUSIONS: The four methods which use information from all SNP namely RR-BLUP, Bayes-R, PLSR and SVR generate similar accuracies of MBV prediction for genomic selection, and their use in the selection of immediate future generations in dairy cattle will be comparable. The use of FR-LS in genomic selection is not recommended.

Improved polygenic prediction by Bayesian multiple regression on summary statistics.

Accurate prediction of an individual's phenotype from their DNA sequence is one of the great promises of genomics and precision medicine. We extend a powerful individual-level data Bayesian multiple regression model (BayesR) to one that utilises summary statistics from genome-wide association studies (GWAS), SBayesR. In simulation and cross-validation using 12 real traits and 1.1 million variants on 350,000 individuals from the UK Biobank, SBayesR improves prediction accuracy relative to commonly used state-of-the-art summary statistics methods at a fraction of the computational resources. Furthermore, using summary statistics for variants from the largest GWAS meta-analysis (n ≈ 700, 000) on height and BMI, we show that on average across traits and two independent data sets that SBayesR improves prediction R2 by 5.2% relative to LDpred and by 26.5% relative to clumping and p value thresholding.

Inference on the Genetic Basis of Eye and Skin Color in an Admixed Population via Bayesian Linear Mixed Models.

Genetic association studies in admixed populations are underrepresented in the genomics literature, with a key concern for researchers being the adequate control of spurious associations due to population structure. Linear mixed models (LMMs) are well suited for genome-wide association studies (GWAS) because they account for both population stratification and cryptic relatedness and achieve increased statistical power by jointly modeling all genotyped markers. Additionally, Bayesian LMMs allow for more flexible assumptions about the underlying distribution of genetic effects, and can concurrently estimate the proportion of phenotypic variance explained by genetic markers. Using three recently published Bayesian LMMs, Bayes R, BSLMM, and BOLT-LMM, we investigate an existing data set on eye (n = 625) and skin (n = 684) color from Cape Verde, an island nation off West Africa that is home to individuals with a broad range of phenotypic values for eye and skin color due to the mix of West African and European ancestry. We use simulations to demonstrate the utility of Bayesian LMMs for mapping loci and studying the genetic architecture of quantitative traits in admixed populations. The Bayesian LMMs provide evidence for two new pigmentation loci: one for eye color (AHRR) and one for skin color (DDB1).

Prognostic score in patients with recurrent or metastatic carcinoma of the head and neck treated with cetuximab and chemotherapy.

Despite modern treatment approaches, survival of patients with recurrent or metastatic squamous cell carcinoma of the head and neck (SCCHN) remains low and it is difficult to identify patients who derive optimal benefit from treatment. We therefore analyzed which commonly available laboratory and clinical parameters may help improve the prognostication in this patient group. This retrospective monocenter analysis includes 128 patients with recurrent or metastatic SCCHN treated with cetuximab alone or in combination with polychemotherapy as first line therapy. Factors with independent prognostic power in the multivariate analysis were used to build up a score separating patient groups with different survival. Patients had a median age of 61 years and 103 patients were treated with polychemotherapy plus cetuximab. An ECOG score above 1, high CRP and leukocyte levels, less intensive treatment and a time below 12 months from primary diagnosis to relapse remained as independent negative prognostic factors in multivariate analysis. Patients with 0 to 1 risk factors had a median OS of 13.6 months compared to a median OS of less than one month for patients 4 to 5 risk factors (p<0.001). This study identifies 5 clinical and serum values that influence survival of patients with recurrent or metastatic SCCHN treated with cetuximab. By combining these factors to create a score for OS, it is possible to distinguish a group of patients with significantly improved survival and define those most likely to have no benefit from cetuximab treatment.

Genotype-covariate interaction effects and the heritability of adult body mass index.

Obesity is a worldwide epidemic, with major health and economic costs. Here we estimate heritability for body mass index (BMI) in 172,000 sibling pairs and 150,832 unrelated individuals and explore the contribution of genotype-covariate interaction effects at common SNP loci. We find evidence for genotype-age interaction (likelihood ratio test (LRT) = 73.58, degrees of freedom (df) = 1, P = 4.83 × 10-18), which contributed 8.1% (1.4% s.e.) to BMI variation. Across eight self-reported lifestyle factors, including diet and exercise, we find genotype-environment interaction only for smoking behavior (LRT = 19.70, P = 5.03 × 10-5 and LRT = 30.80, P = 1.42 × 10-8), which contributed 4.0% (0.8% s.e.) to BMI variation. Bayesian association analysis suggests that BMI is highly polygenic, with 75% of the SNP heritability attributable to loci that each explain <0.01% of the phenotypic variance. Our findings imply that substantially larger sample sizes across ages and lifestyles are required to understand the full genetic architecture of BMI.

Development of the protympanum.

The protympanum, the only opening of the tympanic cavity, has enormous significance in the pathogenesis of middle ear inflammation. In our study, we analyzed 70 macerated temporal bones stereomicroscopically to understand the development of the protympanum. Details of this embryonic process were attained using histological sections made in frontal and horizontal planes. In contrast to recent descriptions, the present work reveals that almost the entire protympanum develops from different processes of the petrous bone. The tympanic bone takes part in forming only the posterior border of the lateral wall of the protympanum. Consequently the tympanic plate, which forms the inferior half of the mandibular fossa, is formed by two anlages. The anterosuperior quarter is formed by the petrous bone and the remainder by the tympanic bone. The main structures of the medial wall of the protympanum are the carotid canal with the internal carotid artery (ICA). The carotid canal starts to develop as two laminae of the petrous bone in the 18th fetal week. In cases of incomplete closure of these laminae the carotid canal occupies dehiscences increasing the danger of injury to the ICA. The presence of the ICA is necessary for the development of the carotid canal. If the artery takes an aberrant pathway in the tympanic cavity, or is absent, there is no sign of the carotid canal.

Development of the anterior chordal canal.

Resent advances have led to the reexamination of the intraosseous pathway of the chorda tympani a few years ago and they stated that the nerve never enters the mandibular fossa and its exit the skull base in the sphenopetrosal fissure. In our report, 58 temporal bones were investigated after maceration and formalin fixation in order to understand the development of the anterior chordal canal. Our study revealed that the chorda tympani leaves the tympanic cavity through the tympanosquamosal fissure before formation of the anterior chordal canal of Huguier. This canal is situated parallel to and in front of the musculotubal canal and formed by the processus inferior tegminis tympani and the sphenoid bone between the second and fifth years of age. Prior to the age of 2, only the exit of the bony canal exists which is gradually followed by the appearance of a groove in the growing processus inferior tegminis tympani. The borders of the groove elevate and develop to upper and lower plates which lengthen with similar plates of the sphenoid bone, completing the anterior chordal canal by the fifth postnatal year. The entrance of the canal develops above the petrotympanic fissure and similar to the canal itself, it is also completely formed in the fifth year. In case of an incomplete development the anterior chordal canal remains partially opened laterally which might allow the head of the mandibula to effect the chorda tympani mechanically causing Costen's syndrome.