Colin Decourt

Luc Duong

Semi-supervised generative adversarial networks for the segmentation of the left ventricle in pediatric MRI

Keywords: Segmentation, Generative Adversarial Networks, Semi-Supervised Learning, Distance Transform, Cardiac, Magnetic Resonance Imaging

Segmentation of the left ventricle in magnetic resonance imaging (MRI) is important for assessing cardiac function. We present DT-GAN, a generative adversarial network (GAN) segmentation approach for the identification of the left ventricle in pediatric MRI. Segmentation of the left ventricle requires a large amount of annotated data; generating such data can be time-consuming and subject to observer variability. Additionally, it can be difficult to accomplish in a clinical setting. During the training of our GAN, we therefore introduce a semi-supervised semantic segmentation to reduce the number of images required for training, while maintaining a good segmentation accuracy. The GAN generator produces a segmentation label map and its discriminator outputs a confidence map, which gives the probability of a pixel coming from the label or from the generator. Moreover, we propose a new formulation of the GAN loss function based on distance transform and pixel-wise cross-entropy. This new loss function provides a better segmentation of boundary pixels, by favoring the correct classification of those pixels rather than focusing on pixels that are farther away from the boundary between anatomical structures. Our proposed method achieves a mean Hausdorff distance of 2.16 mm ± 0.42 mm (2.28 mm ± 0.21 mm for U-Net) and a Dice score of 0.88 ± 0.08 (0.91 ± 0.12 for U-Net) for the endocardium segmentation, using 50% of the annotated data. For the epicardium segmentation, we achieve a mean Hausdorff distance of 2.23 mm ± 0.35 mm (2.34 mm ± 0.39 mm for U-Net) and a Dice score of 0.93 mm ± 0.04 mm (0.89 ± 0.09 for U-Net). For the myocardium segmentation, we achieve a mean Hausdorff distance of 2.98 mm ± 0.43 mm (3.04 mm ± 0.27 mm for U-Net) and a Dice score of 0.79 mm ± 0.10 mm (0.74 ± 0.04 for U-Net). This new model could be very useful for the automatic analysis of cardiac MRI and for conducting large-scale studies based on MRI readings, with a limited amount of training data.

Introduction

Heart function analysis is crucial in clinical cardiology, especially in the evaluation of the left ventricular systolic function. An abnormal function of the left ventricle suggests the presence of heart disease. MRI imaging has been shown to be promising for the assessment of the left ventricle, and recent years have seen significant improvements in segmentation methods, such as semantic segmentation. Semantic segmentation is the task of assigning a class to every pixel in a given image. Usually, semantic segmentation is solved using supervised learning-based segmentation methods. Automatic segmentation methods for the left ventricle would be particularly useful for the clinical assessment of heart function, morphology, and temporal dynamics. However, such methods require a large amount of data that has been validated and annotated by experts. However, such annotation is fastidious, time-consuming, subject to observer variability, and potentially difficult to perform in a clinical setting. In recent years, atlas-based MRI segmentation ( [START_REF] Bai | Multi-Atlas Segmentation with Augmented Features for Cardiac MR Images[END_REF], [START_REF] Farrar | Atlas-Based Ventricular Shape Analysis for Understanding Congenital Heart Disease[END_REF], [START_REF] Iglesias | Multi-Atlas Segmentation of Biomedical Images: A Survey[END_REF], [START_REF] Medrano-Gracia | The Cardiac Atlas Project: Preliminary Description of Heart Shape in Patients with Myocardial Infarction[END_REF], [START_REF] Yang | Deep Fusion Net for Multiatlas Segmentation: Application to Cardiac MR Images[END_REF], [START_REF] Gilbert | Atlas-Based Computational Analysis of Heart Shape and Function in Congenital Heart Disease[END_REF], [START_REF] Bai | A Bi-Ventricular Cardiac Atlas Built From 1000+ High Resolution MR Images of Healthy Subjects and an Analysis of Shape and Motion[END_REF]) has shown promising results in detecting Email address: colin.decourt@mailfence.com (Colin Decourt) heart diseases. To be used in clinical applications, atlas-based segmentation methods, however, require many steps, and are both computationally-and time-intensive ( [START_REF] Iglesias | Multi-Atlas Segmentation of Biomedical Images: A Survey[END_REF]). These limitations and the success of deep learning ( [START_REF] Avendi | A Combined Deep-Learning and Deformable-Model Approach to Fully Automatic Segmentation of the Left Ventricle in Cardiac MRI[END_REF], [START_REF] Zhang | Radiological Images and Machine Learning: Trends, Perspectives, and Prospects[END_REF], [START_REF] Abdelmaguid | Left Ventricle Segmentation and Volume Estimation on Cardiac MRI using Deep Learning[END_REF], [START_REF] Yang | Deep Convolutional Neural Networks for Automatic Segmentation of Left Ventricle Cavity from Cardiac Magnetic Resonance Images[END_REF], [START_REF] Khened | Fully Convolutional Multi-Scale Residual DenseNets for Cardiac Segmentation and Automated Cardiac Diagnosis using Ensemble of Classifiers[END_REF], [START_REF]Automatic Segmentation of Left Ventricle from Cardiac MRI via Deep Learning and Region Constrained Dynamic Programming[END_REF], [START_REF] Tong | RIANet: Recurrent Interleaved Attention Network for Cardiac MRI Segmentation[END_REF]), particularly of the U-Net architecture ( [START_REF] Ronneberger | U-Net: Convolutional Networks for Biomedical Image Segmentation[END_REF], [START_REF] Baldeon-Calisto | Adaresu-Net: Multiobjective Adaptive Convolutional Neural Network for Medical Image Segmentation[END_REF]) have contributed to the popularity of deep learning for a fully automatic left ventricle segmentation. The U-Net architecture consists of a contracting path that extracts the most relevant features, followed by an expanding path that uses up-convolutions to increase the feature map to the size of the segmented image. Deep learning models help improve left ventricle segmentation precision because neural networks are able to extract low-, mid-and high-level features. Nonetheless, both the U-Net and the other deep learning models require a large amount of annotated data to learn the proper segmentation.

Over the last five years, GAN ( [START_REF] Goodfellow | Generative Adversarial Nets[END_REF]) has become very popular in computer vision, especially for image synthesis ( [START_REF] Tmenova | CycleGAN for Style Transfer in X-ray Angiography[END_REF]) and image segmentation ( [START_REF] Luc | Semantic Segmentation using Adversarial Networks[END_REF]). Semi-supervised learning methods using adversarial networks have been proposed to improve the amount of data required for image segmentation ( [START_REF] Baur | Semi-supervised Deep Learning for Fully Convolutional Networks[END_REF], [START_REF] Hung | Adversarial Learning for Semi-Supervised Semantic Segmentation[END_REF], [START_REF] Souly | Semi Supervised Semantic Segmentation Using Generative Adversarial Network[END_REF]). This has in turn led to the wide use of GAN-based segmentation in many clinical MRI applications, such as brain imaging and prostate imaging ( [START_REF] Kazeminia | [END_REF], [START_REF] Xue | Segan: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation[END_REF]). However, to the best of our knowledge, no previous work has studied the segmentation of the left ventricle in pediatric MRI using generative adversarial networks and semi-supervised learning.

In this study, we present a semi-supervised segmentation algorithm using GAN, which is capable of segmenting the left ventricle in pediatric MRI with limited annotated data. We propose a new loss term based on the formulation of distance transform and pixel-wise cross-entropy to improve the segmentation results ( [START_REF] Caliva | Distance Map Loss Penalty Term for Semantic Segmentation[END_REF]) during the supervised training phase. This loss is used in a pre-trained segmentation network of GAN. The training procedure is based on a segmentation network which acts as a generator, and a discriminator network, which helps the segmentation network to produce better segmentation by generating a confidence map. According to Hung et al. ([21]), this confidence map is used as a signal to guide the cross-entropy loss in a self-taught manner. To leverage unlabeled data, two semi-supervised loss terms are combined. An adversarial loss is used on unlabeled data to encourage the model to predict their segmentation close to the ground truth distributions. This work is organized as follows: Section 2, "Related work" present previous work on real and medical image segmentation using GANs. Section 3, "Methodology" presents our segmentation approach, including image preprocessing and the new loss function, and introduces our semi-supervised GAN architecture. Section 4, "Results" presents experimental results for the segmentation of the endocardium and the epicardium and section 3, "Discussion", discusses the limits of our model. Then, the "Conclusion" outlines some future work.

Related work

GAN for image segmentation

GAN ( [START_REF] Goodfellow | Generative Adversarial Nets[END_REF]) has been successfully used for supervised segmentation ( [START_REF] Xue | Segan: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation[END_REF]) or for semi-supervised segmentation ( [START_REF] Souly | Semi Supervised Semantic Segmentation Using Generative Adversarial Network[END_REF]). The general formulation of a GAN has two parts:

• A generator G(z; θ G )
where G is a differentiable function and θ G are the parameters of G. To learn a distribution p g a prior input noise is defined as p z (z).

• A discriminator D(x; θ D ) where D where D is trained to maximize the probability of the data coming from p g or from the real data distribution p data .

G is trained simultaneously to D to minimize log(1 -D(G(z)) where z ∈ p z and p z are each a prior input noise distribution. D and G play a two-player minimax game with value function :

(1) min G max D L(D, G) = E x∼p data (x) [log(D(x)] + E z∼p z (z) [log(1 -D(G(z))]
The generator G is trained to minimize L in order to reproduce the true data distribution p data and to fool the discriminator by generating images which are difficult to differentiate from real images. Concurrently, the discriminator D is trained to maximize L and to distinguish if images come from p g or p data . In recent years, several studies have focused on supervised and semi-supervised learning approaches using GAN. Luc et al. ([19]) trained a fully convolutional neural network for image segmentation (the generator) and added another network (the discriminator) to deduce whether the segmentation results originate from the generator (fake) or from the label (real). They demonstrated that such an antagonistic approach reduces overfitting during training. Hung et al. ([21]) also developed a discriminator trained to identify images that are fake and real, using the images' spatial resolution. This semi-supervised training technique was introduced to reduce the amount of learning data required for training. Souly et al. ([22]) also proposed a semi-supervised learning model using GAN. The novelty of their model lay in its generation of additional training data and a discriminator classifying images between their class memberships or as being generated. While these methods outperformed supervised learning methods, the images generated using them were still based on pixel intensities. According to [START_REF] Kazeminia | [END_REF], pixel intensities are not always associated with the actual physical properties of the anatomical structure.

GAN for medical image segmentation

When they were initially introduced, GAN-based methods were quickly adopted for medical image segmentation due to their high performance. However, in practice, early GAN architectures were difficult to train. Most GAN discriminators only returned a single probability, indicating the quality of segmentation and suffered from training instabilities. With fully convolutional GAN, better segmentation is obtained, with a quality level much closer to that of the ground truth segmentation, while the discriminator determines whether the segmentation originates from the generator or from the label ( [START_REF] Izadi | Generative Adversarial Networks To Segment Skin Lesions[END_REF], [START_REF] Son | Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks[END_REF], [START_REF] Zhang | Deep Adversarial Networks for Biomedical Image Segmentation Utilizing Unannotated Images[END_REF], [START_REF] Li | Brain Tumor Segmentation Using an Adversarial Network[END_REF], [START_REF] Pham | Simultaneous Super-Resolution and Segmentation Using a Generative Adversarial Network: Application to Neonatal Brain MRI[END_REF]). This process is conducted on a pixel-by-pixel basis, to improve the accuracy of the segmentation. Based on this principle, Xue et al. ( [START_REF] Xue | Segan: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation[END_REF]) proposed SegAN, a novel adversarial critic network with a common multiscale L1-loss function, to force the critic and segmentor to learn both local and global features. Based on this, Upendra et al. ( [START_REF] Upendra | An adversarial network architecture using 2d u-net models for segmentation of left ventricle from cine cardiac mri[END_REF]) investigated 3 types of U-Net-inspired architectures for left ventricle segmentation. They showed that U-Net performed better when trained in a SegAN framework. Still using a discriminator to distinguish whether a segmentation map comes from the generator or from the ground truth, Pham et al. ( [START_REF] Pham | Simultaneous Super-Resolution and Segmentation Using a Generative Adversarial Network: Application to Neonatal Brain MRI[END_REF]) used the GAN generator for progressive reconstruction at different scales and segmentations of brain MRI. Another method was proposed by Rezaei et al. ( [START_REF] Rezaei | Whole Heart and Great Vessel Segmentation with Context-aware of Generative Adversarial Networks[END_REF]) consisting of a cascade of conditional GANs for semantic segmentation to segment the myocardium on patients with congenital heart disease. In [START_REF] Xu | Mutgan: Simultaneous segmentation and quantification of myocardial infarction without contrast agents via joint adversarial learning[END_REF], Xu et al. proposed MutGAN, a multitask GAN, to segment and quantify myocardial infarction. MutGAN consists of a generator and a discriminator module, and is implemented by 3 seamless connected networks, with each network learning different bits of information for use in specific tasks. However, apart from [START_REF] Zhang | Deep Adversarial Networks for Biomedical Image Segmentation Utilizing Unannotated Images[END_REF], these methods are all fully-supervised. In [START_REF] Zhang | Deep Adversarial Networks for Biomedical Image Segmentation Utilizing Unannotated Images[END_REF] the model assessed the quality of the segmentation using a scoring method and provides a segmentation for unannotated images through an iterative adversarial training process. The above methods have yet to be applied for cardiac MRI segmentation, though. Furthermore, discriminators are designed to determine whether or not a segmentation is real, and do not consider the spatial resolution of the image, which is very important for medical image segmentation. Finally in [START_REF] Chartsias | Factorised spatial representation learning: Application in semi-supervised myocardial segmentation[END_REF], the authors proposed a latent space factorization methodology relying on the cycle consistency principle. They demonstrated this method in a semi-supervised setting for myocardium segmentation.

Methodology

In this section, we describe our experimental dataset and the proposed segmentation method, including the preprocessing steps, the network architecture and the design of the loss functions for both supervised and semi-supervised contexts.

Dataset

The proposed method was evaluated on short-axis cardiac MR image sequences provided by the Department of Diagnostic Imaging of the Hospital for Sick Children in Toronto, Canada ( [START_REF]Efficient and Generalizable Statistical Models of Shape and Appearance for Analysis of Cardiac MRI[END_REF]). The MRI dataset is comprised of 33 subjects and each patient's scan consisted of 20 frames, each with 8 to 15 slices, for a total of 10,022 images. Patient ages ranged from 2 to 18 years old. Two patients were normal. The remaining subjects displayed a variety of heart abnormalities, such as cardiomyopathy, aortic regurgitation, enlarged ventricles and ischemia. The images were scanned with a GE Genesis Signa MR scanner, using the FIESTA scan protocol. All slices were 256×256 in size, but the pixel resolution and spacing between slices differed for each patient. This dataset is composed of 3D images, but for this study, we focused on the segmentation of 2D images. Moreover, the dataset contained 5011 images, with contours drawn by clinicians to provide a ground truth (GT) for the segmentation of the endocardium and epicardium. Therefore, all in all, we used 5011 images to train, test and evaluate the network.

Image pre-processing

Each MR image was scanned using the same MR scanner, and using the same protocol. However, the pixel spacing differed from one patient to the next, and all volumes had to be normalized. For each image, we applied the following preprocessing steps: As explained in 3.1 the segmentation of the endocardium and the epicardium were provided by the dataset, in the form of 2D coordinates in the image. We used these coordinates to create a binary mask as the ground truth, as shown in Figure 1.

GAN architecture and algorithm overview

Figure 2 shows the architecture of the GAN used for this study (similar to what is described in [START_REF] Hung | Adversarial Learning for Semi-Supervised Semantic Segmentation[END_REF]). It consists of two networks: a segmentation network and a discriminator network.

For the segmentation network, we used a DeepLab-v2 framework ( [START_REF] Chen | DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs[END_REF]) with ResNet-101 ( [START_REF] He | Deep Residual Learning for Image Recognition[END_REF]) with the same modification used by authors of [START_REF] Hung | Adversarial Learning for Semi-Supervised Semantic Segmentation[END_REF]. We used pre-trained weights to ensure quick convergence of the segmentation network. The last classification layer of ResNet-101 was removed and the stride of the last two convolution layers was changed from 2 to 1. A dilated convolution from [START_REF] Yu | Multi-scale context aggregation by dilated convolutions[END_REF] was applied to the conv4 and conv5 layers, with strides of 2 and 4, respectively. Then, the Atrous Spatial Pyramid Pooling method ( [START_REF] Chen | DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs[END_REF]) was used in the last layer. Finally, an up-sampling layer was added to match the size of the input image.

The discriminator network was based on a fully convolutional network (FCN). It consisted of 5 convolution layers, with a 4×4 kernel. The layers had 64, 128, 256, 512 and 1 channels, respectively. We used a stride of 2 for each layer. A leaky ReLU activation function was used for all layers, except the last one. An up-sampling layer was added to the last layer in order to rescale the output to the size of the input map.

The segmentation network was designed to receive as input a set of cardiac MRI images of size H×W×3 and outputs a class probability maps of dimension H×W×2. The latter were divided into 2 classes, namely, the endocardium or epicardium and the background. The discriminator network received as input a class probability map obtained either from the segmentation network or from the ground truth (GT). It output a confidence map of size H×W×1 where each pixel p i represented the probability of p i originating from the ground truth or from the segmentation network. During the training, both labeled and unlabeled data are used under the semi-supervised setting with different loss functions. When labeled data is used, training is supervised both by the distance map loss penalty term ( [START_REF] Caliva | Distance Map Loss Penalty Term for Semantic Segmentation[END_REF]) L pm with the GT label, and by the adversarial loss L adv with the discriminator network. For unlabeled data, segmentation network is trained as follows. After a prediction of the segmentation made by S is obtained, a confidence map is computed by passing the prediction through D. This confidence map is used as a supervisory signal to train S with a masked cross-entropy loss (the semi-supervised loss) L semi . Because the confidence indicates the quality of the segmentation, the confidence map help S to know the regions it can trust during the training.

L D = - h,w ((1 -q) • log(1 -D(S(x)) (h,w) )) + (q • log(D(y) (h,w) )) (2)
where : q = 0 if the sample come from S q = 1 if the sample come from the ground truth label [START_REF] Iglesias | Multi-Atlas Segmentation of Biomedical Images: A Survey[END_REF] where x is the input image, y the GT label and ŷ the predicted label. In addition, the pixel value of y in coordinate (h, w) is denoted by y (h,w) .

Segmentation network training

The segmentation network is trained by minimizing the multitask loss function below:

L seg = L pm + λ adv L adv + λ semi L semi (4)
where λ adv and λ semi are two weights for minimizing L seg .

Considering the training with annotated data, the distant map loss penalty term is firstly computed.

Distance map loss penalty term. Semantic GAN segmentation focuses on categories, while in medical imaging, the accuracy of the contour of the anatomical structure is very important. In order to improve the shape preservation at left ventricle boundaries, we introduced a loss function based on the distance transform of the ground truth to penalize overand under-segmentation. The distance transform allowed to interpolate the 2D distance between each pixel to the ground truth segmentation. We use the following loss function:

L pm = 1 N N i=1 (1 + Φ γ ) h,w c∈C -y (h,w) log(ŷ (h,w) ) (5)
where the second sum is the pixel-wise cross-entropy between the predicted label and the ground truth label; C is the number of classes; N = H × W. is the Hadamard product and Φ is our distance map penalty term. To compute Φ we first compute the distance transform of the inverse of the ground truth and then invert it to favor the pixels closer to the boundaries. The variable γ controls the fit with the LV boundary, and was set experimentally to 20 in the present study. Then, we compute the distance transform ground truth to obtain a distance map inside the left ventricle. In other words, we compute the inward and outward distance from the object's boundaries.

Adversarial loss. The adversarial loss is used to train the segmentation network to fool the discriminator (trained only with labeled data) by maximizing the probability of the predicted results being generated from the ground truth distribution. Given the discriminator network D, L adv is defined as :

L adv = - h,w log(D(S (h,w) (x)) (6)
Training with unlabeled data. Hung et al. ( [START_REF] Hung | Adversarial Learning for Semi-Supervised Semantic Segmentation[END_REF]) considered adversarial training under a semi-supervised setting. For unlabeled data L pm is not used because labeled data are not available. The adversarial loss is still applicable because it requires only D. Note that in this step of the training, the discriminator network is already trained. Going forward, this trained discriminator is used to generate a confidence map D(S(x)) which describes the regions sufficiently close to those from the ground truth distribution ( [START_REF] Hung | Adversarial Learning for Semi-Supervised Semantic Segmentation[END_REF]). The semi-supervised loss is define as :

L semi = - h,w I(D(S(x)) (h,w) > T semi ) • ŷ(h,w) log(S(x) (h,w) ) (7)
Where T semi is a threshold applied on the confidence map to highlight the trustworthy region and I is defined as :

I(x) =        1 if D(S(x)) (h,w) > T semi 0 otherwise (8)
T semi value is set to 0.2. Values below this threshold produced trustworthy regions too small to properly improve the segmentation under the semi-supervised setting. The same analysis was done with a threshold higher than 0.2.

Experimental setup and assessing metrics

Experimental setup

The model was implemented using Python and the deep learning framework, PyTorch. We trained and evaluated our model on the dataset described in 3.1. To avoid overfitting, we applied cross-validation during the training. Because the dataset was composed of 33 patients, we broke it down into three different sets: training (25 patients), testing (4 patients) and validation (4 patients). Then, all images of patients in each set were shuffled to avoid considering two subsequent images in a batch and avoid biasing the training. We used the Adam optimizer for both networks with a weight decay of 10 -4 and a learning rate of 2.5 × 10 -4 for the segmentation network and of 10 -4 for the discriminator network. After several experiments, we set λ adv and λ semi to 0.1 and 0.001, respectively, similarly to what is present in [START_REF] Hung | Adversarial Learning for Semi-Supervised Semantic Segmentation[END_REF], because we observed no noticeable change in the training. We trained our model over 5000 iterations and started semisupervised learning after 700 iterations to prevent the model from suffering from initial wrong predictions; we then stopped it early to avoid overfitting. Due to the random selection of patients, the shuffling of images, a bad resolution and poor segmentation on some images, we averaged the results of 10 ex-periments to evaluate the model accuracy and robustness. Finally, we implemented the U-Net segmentation model for the LV segmentation proposed in [START_REF] Abdelmaguid | Left Ventricle Segmentation and Volume Estimation on Cardiac MRI using Deep Learning[END_REF] to compare the performance of our model against another segmentation network. U-Net model is composed of 23 layers. We also test our model with the adult cardiac MRI dataset ACDC ( [START_REF] Bernard | Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: Is the problem solved?[END_REF]).

Evaluation metrics

We used the Hausdorff distance and the Dice score to evaluate the performance of our model. The Hausdorff distance (HD) is widely used in evaluating medical image segmentation, and is an indicator of the largest segmentation error. It is a very informative criterion in medical image segmentation [START_REF] Karimi | Reducing the Hausdorff Distance in Medical Image Segmentation with Convolutional Neural Networks[END_REF]. The Hausdorff distance is defined as the maximum distance of a set to the nearest point in another set. Considering ŷ as the prediction and y as the ground-truth, HD is defined by :

HD(y, ŷ) = max y min ŷ ( y -ŷ 2 ) (9)
Where . 2 is the euclidean distance.

The Dice score is used to evaluate the amount of overlap between two regions. It is defined as :

Dice = 2 | y ∩ ŷ | | y | + | ŷ | (10)

Results

Tables 1, 2 and 3 show the results we obtained for the prediction of the endocardium, the epicardium and the myocardium for different models. "DT-GAN" represents the results of our model based on [START_REF] Hung | Adversarial Learning for Semi-Supervised Semantic Segmentation[END_REF] with the loss we defined in 3.3. We evaluated the segmentation performance with different amounts of data used for the supervised learning phase and two different datasets. The first dataset, the York dataset is a pediatric dataset. The second dataset, the ACDC dataset ( [START_REF] Bernard | Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: Is the problem solved?[END_REF]), is an adult dataset we used to validate our method. Finally, we compared the introduction of the distance loss penalty map with the initial model of Hung et al. [START_REF] Hung | Adversarial Learning for Semi-Supervised Semantic Segmentation[END_REF], using a masked cross-entropy loss, and with a U-Net model for LV segmentation for both datasets.

Endocardium segmentation

For the segmentation of the endocardium, figure 3 shows the segmentation for different models. While the U-Net model performs well in terms of the Dice score, the Hausdorff distance score is not as good as in our model. Compared to the U-Net model, ours produces lower Dice score results for the segmentation of the endocardium using different amounts of annotated data. However, our approach outperforms U-Net for the mean Hausdorff distance for the endocardium segmentation. Indeed, we observed that our approach yields better results than the U-Net model, while using only 50% or 25% of the complete training set. We also obtained a better segmentation with our model as compared to the initial model, using a masked cross-entropy loss. Figure 3 shows the predictions (in blue) of different model with different amount of data. We can note in Figures 3b,3h, 3e and 3k that our model outputs a prediction which is closer to the ground truth (in red) than does the original GAN model. The distance map loss penalty term helps our model predict more realistic forms (see Figures 3h to 3k). Furthermore, compared to U-Net predictions in Figures 3f and3l our model predicts shapes at least as good as those predicted by U-Net, irrespective of the amount of data used. Figures 3b,3c, 3d, 3h, 3i and 3j as well as table 1 shows that our approach produces a very good segmentation of the endocardium, even with a small amount of data submitted for training. Finally, regarding ACDC dataset we draw the same conclusions as the York dataset. We obtain similar Hausdorff distances using our method as using U-Net although the Dice scores of DT-GAN are lower than U-Net. The added value of our loss function is noticeable comparing our model (DT-GAN) to a GAN model using only a cross-entropy loss function.

Epicardium segmentation

The segmentation of the epicardium (figure 4 and table 2) shows that mean Dice scores are similar regardless of the model. Differences between each model can be noted in terms of the mean Hausdorff distance. As in the segmentation of the endocardium, our model presents a better mean Hausdorff distance than does the U-Net model, and a GAN using a masked cross-entropy. The similarity in these results is probably due to the fact that the epicardium is the outer wall of the left ventricle, and is larger than the endocardium, and consequently, this part of the left ventricle is easier to segment. As shown in table 2 the U-Net's Hausdorff distance increases drastically as the amount of annotated data decreases, while its Dice score remains stable. Conversely, the GAN models' Hausdorff distance increases only slightly as the amount of data decreases. Specifically, our model's Hausdorff distance showed a marginal decrease relative to other baselines. Figures 4b,4c, 4d, 4h, 4i and 4j show a slight difference between each prediction, irrespective of the amount of annotated data used for training. Generally, prediction results are positive and segmentations generated are visually matched for all models. However, as in the endocardium segmentation, our method produces a segmentation that is very close to the ground truth. On an adult database, our model performs similarly to U-Net regarding Dice scores. We can see on table 2 that when the number of annotated data decrease GAN models perform better comparing to U-Net. Finally, the usage of our loss function enable to our GAN model to produces similar results regardless of the amout of data.

Myocardium segmentation

Table 3 and figure 5 show the Dice scores, the Hausdorff distance and figure for myocardium segmentation. On the York dataset our model reach a Dice score of 0.79±0.10 using 50% of annotated data for training while U-Net and a classic GAN reach respectively 0.74±0.04 and 0.77±0.08. Our model also seems to be less sensitive to the amount of data compared to other models. Indeed on both database, although the Dice scores are better with the U-Net model (except for the York database), we note that with 10% of annotated data for training, our model DT-GAN obtain the best performance (see figures 5d and 5j) whereas U-Net performances collapse. However regarding the Hausdorff distances GANs obtain the best results on the York dataset. On the ACDC dataset U-Net and DT-GAN have similar results but DT-GAN is better with less annotated data.

Discussion

From our experiments, as expected, the results varied depending on the data used for training, which was selected randomly during cross-validation. A total of 5011 images on 10022 images were manually annotated. Among annotated images, a quarter (1/4) had rough manual annotations, where the endocardium was not clearly visible. If these images are included in the training set, the model learns more complex structures, and will allow recognizing more complex anatomical structures in MR images. Conversely, if the training set contains only good quality images with very visible left ventricles, the model will not be able to generalize information. A dataset with a manual image distribution could be created in order to obtain a training set with both good and difficult images to enable an efficient and generalizable training, as illustrated in Fig instance, Figure 4b shows that our model produces a better segmentation of the epicardium as compared to the U-Net model in Figure 4f. However, the segmentation of the epicardium using Figure 4g is better when using a U-Net (figure 4l) model than when ours is used (figure 4h). Objectively Table 2 show that the results are similar using a U-Net model with 100% annotated data and a DT-GAN with 50% annotated data. Second, we notice that our model converges very quickly, as compared to the other models, for both endocardium and epicardium segmentation. However, even though the model converges rapidly in terms of number of iterations, using a GAN model involves a longer training time than using U-Net. This is because the GAN model must train two neural networks in parallel. On an indicative basis, the U-Net model takes a lot of iterations to converge, but its training time is around 15 minutes while our model (in the best case scenario) needs around 40 minutes (1 hour and 20 minutes using the masked cross-entropy).

A semi-supervised framework combined with a GAN model constitutes a very promising approach to produce good LV segmentation, with a limited amount of annotated data. Compara-tively (with the same dataset), our model achieves a Dice score of 0.86 ± 0.15 using only 10% of annotated data and 0.89 ± 0.18 using the whole dataset while Nasr-Esfahani et al. ( [START_REF] Nasr-Esfahani | Left Ventricle Segmentation in Cardiac MR Images Using Fully Convolutional Network[END_REF]) reach a Dice score of 0.8724 using the whole dataset, a fully connected network and a post processing step. One limitation of our method is that it less efficient for myocardium segmentation, unlike other methods using GANs or other types of neural network models ( [START_REF] Khened | Fully Convolutional Multi-Scale Residual DenseNets for Cardiac Segmentation and Automated Cardiac Diagnosis using Ensemble of Classifiers[END_REF], [START_REF] Xu | Mutgan: Simultaneous segmentation and quantification of myocardial infarction without contrast agents via joint adversarial learning[END_REF] and [START_REF] Chartsias | Factorised spatial representation learning: Application in semi-supervised myocardial segmentation[END_REF]). Indeed using our method, we did not obtain results as good as we wanted for myocardium segmentation (see Table 3).

Our approach seems to be similar to the ter myocardium segmentation, we will investigate a weighted cross-entropy loss function based on distance maps for multiclass segmentation in a future study. Finally, this study presents a GAN framework that is very promising for bridging wellestablished medical imaging segmentation models ( [START_REF] Karimi | Reducing the Hausdorff Distance in Medical Image Segmentation with Convolutional Neural Networks[END_REF], [START_REF] Xue | Shape-aware organ segmentation by predicting signed distance maps[END_REF] and [START_REF] Kervadec | Boundary loss for highly unbalanced segmentation[END_REF]) within a learning framework. external/internal forces in the active contour formulation can be modeled by the discriminator and the generator function of GAN. This will be further investigated in future studies.

Conclusion

In this paper, we have presented a new semi-supervised learning approach using GAN. This method promises to segment the endocardium and epicardium of the left ventricle from cardiac MRI. Our new loss function based on the distance transform penalizes wrong predictions during the training phase and helps the segmentation network to produce more accurate segmentations on unannotated images during the unsupervised learning phase. We have shown that the model of Hung et al. ([21]) can be generalized from natural to medical images. Also, we have shown that it is possible to obtain an accurate segmentation of the left ventricle, using a limited amount of annotated data. In fact, the method can generate good predictions of the location and the form of the left ventricle. However, the model is still highly dependent on the image quality distribution. For proper training, it requires a good balance of well-annotated images and of different image qualities in the training set to be able to accurately predict the segmentation on any images. For future work, we aim at integrating additional shape and heart motion constraints in the formulation of our GAN, as well as extending this work to 3D. Also, we will investigate the detection of anatomical defects from the MRI versus a healthy control population, to automatically detect and classify congenital heart defects from the segmentation output.

Acknowledgment

The Titan Xp used for this research was donated by the NVIDIA Corporation.

Figures

1 . 2 .

 12 Normalization: Normalize pixel spacing values to 1mm×1mm. Rescaling: Rescale image size to 256×256. 3. CLAHE: Apply CLAHE equalizer.

3. 4 .Figure 1 :

 41 Figure 1: From left to right : (a) pre-processed image with endocardium contour (blue) and epicardium contour (red), (b) endocardium ground truth and (c) epicardium GT

Figure 2 :

 2 Figure 2: GAN architecture overview. Using a discriminator network trained with loss L D , the segmentation network is optimized using three loss functions during the training : distance map loss penalty term L pm for the segmentation ground-truth, adversarial loss L adv to fool the discriminator and the semi-supervised loss L semi based on the output of the discriminator (a confidence map).

  (a) Original image (b) DT-GAN 50% (c) DT-GAN 25% (d) DT-GAN 10% (e) Original GAN 50% (f) U-Net 100% (g) Original image (h) DT-GAN 50% (i) DT-GAN 25% (j) DT-GAN 10% (k) Original GAN 50% (l) U-Net 100%

Figure 3 :

 3 Figure 3: Prediction (blue) of endocardium with associated ground truth (red)

Figure 4 :

 4 Figure 4: Prediction (blue) of epicardium with associated ground truth (red)

Figure 5 :

 5 Figure 5: Prediction (blue) of myocardium with associated ground truth (red)

Table 1 :

 1 ure 4. The quality of images 4a and 4g is quite different, and we can also see differences in the quality of the segmentation. For Dice score (top) and Hausdorff distance (bottom) for different endocardium segmentation methods with different amount of data used for training the models.

		Endocardium
	Data percentage for training (%)	100%	50%	25%	10%
	U-Net (York)	0.91 ± 0.09 2.19±0.23	0.91±0.12 2.28±0.21	0.91±0.13 2.27±0.42	0.85±0.11 2.34±0.57
	U-Net (ACDC)	0.94 ± 0.06 2.49±0.32	0.93±0.06 2.49±0.32	0.92±0.06 2.50±0.48	0.85±0.28 3.18±0.87
	DT-GAN (York)	0.87±0.11 2.14 ± 0.43	0.88 ± 0.08 2.16 ± 0.42	0.86±0.12 2.24±0.59	0.86±0.15 2.25±0.49
	DT-GAN (ACDC)	0.89±0.16 2.48±0.63	0.88±0.21 2.48±0.66	0.86±0.22 2.47±0.68	0.86±0.21 2.54±0.66
	GAN (York)	0.86±0.11 2.24±0.47	0.87±0.09 2.25±0.43	0.85±0.09 2.23±0.45	0.86±0.09 2.26±0.43
	GAN (ACDC)	0.87±0.19 2.53±0.61	0.87±0.18 2.57±0.69	0.85±0.24 2.58±0.67	0.85±0.21 2.58±0.67
		Epicardium
	Data percentage for training (%)	100%	50%	25%	10%
	U-Net (York)	0.92 ± 0.04 2.25 ± 0.46	0.89 ± 0.09 2.34 ± 0.39	0.91 ± 0.18 2.76 ± 0.23	0.91 ± 0.13 2.71 ± 0.52
	U-Net (ACDC)	0.94 ± 0.08 2.45 ± 0.32	0.92 ± 0.08 2.52 ± 0.23	0.90 ± 0.12 2.74 ± 0.61	0.86 ± 0.15 3.12 ± 0.74
	DT-GAN (York)	0.94 ± 0.07 2.24 ± 0.62	0.93 ± 0.04 2.23 ± 0.35	0.93 ± 0.08 2.29 ± 0.41	0.92 ± 0.05 2.27 ± 0.37
	DT-GAN (ACDC)	0.92±0.16 2.50±0.59	0.92±0.15 2.53±0.62	0.92±0.15 2.60±0.69	0.91±0.17 2.63±0.69
	GAN (York)	0.90 ± 0.06 2.25 ± 0.46	0.91 0.05 2.34 ± 0.35	0.92 ± 0.04 2.37 ± 0.41	0.93 ± 0.04 2.31 ± 0.41
	GAN (ACDC)	0.89 ± 0.2 2.65 ± 0.71	0.88 ± 0.20 2.74 ± 0.60	0.88 ± 0.21 2.74 ± 0.65	0.88 ± 0.19 2.66 ± 0.63

Table 2 :

 2 Dice score (top) and Hausdorff distance (bottom) for different epicardium segmentation methods with different amount of data used for training the models.

Table 3 :

 3 Dice score (top) and Hausdorff distance (bottom) for different myocardium segmentation methods with different amount of data used for training the models.

	Khened et al. approach

for its part, our weight map is based on distances around contours. It helps the network focus on the boundaries of the object to segment, and improves the object's segmentation. We never obtained a Dice score better than 0.81 ± 0.08. For a bet-


PhD Nicolas Vince

PhD Sophie Limou

PhD Michelle Daya

PhD Wataru Morii

MS Nicholas Rafaels

MS Estelle Geffard

MS Venceslas Douillard

PharmD Alexandre Walencik

MS Meher Preethi Boorgula

MS Sameer Chavan

MD, PhD Candelaria Vergara

MD, PhD Victor E Ortega

MD James G Wilson

PhD Leslie A Lange

MD Harold Watson

PhD Dan L Nicolae

PhD Deborah A Meyers

MD, MPH Nadia N Hansel

MD Jean G Ford

MD, PhD Mezbah U Faruque

MD Eugene R Bleecker

MS Monica Campbell

PhD Terri H Beaty

PhD Ingo Ruczinski

ScD Rasika A Mathias

PhD Margaret A Taub

PhD Carole Ober

MD, PhD Emiko Noguchi

PhD Kathleen C Barnes

Dara Caapa

PhD Torgerson

PhD, MPH Pierre-Antoine Gourraud
email: pierre-antoine.gourraud@univ-nantes.fr

Association of HLA-DRB1*09:01 with tIgE levels among African ancestry individuals with asthma

Keywords: Asthma, HLA, tIgE levels, Atopy, CAAPA, Imputation, Admixture. Capsule summary

Background: Asthma is a complex chronic inflammatory disease of the airways. Association studies between HLA and asthma were first reported in the 1970's, and yet, the precise role of HLA alleles in asthma is not fully understood. Numerous genome-wide association studies were recently conducted on asthma, but were always limited to simple genetic markers (SNPs) and not complex HLA gene polymorphisms (alleles/haplotypes), therefore not capturing the biological relevance of this complex locus for asthma pathogenesis.

Objective: To run the first HLA-centric association study with asthma and specific asthmarelated phenotypes in a large cohort of African ancestry individuals.

Methods:

We collected high-density genomics data for the CAAPA participants (Consortium on Asthma among African-ancestry Populations in the Americas, N=4,993). Using computerintensive machine-learning attribute bagging methods to infer HLA alleles, and Easy-HLA to infer HLA 5-gene haplotypes, we conducted a high-throughput HLA-centric association study of asthma susceptibility and total serum IgE levels (tIgE) in subjects with and without asthma.

Results: Among the 1,607 individuals with asthma, 972 had available tIgE levels with a mean tIgE level of 198.7 IU.ml -1 . We could not identify any association with asthma susceptibility.

However, we showed that HLA-DRB1*09:01 was associated with increased tIgE levels (P=8.5x10 -4 , weighted effect size 0.51 [0.15-0.87]).

Conclusions:

We identified for the first time an HLA allele associated with tIgE levels in African ancestry individuals with asthma. Our report emphasizes that by leveraging powerful computational machine-learning methods, specific/extreme phenotypes, and population diversity, we can explore HLA gene polymorphisms in depth and reveal the full extent of complex disease associations.

Introduction

Asthma is a complex chronic inflammatory disease of the airways presenting a large variety of phenotypes which can be divided between Th2-high (e.g. allergic asthma) and non-Th2 (very late-onset) [START_REF] Kontakioti | HLA and asthma phenotypes/endotypes: A review[END_REF][START_REF] Wenzel | Asthma phenotypes: the evolution from clinical to molecular approaches[END_REF] . The Human Leukocyte Antigen (HLA) molecules play a central role in the initiation and regulation of innate and acquired immune responses [START_REF] Hansen | MHC class I antigen presentation: learning from viral evasion strategies[END_REF][START_REF] Jones | MHC class II proteins and disease: a structural perspective[END_REF] , and, as such, have been extensively studied for its potential links with numerous diseases. According to the GWAS catalog, discoveries inside the HLA genomic region account for 22% of all diseases and traits [START_REF] Welter | The NHGRI GWAS Catalog, a curated resource of SNP-trait associations[END_REF] , emphasizing the crucial role played by HLA in a large number of immune-related pathologies [START_REF] Trowsdale | Major histocompatibility complex genomics and human disease[END_REF] . The study of potential associations between HLA and asthma was first reported in the 1970's [START_REF] Brostoff | 80% of patients with intrinsic asthma are homozygous for HLA W6. Is intrinsic asthma a recessive disease?[END_REF][START_REF] Bruce | Studies of HLA antigen frequencies, IgE levels, and specific allergic sensitivities in patients having ragweed hayfever, with and without asthma[END_REF] , and yet, the precise role of HLA alleles in asthma is not fully understood [START_REF] Kontakioti | HLA and asthma phenotypes/endotypes: A review[END_REF] .

Difficulties for replication of associations include variability in asthma etiologies and biological characteristics 2 , small sample sizes [START_REF] Kontakioti | HLA and asthma phenotypes/endotypes: A review[END_REF][START_REF] Yunis | Immunogenetics of atopic asthma: association of DRB1*1101 DQA1*0501 DQB1*0301 haplotype with Dermatophagoides spp.-sensitive asthma in a sample of the Venezuelan population[END_REF] , population heterogeneity, the cost of HLA molecular typing, and the challenge of interpreting associations due to high allelic and structural variations and complex linkage disequilibrium patterns [START_REF] Robinson | The IPD and IMGT/HLA database: allele variant databases[END_REF] . Overall, class II HLA alleles appear to be involved in late-onset and allergic asthma in European and Hispanic populations [START_REF] Kontakioti | HLA and asthma phenotypes/endotypes: A review[END_REF][START_REF] Hirota | Genomewide association study identifies three new susceptibility loci for adult asthma in the Japanese population[END_REF][START_REF] Moffatt | A large-scale, consortium-based genomewide association study of asthma[END_REF][START_REF] Ivković-Jureković | The distribution of HLA alleles among children with atopic asthma in Croatia[END_REF][START_REF] Mc | HLA Dr-Dq haplotypes and the TNFA-308 polymorphism: associations with asthma and allergy[END_REF][START_REF] Torío | HLA class II genotypic frequencies in atopic asthma: association of DRB1*01-DQB1*0501 genotype with Artemisia vulgaris allergic asthma[END_REF] .

With the generalization of genomic studies, an important number of genome-wide association studies (GWAS) were conducted on asthma in the past few years [START_REF] Hirota | Genomewide association study identifies three new susceptibility loci for adult asthma in the Japanese population[END_REF][START_REF] Moffatt | A large-scale, consortium-based genomewide association study of asthma[END_REF][START_REF] Demenais | Multiancestry association study identifies new asthma risk loci that colocalize with immune-cell enhancer marks[END_REF][START_REF] Galanter | Genome-wide association study and admixture mapping identify different asthma-associated loci in Latinos: The Genes-environments & Admixture in Latino Americans study[END_REF][START_REF] Li | Genome-wide association studies of asthma indicate opposite immunopathogenesis direction from autoimmune diseases[END_REF][START_REF] Mathias | A genome-wide association study on African-ancestry populations for asthma[END_REF][START_REF] Noguchi | Genome-Wide Association Study Identifies HLA-DP as a Susceptibility Gene for Pediatric Asthma in Asian Populations[END_REF][START_REF] Pino-Yanes | Genome-wide association study and admixture mapping reveal new loci associated with total IgE levels in Latinos[END_REF][START_REF] Torgerson | Meta-analysis of genome-wide association studies of asthma in ethnically diverse North American populations[END_REF][START_REF] Lasky-Su | HLA-DQ strikes again: Genome-wide association study further confirms HLA-DQ in the diagnosis of asthma among adults[END_REF][START_REF] Ferreira | Shared genetic origin of asthma, hay fever and eczema elucidates allergic disease biology[END_REF][START_REF] Shrine | Moderate-to-severe asthma in individuals of European ancestry: a genome-wide association study[END_REF][START_REF] Kim | Lessons Learned From GWAS of Asthma[END_REF] , and notably reported several associations for SNPs within the MHC (Major Histocompatibility Complex) region that includes the HLA genes. In particular, SNPs in class II HLA genes were associated with asthma in European and Japanese populations [START_REF] Hirota | Genomewide association study identifies three new susceptibility loci for adult asthma in the Japanese population[END_REF][START_REF] Moffatt | A large-scale, consortium-based genomewide association study of asthma[END_REF][START_REF] Noguchi | Genome-Wide Association Study Identifies HLA-DP as a Susceptibility Gene for Pediatric Asthma in Asian Populations[END_REF][START_REF] Pino-Yanes | Genome-wide association study and admixture mapping reveal new loci associated with total IgE levels in Latinos[END_REF][START_REF] Lasky-Su | HLA-DQ strikes again: Genome-wide association study further confirms HLA-DQ in the diagnosis of asthma among adults[END_REF][START_REF] Ferreira | Shared genetic origin of asthma, hay fever and eczema elucidates allergic disease biology[END_REF][START_REF] Shrine | Moderate-to-severe asthma in individuals of European ancestry: a genome-wide association study[END_REF] . Variations in HLA-DQ appear to be the main asthma contributors identified through GWAS, as signals near this gene were found in European American, African American, and Latino populations; but with different SNPs identified in each ethnic group [START_REF] Torgerson | Meta-analysis of genome-wide association studies of asthma in ethnically diverse North American populations[END_REF] . In addition, genetic ancestry at the HLA locus has been associated with both asthma and allergen-specific IgE levels in Latinos [START_REF] Galanter | Genome-wide association study and admixture mapping identify different asthma-associated loci in Latinos: The Genes-environments & Admixture in Latino Americans study[END_REF][START_REF] Pino-Yanes | Genome-wide association study and admixture mapping reveal new loci associated with total IgE levels in Latinos[END_REF] , and other HLA variations also seem to play a role (e.g. HLA-G [START_REF] Nicodemus-Johnson | Maternal asthma and microRNA regulation of soluble HLA-G in the airway[END_REF] and HLA-DRA [START_REF] Li | Genome-wide association studies of asthma indicate opposite immunopathogenesis direction from autoimmune diseases[END_REF] ). However, all these studies only focused on investigations at the SNP level, which do not convey HLA biological complexity and functional relevance. To fully capture the complex information related to antigen presentation and interactions with other immune-related molecules in the context of asthma, it is necessary to examine HLA alleles, defined by HLA gene sequence (www.ebi.ac.uk/ipd/imgt/hla/) [START_REF] Robinson | The IPD and IMGT/HLA database: allele variant databases[END_REF] or SNP haplotypes, and HLA 5-genes haplotypes (HLA-

A~HLA-B~HLA-C~HLA-DQB1~HLA-DRB1).

Due to the high degree of diversity in the HLA region, HLA alleles exhibit relatively low frequencies and only the top 10 most frequent 5-genes haplotypes are in the 1% frequency range [START_REF] Gourraud | HLA diversity in the 1000 genomes dataset[END_REF] ; therefore, a very large sample size or a large effect size is necessary for adequate statistical power to detect an association between an HLA allele and asthma.

However, collecting a very large asthmatic cohort often implies a mix of diverse phenotypes, which may introduce heterogeneity of genetic causes and lead to false negative signals.

Focusing on specific or extreme phenotypes may then contribute to find specific associations [START_REF] Limou | Multiple-Cohort Genetic Association Study Reveals CXCR6 as a New Chemokine Receptor Involved in Long-Term Nonprogression to AIDS[END_REF][START_REF] Limou | Immunogenetics: Genome-Wide Association of Non-Progressive HIV and Viral Load Control: HLA Genes and Beyond[END_REF] , even though the working sample size is reduced.

Here, we leveraged genomic data generated by CAAPA, the Consortium on Asthma among African-ancestry Populations in the Americas, to explore for the first time the role of HLA alleles on asthma and specific asthma-related phenotypes in a large cohort of African ancestry individuals (N = 4,993) [START_REF] Daya | Association study in African-admixed populations across the Americas recapitulates asthma risk loci in non-African populations[END_REF] . CAAPA recently published a GWAS that found strong evidence for association at four previously reported asthma loci (whose discovery was driven largely by non-African populations), and also identified two novel loci that may be specific to asthma risk in African ancestry populations [START_REF] Daya | Association study in African-admixed populations across the Americas recapitulates asthma risk loci in non-African populations[END_REF] . Here, our aims were to deliver the first highthroughput HLA-centric study of asthma outcomes by performing a case/control analysis of asthma susceptibility and a tIgE levels quantitative analysis in subjects with asthma to explore atopy. Our hypothesis is that HLA alleles are associated with asthma phenotypes. We also hypothesize that a classical GWAS design cannot identify these associations as GWAS focus on simple genetics markers (SNPs) that do not fully recapitulate the complexity of the HLA genomic region.

Methods

CAAPA

The CAAPA multi-centre participants were previously described [START_REF] Daya | Association study in African-admixed populations across the Americas recapitulates asthma risk loci in non-African populations[END_REF] . A total of 1,607 asthmatic cases and 3,365 controls of African ancestry (USA and Barbados) were recruited (see table S1, below and in Daya et al. [START_REF] Daya | Association study in African-admixed populations across the Americas recapitulates asthma risk loci in non-African populations[END_REF] for a full description). Briefly, 8 of 17 of the CAAPA investigators contributed data to this study. The distributions of age, sex and age of asthma onset for each cohort are summarized in Table S1. Participants in these studies were unrelated except for BAGS and HUFS which included families; however, we only included the founder's individuals to perform our statistical analyses on unrelated subjects. Childhood onset asthma is defined as having been diagnosed with asthma before 16 years of age.

Studentized residuals of log10 transformed total serum IgE (tIgE, adjusted for age and sex as previously described [START_REF] Levin | A meta-analysis of genome-wide association studies for serum total IgE in diverse study populations[END_REF] ) was available from 4/8 CAAPA studies (see table S1 for mean and standard error of tIgE before transformation and figure S8 for population distribution). We defined atopy as tIgE level > 80 KU/L. Previous work has shown that the genetic structure of the Barbados population is similar to that of African Americans, with subjects from Barbados having on average higher proportions of African ancestry compared to African Americans [START_REF] Mathias | A genome-wide association study on African-ancestry populations for asthma[END_REF][START_REF] Daya | Association study in African-admixed populations across the Americas recapitulates asthma risk loci in non-African populations[END_REF] . Asthma cases were defined by reported and documented histories of current or past physician-diagnosed asthma, while controls reported a negative history of asthma. All participants provided written informed consent. This study is an initiative of CAAPA, which was funded by the NIH (R01 HL104608). NIH guidelines for conducting human genetic research were followed. The Institutional Review Boards (IRB) of Johns Hopkins University (for the GRAAD, BAGS and BRIDGE cohorts), Howard University (for the GRAAD and HUFS cohorts), Wake Forest University (for the SARP cohort), the University of Chicago (for the CAG cohort), University of the West Indies, Cave Hill Campus, Barbados (for the BAGS cohort), and University of Mississippi Medical Center (for the JHS and ARIC cohorts) all reviewed and approved this study.

Genotyping and SNP imputation

Details of genotyping and SNP imputation can be found in the GWAS paper [START_REF] Daya | Association study in African-admixed populations across the Americas recapitulates asthma risk loci in non-African populations[END_REF] .

Briefly, each CAAPA study was separately genotyped on a variety of GWAS chips, as well as genotyped on an African-ancestry specific chip (African Diaspora Power Chip or ADPC) [START_REF] Johnston | Identifying tagging SNPs for African specific genetic variation from the African Diaspora Genome[END_REF] .

All these genomic datasets were imputed using the CAAPA whole-genome sequence reference panel on the Michigan imputation server (https://imputationserver.sph.umich.edu) [START_REF] Mccarthy | A reference panel of 64,976 haplotypes for genotype imputation[END_REF] .

HLA allele SNP-based imputation and haplotyping

The HIBAG R package is designed to impute HLA alleles from SNP genotypes using a reference panel (or bagging set) built with an attribute bagging machine-learning technique [START_REF] Zheng | HIBAG--HLA genotype imputation with attribute bagging[END_REF][START_REF] Pappas | Significant variation between SNP-based HLA imputations in diverse populations: the last mile is the hardest[END_REF] From this large HLA+SNP dataset, we used HIBAG to train statistical models called bags [START_REF] Zheng | HIBAG--HLA genotype imputation with attribute bagging[END_REF] which will serve as a reference for HLA allele imputation from SNP data. With this strategy, we obtained 5 bagging sets corresponding to the HLA-A, HLA-B, HLA-C, HLA-DQB1 and HLA-DRB1 genes (available upon request). The bagging step is computationally very demanding as it required 30,000 CPU-hours on 700 CPUs to build a full reference panel on the 5 HLA genes (table S2). We then imputed HLA alleles in all available CAAPA samples (N = 4,993, table S1), and selected a post-probability threshold ≥ 0.5 as the recommended compromise between good accuracy imputation and call rate from Zheng et al. [START_REF] Zheng | HIBAG--HLA genotype imputation with attribute bagging[END_REF] (table S2 and S3). Choosing a higher post-probability threshold results in marginal accuracy improvements but in a large call rate drop [START_REF] Zheng | HIBAG--HLA genotype imputation with attribute bagging[END_REF] .

The imputed HLA alleles were then uploaded in our in-house web application, Easy-HLA (http://hla.univ-nantes.fr/) [START_REF] Geffard | Easy-HLA, a validated web application suite to reveal the full details of HLA typing[END_REF] , to infer the HLA 5-gene haplotypes (HLA-A~HLA-B~HLA-C~HLA-DQB1~HLA-DRB1) by extending the method of Gourraud et al. [START_REF] Gourraud | Inferred HLA haplotype information for donors from hematopoietic stem cells donor registries[END_REF] . We identified a total of 4,077 unique 5-gene haplotypes, including 2,698 haplotypes with only one occurrence (frequency = 1.1x10 -4 ) up to one haplotype (30:01~42:01~17:01~03:02~04:02) with 149 occurrences (frequency = 0.017). To increase power of detecting an association with HLA haplotypes, we chose to restrict the regression analyses to 27 haplotypes with at least 20 occurrences representing a frequency of 0.2% in the pooled CAAPA individuals.

A complete workflow of the analyses is available as figure S1.

Ancestry estimation

Details of local ancestry inference can be found in the GWAS paper [START_REF] Daya | Association study in African-admixed populations across the Americas recapitulates asthma risk loci in non-African populations[END_REF] . Briefly, after performing SNP phasing using ShapeIT [START_REF] Delaneau | Improved whole-chromosome phasing for disease and population genetic studies[END_REF] , the 4,993 genomes were merged with African and European genomes from the 1000 Genomes project [START_REF] Auton | A global reference for human genetic variation[END_REF] . RFMix 41 inferred 15,824 local ancestry segments across the CAAPA genomes, including 20 segments in the MHC region on chromosome 6. The mean proportion of African ancestry was calculated from the RFMix estimation both genome-wide and in the MHC region. The local ancestry estimate was further used to correct for population differences in statistical analyses.

Statistical analysis

We performed association analyses on HLA alleles with good imputation accuracy (post-probability threshold ≥ 0.5) that were exhibiting an overall (case + controls) frequency ≥ 2% (54 HLA alleles in total, table S4). For each CAAPA study and each HLA allele, we performed a case-control (overall: 1,607 cases vs. S1) and non-asthmatics (N = 816) using a linear regression model.

We accounted for ancestry as described for case-control analyses. Atopy was defined as tIgE level > 80 KU/L and was only explored in asthma cases (atopic individuals = 725, and nonatopic individuals = 247).

We then performed a meta-analysis using METAL [START_REF] Willer | METAL: fast and efficient meta-analysis of genomewide association scans[END_REF] for both case-control and tIgE statistical tests. We used the default sample size-based method from the METAL tool. Z scores in tables S5 and S6 are a weighted sum of Z score across studies; each Z score reflecting the direction of effect and P-value [START_REF] Willer | METAL: fast and efficient meta-analysis of genomewide association scans[END_REF] . We also computed a weighted effect size using the inverse variance based method from the METAL tool for the most significant alleles. We established a stringent Bonferroni threshold of significance at P = 9.3x10 -4 (0.05/54) accounting for the number of alleles tested [START_REF] Vince | HLA class I and KIR genes do not protect against HIV type 1 infection in highly exposed uninfected individuals with hemophilia A[END_REF][START_REF] Dunn | Multiple Comparisons among Means[END_REF] .

Results

Description of the study population

In our study, we had access to a total of 4,993 individuals of African ancestry (USA and Barbados) from 8 CAAPA studies (Table S1). Overall, our patients were 39.3% male and were recruited at 38.7 years of age on average (for a description of the demographics in each cohort, see Table S1). Our analyses only focused on unrelated individuals with a total of 1,607 subjects with asthma and 3,365 controls (BAGS and HUFS cohorts included some family members, these individuals were excluded from our dataset). Finally, tIgE data was available for 972 subjects with asthma and 816 controls. In this subset group, median (IQR) tIgE was 214.0 (69.0-590.7) IU ml -1 for cases and 66.4 (25.0-212.2) IU ml -1 for controls respectively (for a description in each cohort, see Figure S8 and Supplementary Table S1).

The workflow of the analysis is described in figure S1. the genome for CAAPA individuals (figure 1B), as previously described in Mathias et al. [START_REF] Mathias | A continuum of admixture in the Western Hemisphere revealed by the African Diaspora genome[END_REF] .

As expected, the MHC region showed a non-normally distributed admixture among CAAPA individuals with a particular tri-modal distribution (figure 1A, bottom panel) that reflects the diploid origin of a small portion of the genome (1/1000). Indeed, we obtained similar trimodal distribution patterns with similarly sized chromosome 6 segments (5Mb, figure 1C).

Within 2) at the opposite, the green diamond on figure 1B represents an individual with 0% African ancestry within MHC region but 89.9% across whole genome.

HLA alleles imputation

The imputation of HLA alleles is a powerful computational technique to infer HLA alleles only from genotyped SNPs of the MHC using a reference panel [START_REF] Zheng | HIBAG--HLA genotype imputation with attribute bagging[END_REF] . The accuracy of the imputation depends on the reference panel size and matching with the population of interest.

Since the publicly available African ancestry reference panel was small (~150) and did not yield accurate HLA allele imputation, and because the allelic diversity is larger in African ancestry individuals compared to Europeans, we developed our own reference panel from a subset of 917 CAAPA individuals for whom we had genotyped HLA alleles in addition to the MHC SNP genotypes. Our large matching reference panel provided a much-improved quality of imputation (table S3) with an overall call rate of 81% (compared to 42% with the public embedded reference panel). This result emphasizes the need to develop and share large and diverse reference panels to improve HLA imputation accuracy (our panel is available upon request). This innovative approach therefore generated accurate HLA alleles for all CAAPA individuals, including 54 common HLA alleles with a frequency ≥ 2% that were further tested for association with asthma-related phenotypes.

Asthma susceptibility

For each HLA allele, we conducted a logistic regression analysis comparing asthmatics and non-asthmatics while stratifying by 2 African ancestry chromosomes (resulting in 1,178 cases and 2,421 controls).

First, we assessed the association of HLA alleles under an allelic model with asthma susceptibility in each CAAPA cohort using logistic regression. The results obtained in each study (table S1) were subsequently combined by meta-analysis (figure 2). The QQplot showed no inflation of P-values, demonstrating the fitness of our statistical model (lambda 0.93, META in figure S4). No HLA allele passed the Bonferroni threshold of significance (P ≤ 9.3x10 -4 ) and the best association was identified for HLA-B*15:16 (P = 0.04, weighted effect size 0.43 [0.04-0.83], figure 2). In this setting, we expect a statistical power > 90% to detect HLA associations with an allele frequency of 5% and an effect size of 1. did not reveal any significant signals (full results presented in table S5).

We then inferred HLA 5-gene haplotypes (HLA-A~HLA-B~HLA-C~HLA-DQB1~HLA-DRB1) for each CAAPA individual using the local version of an HLA oriented web application (http://hla.univ-nantes.fr/). No statistically significant associations were observed.

Next, we investigated age-at-onset by running a stratified case-control association analysis (N = 804 childhood onset and N = 445 adult onset, respectively) but no significant associations were observed.

Finally, we focused specifically on the HLA alleles that were previously associated with asthma-related phenotypes in European and Asian populations [START_REF] Kontakioti | HLA and asthma phenotypes/endotypes: A review[END_REF][START_REF] Yunis | Immunogenetics of atopic asthma: association of DRB1*1101 DQA1*0501 DQB1*0301 haplotype with Dermatophagoides spp.-sensitive asthma in a sample of the Venezuelan population[END_REF][START_REF] Ivković-Jureković | The distribution of HLA alleles among children with atopic asthma in Croatia[END_REF][START_REF] Mc | HLA Dr-Dq haplotypes and the TNFA-308 polymorphism: associations with asthma and allergy[END_REF][START_REF] Torío | HLA class II genotypic frequencies in atopic asthma: association of DRB1*01-DQB1*0501 genotype with Artemisia vulgaris allergic asthma[END_REF] (Table 1), but did not identify any significant associations.

Total IgE levels in individuals with asthma

We next explored HLA associations with asthma atopy by focusing on the tIgE levels (available in 4/8 CAAPA studies) among subjects with asthma. tIgE levels are higher in asthmatics compared to non-asthmatics (Fig S8 ), and high tIgE levels associate with more severe asthma and lower baseline lung function [START_REF] Naqvi | Association between IgE levels and asthma severity among African American, Mexican, and Puerto Rican patients with asthma[END_REF] . For each HLA allele, we conducted a linear regression analysis in asthmatics using tIgE as a continuous variable while stratifying for 2

African ancestry chromosomes (resulting in 724 individuals). In the meta-analysis, HLA- S6 for full results). This association is specific to the asthmatic population, as no association was found between HLA-DRB1*09:01

(or any other HLA allele) and tIgE in the controls (N = 816). HLA-DRB1*09:01 had a frequency of 3.8% in the overall CAAPA study (Table S4), 3.0% in the tIgE CAAPA subgroup (Table S6), 3.1% in the HLA genotyped CAAPA subset (N = 917), and 3.0% in a reference African American dataset (http://www.allelefrequencies.net, USA NMDP African American) [START_REF] Maiers | High-resolution HLA alleles and haplotypes in the United States population[END_REF] , confirming the quality of our imputation and the confidence in this association signal. To test for possible biases between cohorts with and without tIgE availability, we performed an asthma case/control analysis focusing on the 4 cohorts with tIgE levels data.

The results did not significantly differ from the whole data analysis and there was still no significant signal identified (Table S7). In a sensitivity analysis, we increased the postprobability threshold to test if our signal would hold. The direction and strength of the effect is conserved up to a > 0.7 and > 0.6 threshold, respectively; higher thresholds result in a large call rate drop (71% of HLA-DRB1 alleles reach a post-prob > 0.5 and 30% a post-prob > 0.8; see Table S8).

Finally, the HLA alleles that were previously associated with asthma-related phenotypes in European and Asian populations [START_REF] Kontakioti | HLA and asthma phenotypes/endotypes: A review[END_REF][START_REF] Yunis | Immunogenetics of atopic asthma: association of DRB1*1101 DQA1*0501 DQB1*0301 haplotype with Dermatophagoides spp.-sensitive asthma in a sample of the Venezuelan population[END_REF][START_REF] Ivković-Jureković | The distribution of HLA alleles among children with atopic asthma in Croatia[END_REF][START_REF] Mc | HLA Dr-Dq haplotypes and the TNFA-308 polymorphism: associations with asthma and allergy[END_REF][START_REF] Torío | HLA class II genotypic frequencies in atopic asthma: association of DRB1*01-DQB1*0501 genotype with Artemisia vulgaris allergic asthma[END_REF] were not associated with tIgE levels in our population (Table 1). Using tIgE levels, we also stratified our cohort into atopic (N = 725) and non-atopic (N = 247) individuals. Performing atopic vs. non-atopic analysis did not reveal any significant results, likely due to a lack of statistical power.

Discussion

Our study focused for the first time on HLA allele associations with asthma susceptibility and atopy in a large African ancestry cohort of individuals with asthma (CAAPA, N = 4,993). We identified the HLA-DRB1*09:01 allele associated with elevated tIgE levels in individuals with asthma.

Thus far, most HLA studies in asthma either had small sample sizes or assessed associations only at the SNP level, therefore not fully capturing the complexity and biological relevance of the HLA genes in the MHC region. Moreover, since previous studies focused on populations of European and Asian descent, studying a large population of African ancestry is of great interest as it can shed new light on the relationship between HLA and asthma. In the recently published CAAPA GWAS (of the same data) [START_REF] Daya | Association study in African-admixed populations across the Americas recapitulates asthma risk loci in non-African populations[END_REF] , Daya et al. did not identified any significant associations for SNPs inside the HLA region (best SNP: rs9272346, P = 0.03). This reinforced our strategy to study HLA alleles. To investigate the specific role of HLA in asthma, we used a powerful machine-learning based computational technique to infer HLA alleles. We developed a new reference panel matching individuals of African ancestry in order to increase imputation accuracy in this (genetically diverse) population. We inferred HLA alleles with at least 86% accuracy for the HLA-A, HLA-B, HLA-C, HLA-DQB1 and HLA-DRB1 genes in all CAAPA studies (table S2).

As the African American and Barbados African Caribbean genomes are a mosaic of European and African ancestral genomes, we first explored the specific admixture structure within the MHC genomic region. As HLA genes are under strong selection pressure [START_REF] Meyer | A genomic perspective on HLA evolution[END_REF] , one could have expected differences between the MHC region and the rest of the genome. Our analyses revealed a similar proportion of admixture as in the rest of the genome (around 20% of European ancestry), and an admixture pattern that is comparable to similarly sized segments on chromosome 6 (figure 1C). This indicates that the MHC does not seem to be under ancestry-specific selective pressure compared to the rest of the genome and that we can analyze this genomic region as any other part of the genome. Yet, we must acknowledge the fairly recent history of admixture in African Americans and African Caribbean, having occurred only in the last few centuries, which does not exclude the possibility of detecting ancestry-specific selection in the future, after many more generations. In our analyses to account for admixture, we applied 2 strategies: 1) stratification on local ancestry only from African origin and 2) local ancestry as covariate in the regressions (see methods).

Our case-control analyses (1,607 vs. 3,365) did not reveal any significant HLA associations with asthma susceptibility. Focusing on tIgE levels to study atopy in individuals with asthma, we restricted our sample size to only 972 asthmatics from 4 studies (724 after African local ancestry stratification) and found HLA-DRB1*09:01 associated with an increased level of tIgE in blood (P = 8.5x10 -4 ). This P-value reaches our Bonferroni threshold (9.3x10 -4 ) of significance calculated by dividing 0.05 by the number of tested alleles (N = 54).

This association was not found in control individuals, indicating the specificity of the signal for atopy in individuals with asthma. These results illustrate how an extreme phenotype (high tIgE levels associated with increased asthma severity [START_REF] Naqvi | Association between IgE levels and asthma severity among African American, Mexican, and Puerto Rican patients with asthma[END_REF] ) such as atopy can maximize statistical power of detection by limiting the noise due to the diversity of asthmatic phenotypes.

We have to acknowledge the difficulty of finding additional replication cohorts, with CAAPA standing as an exceptionally large dataset. The HLA-DRB1*09:01 allele is relatively rare in African and European populations (1-3%) but frequent in East Asian populations (15-25%) [START_REF] Gourraud | HLA diversity in the 1000 genomes dataset[END_REF] . In a validating cohort of 544 Japanese subjects including 103 asthmatics with tIgE 51 , we did not identify any significant association (tIgE association with HLA-DRB1*09:01, P = 0.49, frequency = 11.8%). This is probably due to lack of power, but also could be explained by genetic background difference between African ancestry and Asian ancestry individuals or dissimilar environmental effects between the Japanese islands and the Americas.

Previous reports showed HLA-DRB1*09:01 associated with several autoimmune diseases (rheumatoid arthritis, systemic lupus erythematosus [START_REF] Shimane | An association analysis of HLA-DRB1 with systemic lupus erythematosus and rheumatoid arthritis in a Japanese population: effects of *09:01 allele on disease phenotypes[END_REF] , type 1 diabetes -especially late-onset-53 and dermatomyositis [START_REF] Lin | Genetic association of HLA-DRB1 multiple polymorphisms with dermatomyositis in Chinese population[END_REF] ), especially in Asian populations where the allele is more frequent. This suggests that HLA-DRB1*09:01 carriers are at increased risk for selfpeptide presentation and/or exacerbated chronic inflammation. This is in line with the pleiotropic effects observed in autoimmune diseases where some factors, especially genetics, share pathophysiological mechanisms across a span of autoimmune diseases [START_REF] Anaya | Common mechanisms of autoimmune diseases (the autoimmune tautology)[END_REF] .

Interestingly, HLA-DRB1*09:01 was identified as a susceptibility allele to food allergy (peach allergy) [START_REF] Khor | Genome-wide association study of self-reported food reactions in Japanese identifies shrimp and peach specific loci in the HLA-DR/DQ gene region[END_REF] . This correlates with our findings of increased tIgE levels for HLA-DRB1*09:01 carriers. Finally, in the immune epitope database (iedb.org) [START_REF] Vita | The immune epitope database (IEDB) 3.0[END_REF] , CD4+ T cell epitopes against common environmental allergens were identified using Tetramer Guided Epitope Mapping. Among 86 epitopes, 4 Derp2 overlapping epitopes showed evidence of specific binding with HLA-DRB1*09:01 tetramers (direct submission to the immune epitope database, reference ID: 1024966) [START_REF] Vita | The immune epitope database (IEDB) 3.0[END_REF][START_REF] Wambre | Characterization of CD4+ T cell subsets in allergy[END_REF] . Derp2 is a well-known allergen from house dust mite and a strong inducer of allergic asthma in susceptible individuals, which can lead to the immune system activation and elevation of tIgE level [START_REF] Wang | Derp2-mutant gene vaccine inhibits airway inflammation and up-regulates Toll-like receptor 9 in an allergic asthmatic mouse model[END_REF][START_REF] Genc | The decreased CD4+CD25+ FoxP3+ T cells in nonstimulated allergic rhinitis patients sensitized to house dust mites[END_REF] . In the context of HLA-DRB1*09:01, this represents another lead in the possible mechanism of action of this allele in asthma atopy. The allele could be responsible of an increased activation of CD4+ T cells compared to other alleles, by presentation of specific dust mite peptides, which would trigger an amplified inflammation environment and result in an increased production of IgE by B cells. Further experiments are needed to confirm the association and define the functional relationship between HLA-DRB1*09:01 allele and tIgE.

We have to acknowledge some limitations in our study. Even if this is the largest ever African ancestry oriented study of HLA alleles association with asthma, this study remains restricted to a relatively small sample size, especially for samples with tIgE levels available (N = 972 individuals with asthma). However, this study conveys the ambitious will of gathering data from several research groups across multiple cities and countries which appears to be the future of genetics studies. The large diversity of cohorts is also a limitation both in terms of clinical definitions (different clinicians, definitions evolve through time) and in genetics and environmental backgrounds. These effects were limited here by using a metaanalysis technique and corrections from genetically-computed ancestry. The main difficulty remains to find an appropriate replication cohort when the goal of CAAPA was already to assemble as many cohorts as possible.

In conclusion, we identified for the first time HLA-DRB1*09:01 allele association with tIgE levels in African ancestry individuals with asthma, potentially through a mechanism involving specific peptide presentation and/or increasing inflammation. weighted effect size 0.

  Abbreviations: ADPC: African Diaspora Power Chip CAAPA: Consortium on Asthma among African-ancestry Populations in the Americas CPU: Central Processing Unit GWAS: Genome-Wide Association Study HLA: Human Leukocyte Antigen IRB: Institutional Review Boards IQR: Inter Quartile Range NMDP: National Marrow Donor Program MHC: Major Histocompatibility Complex QQplot: Quantile Quantile plot SNP: Single Nucleotide Polymorphism tIgE: total serum Immunoglobulin E levels WGS: whole-genome sequencing

  Admixture in the MHC regionDue to the recent history of admixture between African and European populations, individuals living in the Americas with African ancestry show a high proportion of European ancestry (on average 20% of European ancestry across the genome for African Americans and African Caribbean)[START_REF] Mathias | A continuum of admixture in the Western Hemisphere revealed by the African Diaspora genome[END_REF] . The specific admixture structure within the MHC genomic region has not been studied in detail before. To understand the local ancestry structure in the MHC and the sensitivity of association results to differences in local ancestry, we inferred the local ancestry for the unrelated CAAPA individuals (full dataset, N = 4,993) both across the whole genome and specifically within the MHC region, and compared their admixture structure (figure1A). The whole genome admixture observed in CAAPA was typical of African ancestry individuals with a history of European admixture (figure 1A, top panel), and represents 81.9% on average with a median of 83.9% [inter-quartile range 77.3-89.0] of African ancestry across

  5. Running the analysis by correcting for local ancestry (Fig S6) or under a dominant model (Fig S5 and S7)

DRB1* 09 :

 09 01 was significantly associated with elevated tIgE levels under an allelic model (P = 8.5x10 -4 , weighted effect size 0.51 [0.15-0.87], moderate heterogeneity across studies P = 0.04, figure 2 and 3, Fig S9), indicating that this allele is associated with degree of atopy in individuals with asthma. Association effects were in the same direction across all cohorts and HLA-DRB1*09:01 reached nominal significance in two cohorts (P = 0.02 in CAG and P = 0.02 in SARP). We obtained similar results by correcting for local ancestry (Fig S11) or using a dominant model (Fig S10 and S12, see table

Figure 1 :

 1 Figure 1: Ancestry proportion are equivalent between whole genome and MHC genomic region. (A) CAAPA individual's ancestry proportion (full dataset, N = 4,993). X axis: each individual. Y axis: ancestry proportion. Black horizontal line: 80% African ancestry. The MHC only represents a thousandth of the genome (5Mb on chromosome 6). Mean local ancestry: light gray for European, dark gray for African. (B) Violin plot comparing African ancestry proportion. Median: white dots. Interquartile range: black boxes. Black lines: 95% confidence interval. (C) Density plot comparing African ancestry proportion between whole genome (red), MHC region (blue) and similarly-sized chromosome 6 segments (gray, N = 42 segments). (D) The graph combines 1) a scatter plot comparing African ancestry proportion across all CAAPA individuals and 2) a density plot showing the proportion of individuals with a specific level of African ancestry in whole genome (right) and MHC genomic region (top). The blue line was drawn with the correlation coefficient. Pearson correlation: P = 4.8x10 -180 , R = 0.39 [95% confidence interval 0.37-0.41]. Two extreme individuals are represented: red triangle (100% African ancestry MHC, 41.5% whole genome), green diamond (0% African ancestry MHC, 89.9% whole genome).

Figure 2 :

 2 Figure 2: HLA-DRB1*09:01 allele associates with asthma atopy. Manhattan plot of each HLA allele association for case-control analysis (blue) and tIgE analysis (red). P-values are meta-analysis from regression analyses stratified for African ancestry chromosomes and represented as -log10 of P-values. Red horizontal line represents Bonferroni threshold of significance (P = 9.3x10 -4 ). Black horizontal line represents nominal threshold of significance (P = 0.05). Each HLA allele tested are represented.

Figure 3 :

 3 Figure 3: HLA-DRB1*09:01 allele associates with higher tIgE. (A) Box plot comparing tIgE level between Asthmatics with 1 or 2 HLA-DRB1*09:01 allele (+/-and +/+) and Asthmatics without HLA-DRB1*09:01 allele (-/-). tIgE level is expressed in z-score. The N representing the number of individuals in each group is depicted on the graph, these numbers exclude HLA-DRB1 alleles with a post-probability < 0.5 and individuals with 2 chromosomes from African ancestry (Figure S2). (B) Forest plot representing the association between HLA-DRB1*09:01 and tIgE in the different CAAPA cohorts where tIgE is available.

  . Appropriate reference panels matching the population of interest are crucial to obtain

	accurate	imputation.	Several	reference	panels	are	available	publicly
	(http://www.biostat.washington.edu/~bsweir/HIBAG/) but are mostly suitable for European
	ancestry imputation. Due to the complexity and diversity of African ancestry genomes, large
	reference panels are necessary and are yet to be collected; the currently available panel being
	derived from only ~150 African-ancestry individuals. To maximize imputation quality, we
	created our own African ancestry reference panel from a subset of 917 CAAPA individuals

from whom we had high-resolution genotyped HLA alleles (second field or four-digit), and MHC SNP genotypes (accessed from dbGAP, phs001123.v1.p1). The HLA alleles were called with the Omixon software (Budapest, Hungary) from whole-genome sequencing (WGS) data, and we selected 29,970 MHC SNPs overlapping between the WGS data and the GWAS data.

  of European and African MHC fragments emphasizing the recombination events in that locus (21.0% of the population, figure 1A, bottom panel). Importantly, similar distributions were observed across the individual CAAPA studies (Fig S3), indicating their homogeneity regarding admixture structure. While the distribution patterns looked different, the whole genome African ancestry proportion correlated significantly with the MHC region African ancestry proportion (figure 1D; Pearson correlation: R = 0.39 [95% confidence interval 0.37-0.41], P = 4.8x10 -180 ). This result indicates that the MHC admixture distribution correlates to the whole-genome distribution: individuals above whole-genome African ancestry proportion median (83.9%)are carrying a homozygous MHC region segment of African origin for 77.6% of them compared to 50.8% for individuals below median (fold increase of 1.5). However, it is important to emphasize that this very significant correlation does not preclude specific extreme cases such as the following two cases: 1) the red triangle on figure 1D represents an individual with 100% African ancestry within MHC region but 41.5% across whole genome;

the MHC, we observed European/European ancestry (100% light gray, 2.3% of the population), African/African ancestry (100% dark gray, 64.1% of the population), European/African ancestry (50/50, 12.6% of the population), and admixed individuals carrying a mosaic

Table 1 :

 1 51 [0.15-0.87]. BAGS effect size 0.07 [-0.41-0.56]. CAG effect size 1.48 [0.31-2.64]. GRAAD effect size 0.80 [0.00-1.60]. SARP effect size 1.23 [0.24-2.21]. P-values of previously found asthma associated HLA alleles tested in our study. P-values are from the meta-analysis on case-control and tIgE levels in CAAPA individuals with asthma using an allelic model and stratifying for 2 African ancestry chromosomes.

	HLA	Case vs. Control	tIgE	level	Population	Pubmed ID
		meta-analysis	P-	meta-analysis

Acknowledgments

The authors thank Labex IGO (ANR-11-LABX-0016-01) and IHU-CESTI for their support. Nicolas Vince has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 846520. We gratefully acknowledge the contributions of Pissamai and Trevor Maul, Paul Levett, Anselm Hennis, P. Michele Lashley, Raana Naidu, Malcolm Howitt, and Timothy Roach (BAGS), Audrey Grant, Eduardo Viera Ponte, Alvaro A. Cruz, and Edgar Carvalho (BIAS), Susan Balcer-Whaley, Maria Stockton-Porter, and Mao Yang (GRAAD), Delmy-Aracely Mejía-Mejía, Mario Meraz, Jaime Nuñez, Eileen Fabiani Herrera Mejía (HONDAS), Trevor Ferguson, Deanna Ashley (JAAS), Silvia Jimenez, Nathalie Acevedo, Dilia Mercado (PGCA), Ann Jedlicka (REACH), Addison K. May, Caroline Gilmore, Patricia Minton (Vanderbilt University), Qun Niu (University of Chicago), Adeyinka Falusi, Abayomi Odetunde (University of Ibadan, Nigeria). The authors also acknowledge the support of John Jay Shannon (Cook County Health Systems) and Kevin Weiss (Northwestern University), Regina Miranda and the Indians Zenues guards (San Basilio de Palenque, Bolivar, Colombia), Ulysse Ateba Ngoa (Leiden University), and Charles Rotimi, Adeyemo Adebowale, Floyd J Malveaux, and Elena Reece (Howard University). We thank the numerous health care providers and community clinics and co-investigators who assisted in the phenotyping and collection of DNA samples, and the families and patients for generously donating DNA samples to BAGS, BIAS, BREATHE, CAG, GRAAD, HONDAS, Jackson Heart Study, REACH, VALID, SARP, COPDGene, JAAS, PGCA, AEGS, and the asthma studies in Gabon and Palenque, Colombia. Special thanks to community leaders, teachers, doctors and personnel from health centers at the Garifuna communities for organizing the medical brigades and to the medical students at Universidad Católica de Honduras, Campus San Pedro y San Pablo for their participation in the fieldwork related to HONDAS; study coordinator Sandra Salazar; and health liaisons and public health officers of the main Conde office, Adaliudes Conceição, Luciana Quintela, Ivanice Santos, Analú Lima, Benivaldo Valber Oliveira Silva, and Iraci Santos Araujo, and students from the Federal University of Bahia who assisted in data collection in BIAS: Rafael Santana, Roberta Barbosa, Ana Paula Santana, Charlton Barros, Marcele Brandão, Ludmila Almeida, Thiago Cardoso and Daniela

Costa. We are grateful for the support from the international state governments and universities from Honduras, Colombia, Brazil, Gabon, Nigeria, Netherlands, Jamaica, 20 Barbados and the United States who made this work possible. Funding for this study was provided in part by NIH grants R01-HL129239 (KCB) and R01HL104608 (KCB).