Thomas Gilles

Stefano Sabatini

Dzmitry Tsishkou

Bogdan Stanciulescu

Fabien Moutarde

GOHOME: Graph-Oriented Heatmap Output for future Motion Estimation

In this paper, we propose GOHOME, a method leveraging graph representations of the High Definition Map and sparse projections to generate a heatmap output representing the future position probability distribution for a given agent in a traffic scene. This heatmap output yields an unconstrained 2D grid representation of agent future possible locations, allowing inherent multimodality and a measure of the uncertainty of the prediction. Our graph-oriented model avoids the high computation burden of representing the surrounding context as squared images and processing it with classical CNNs, but focuses instead only on the most probable lanes where the agent could end up in the immediate future. GOHOME reaches 2nd on Argoverse Motion Forecasting Benchmark on the MissRate6 metric while achieving significant speed-up and memory burden diminution compared to Argoverse 1 st place method HOME. We also highlight that heatmap output enables multimodal ensembling and improve 1 st place MissRate6 by more than 15% with our best ensemble on Argoverse. Finally, we evaluate and reach state-of-the-art performance on the other trajectory prediction datasets nuScenes and Interaction, demonstrating the generalizability of our method.

I. INTRODUCTION

Trajectory prediction inherently faces many uncertainties. These uncertainties can be split in two categories: aleatoric and epistemic. Aleatoric uncertainty is the natural randomness of a process: it is the consequence of control noise and will lead to variations in acceleration, curvature, etc ... It translates into a spread of the possible future position and is often tackled by the use of Gaussian predictions in motion estimation [START_REF] Deo | Multi-modal trajectory prediction of surrounding vehicles with maneuver based lstms[END_REF], [START_REF] Mercat | Multi-head attention for multi-modal joint vehicle motion forecasting[END_REF]. Epistemic uncertainty outlines some knowledge that can't be known by the observer at prediction time: what is the car destination, will it choose to overtake the car by the left or stay behind ? Recent methods use multimodal outputs based on anchors [START_REF] Chai | Multipath: Multiple probabilistic anchor trajectory hypotheses for behavior prediction[END_REF], [START_REF] Phan-Minh | Covernet: Multimodal behavior prediction using trajectory sets[END_REF], predefined learning heads [START_REF] Cui | Multimodal trajectory predictions for autonomous driving using deep convolutional networks[END_REF] or the available HD-Map [START_REF] Zhao | Tnt: Target-driven trajectory prediction[END_REF]- [START_REF] Kim | Lapred: Laneaware prediction of multi-modal future trajectories of dynamic agents[END_REF] to cover the span of these possible manoeuvres.

However, existing methods trying to deal with the aforementioned uncertainties in trajectory prediction have limitations. Gaussian predictions are constrained to a 2D predefined shape, that cannot adapt easily to the specific road context, for example at high speed on a curvy road the distribution of the future agent position should resemble the center lane curvature. Regressed sets of coordinates may encounter mode collapse and converge to the same solution, as they are trained with only one ground truth per sample. On the other hand, anchor-based and map-based predictions are restricted to a predefined set of possibilities, depending on preprocessed trajectory clustering or a fixed sampling of the centerlines.

Motion forecasting can also be tackled through the use of a heatmap output representing the final trajectory point location distribution [START_REF] Gilles | Home: Heatmap output for future motion estimation[END_REF], [START_REF] Mangalam | From goals, waypoints & paths to long term human trajectory forecasting[END_REF]. The intermediary waypoints can then be reconstructed from the history and end point. This brings many advantages for uncertainty modelization and multimodal prediction. First of all it does not restrict the representation of the prediction uncertainty to a parametric form (like a gaussian). Moreover, it conveys a richer information regarding the future probability distribution compared to a predefined number of predicted trajectories, enabling predictions with a much better coverage . This is usually achieved rasterizing an image to represent the context around the car, and processing it through an encoder-decoder CNN. However, the distance a car can travel in a given time horizon can exceed this image boundary, and extending the reach results in quadratic complexity increase. Moreover, convolutional networks commonly used for the task of generating images have to operate on the full square image while the actual road and drivable area take a much sparser space.

HOME [START_REF] Gilles | Home: Heatmap output for future motion estimation[END_REF] introduces the use of a probability heatmap as model output for car trajectory prediction, but uses a fullyconvolutional model and is limited to a restricted image size . We iterate on this work and present an optimized motion forecasting framework solely based on graph operations to provide efficiently an heatmap with uncertainty measure exploiting the vectorized form of HD map. We also highlight that heatmap output is suitable for model ensembling without any risk of mode collapse and bring significant improvement to the state-of-the-art using this ensembling. Our GOHOME pipeline is illustrated in Fig. 1.

II. RELATED WORK

The sequential nature of temporal trajectories makes them a straightforward application of recurrent neural networks [START_REF] Mercat | Multi-head attention for multi-modal joint vehicle motion forecasting[END_REF], [START_REF] Altché | An lstm network for highway trajectory prediction[END_REF]. However, the need for local map and context information leads them to be often combined with Convolution Neural Networks (CNNs) applied on top-view images [START_REF] Cui | Multimodal trajectory predictions for autonomous driving using deep convolutional networks[END_REF], [START_REF] Lee | Desire: Distant future prediction in dynamic scenes with interacting agents[END_REF]- [START_REF] Liu | Multimodal motion prediction with stacked transformers[END_REF].

Lately, Graph Neural Networks (GNNs) have been increasingly applied in order to process compact map encodings with a deepened connectivity understanding. VectorNet [START_REF] Gao | Vectornet: Encoding hd maps and agent dynamics from vectorized representation[END_REF] treats indifferently trajectory and map lanes as sets of points (polylines), and encodes them into a global interaction graph. LaneGCN [START_REF] Liang | Learning lane graph representations for motion forecasting[END_REF] uses graph convolutions onto the connected lane graph before fusing this lane information with actor information.

While most previous works tackle multimodality through learned regression heads, recent work brought different output representations in order to avoid mode collapse and sample inefficiency. CoverNet [START_REF] Phan-Minh | Covernet: Multimodal behavior prediction using trajectory sets[END_REF] and MultiPath [START_REF] Chai | Multipath: Multiple probabilistic anchor trajectory hypotheses for behavior prediction[END_REF] use anchor trajectory priors to have identified modes without risk of averaging them. PRIME [START_REF] Song | Learning to predict vehicle trajectories with modelbased planning[END_REF] generates model-based trajectories and then ranks them with a learned model. TNT [START_REF] Zhao | Tnt: Target-driven trajectory prediction[END_REF] samples target candidates along lane priors and scores them using a VectorNet backbone, while LaneRCNN [START_REF] Zeng | Lanercnn: Distributed representations for graph-centric motion forecasting[END_REF] generates a lane graph for each actor and uses the lane nodes as a classification output for future position. GoalNet [START_REF] Zhang | Map-adaptive goal-based trajectory prediction[END_REF] identifies possible long-term goals proposals with the map, and runs a GNN where the features for each possible goal are a path-relative raster.

Generative methods can also be used to obtain multimodal predictions, through either Variational Auto Encoders [START_REF] Lee | Desire: Distant future prediction in dynamic scenes with interacting agents[END_REF], [START_REF] Tang | Multiple futures prediction[END_REF], [START_REF] Rhinehart | R2p2: A reparameterized pushforward policy for diverse, precise generative path forecasting[END_REF], [START_REF] Mangalam | It is not the journey but the destination: Endpoint conditioned trajectory prediction[END_REF] or Generative Adversarial Networks [START_REF] Alahi | Social lstm: Human trajectory prediction in crowded spaces[END_REF], [START_REF] Sadeghian | Sophie: An attentive gan for predicting paths compliant to social and physical constraints[END_REF], but they require multiple forward passes for each prediction, do not guarantee diversity in the samples obtained and their inherent randomness in not advisable in production systems..

Other methods focus on a heatmap output to represent the future distribution, as it possesses natural multimodality and is therefore not subject to mode collapse. Mangalam et al. [START_REF] Mangalam | From goals, waypoints & paths to long term human trajectory forecasting[END_REF] models both long-term goals and intermediary waypoints in the two-dimensional space as an image for pedestrian trajectory forecasting, combined with random sampling and Kmeans clustering. HOME [START_REF] Gilles | Home: Heatmap output for future motion estimation[END_REF] predicts a future probability heatmap for car trajectories, and devises deterministic sampling algorithms for various metric optimization. However, most of these heatmap-based methods use a full CNN architecture. To our knowledge, we are the first work to combine a GNN architecture and a heatmap output without the use of any CNNs.

III. METHOD

The goal is to output a heatmap that represents the position of an agent at a given time in the future. The trajectory is then regressed conditioned to the final end point. To achieve this, our GOHOME system focuses on lane-level operations as illustrated in Fig. 1. The local HD-Map is provided in the dataset as a graph of L lanelets. A lanelet represents a macro section of the road (10 to 20 meters on average), as our goal is to encode connectivity at a macro level (lane segments), and not micro level (every meter). Each lanelet is defined as a sequence of centerline points, and is connected to its predecessor, successor, left and right neighbors if they exist. We encode each lanelet into a road graph, where geometric and connectivity information are represented. Our model yields a score for each of these lanelets, that is used to identify most probables lanes. A partial heatmap is then generated for the top ranking lanelets, and projected onto a global heatmap. Afterwards, we sample a set of endpoints from the heatmap and recreate a trajectory for each. The model architecture is illustrated in Fig. 2. We encode each lanelet through a shared 1D convolution and UGRU [START_REF] Erdem | 6th place solution: Very custom gru[END_REF], [START_REF] Rozenberg | Asymmetrical bi-rnn for pedestrian trajectory encoding[END_REF] recurrent LaneEncoder into features F of C channels. The lanelet features F are then updated through a GraphEncoder1 made of a sequence of four graph convolution operations similar to [START_REF] Liang | Learning lane graph representations for motion forecasting[END_REF] in order to spread connectivity information:

F ← F W + r A r F W r ( 1
)
where F is the (L, C) lane feature matrix , W is the learned (C, C) weight for ego features encoding. A r and W r are the respective adjacency matrix (L, L) and learned weight (C, C) for the relation r ∈ {predecessor, successor, lef t, right} derived from the lane graph. A r is fixed as it comes from the HD Map, while W r enables to learn different operations for each possible relation.

Parallely, each agent trajectory, defined as a sequence of position, speed and yaw, is encoded with a shared T rajEncoder also made of a shared 1D convolution and a UGRU layer. Each agent feature is then updated with map information through a cross-attention Lanes2Agents layer on the lanelet features. Interactions between agents are then taken into account through a self-attention Agents2Agents layer between agents. Finally the target agent feature is concatenated to all the lanelet features by Ego2Agents and then treated through a final GraphEncoder2 layer, also made of 4 graph convolutions to obtain the final graph encoding that will be used to generate the different predictions.

Compared to other methods using graph neural networks, our method uses graph convolutions like LaneGCN [START_REF] Liang | Learning lane graph representations for motion forecasting[END_REF] and LaneRCNN [START_REF] Zeng | Lanercnn: Distributed representations for graph-centric motion forecasting[END_REF], but applies them to lanelets instead of lane nodes (a lane node is a single point in the sequence of a lanelet). VectorNet [START_REF] Gao | Vectornet: Encoding hd maps and agent dynamics from vectorized representation[END_REF] and TNT [START_REF] Zhao | Tnt: Target-driven trajectory prediction[END_REF] also use lanelets, called polylines, but connect them through global attention instead of using graph connectivity. We chose to use a GNN on the lanelets since we wanted an efficient and high-level representation allowing to spread information easily through the graph, while still leveraging connectivity.

B. Heatmap generation through Lane-level rasters

For the heatmap output, we wish to have a dense image in cartesian coordinates of dimensions (H, W ). To do so without using any convolution on the full image, we create a raster for every lanelet in curvilinear coordinates. We use lane ranking to generate these lane rasters only for the top k lanelets and not all of them. a) Lane raster generation: Each of the small lane rasters of size (h, w) has a longitudinal lenght of 20m and a transversal width of 4m. These lane rasters are created as a discretization of the Frenet-Serret referential along the lane , as illustrated in Fig. 3. We decompose the probability distribution along a lanelet in a longitudinal component (h, 1, 8) and a lateral component (1, w, 8) predicted from the lanelet encoding. These components are summed together with broadcast to create a (h, w, 8) R f eatures volume. This way the complexity to create the volume is (h+w)×8 instead of h × w × 8. The obtained volume is then concatenated with pixelwise cartesian coordinates, heading, lane occupancy and curvature informations before a final linear layer on which is applied a sigmoid to get the R proba output. The resulting lane-level R proba heatmaps are then projected onto the full cartesian heatmap Ŷ using each pixel cartesian coordinates as illustrated in Figure 3. If multiple lane-level pixels fall into the same cartesian pixel, their values are averaged. The lane raster widths are set such that adjacent lane rasters overlap and can cover lane change behaviors. The target Y for this final prediction Ŷ is a Gaussian centered around the target agent ground truth position. We use the same pixel-wise focal loss as [START_REF] Gilles | Home: Heatmap output for future motion estimation[END_REF] inspired from CenterNet [START_REF] Zhou | Objects as points[END_REF]:

L = - 1 P p (Y p -Ŷp ) 2 f (Y p , Ŷp ) with f (Y p , Ŷp ) = log( Ŷp ) if Y p =1 (1 -Y p ) 4 log(1 -Ŷp ) otherwise (2)
b) Lane ranking: The lanelet classification is obtained with a linear layer on the graph encoding, followed by a sigmoid activation. The ground-truth is defined by a 1 for all lanelets where the future car position is inside the lanelet polygon and 0 otherwise. The loss is a binary cross-entropy added to the pixel-wise loss of Eq. 2 with a 1e -2 coefficient. Since only a fraction of the lanelets will actually be useful to represent the future car location, we can compute the lanelevel rasters only for a subselection of lanelets, saving more computation. We use the classification score c lane predicted by the network to select only the top k ranking lanelets, and only compute and project the lane raster for these. Since the raster predictions for the other unselected lanelets would have been very close to zero anyway, this does not decrease performance at all, as demonstrated in Sec. IV-C.2. c) Cartesian image connection: Some lane rasters may be projected onto the same pixels and overlap, which is very difficult for the model to know of beforehand. To help the model know of overlaps and propagate location information through the lane rasters, we do a first projection of the lane rasters onto the cartesian coordinates before the final probability estimation. The (h, w, 8) lane raster features R f eatures are projected onto a (H, W, 8) cartesian image I f eatures through the same operation previously described for the probability heatmaps, with the overlaps averaged. The occupancy (number of raster pixels aggregated in each cartesian pixel) information is also concatenated to the volume I f eatures . A linear layer is then applied on the last dimension of I f eatures , which is then reprojected onto curvilinear coordinates and concatenated to the initial lane rasters R f eatures before final probability estimation and projection. This way, the features of overlapping lanes are shared between them so that they can propagate information and homogenise probability.

C. Sparse sampling for Miss Rate Optimization and Full trajectory generation

To derive final trajectory points from the heatmap, we use the same MissRate optimization sampling algorithm as HOME [START_REF] Gilles | Home: Heatmap output for future motion estimation[END_REF]: we iteratively select the grid point that maximizes surrounding probability in a local neighborhood of radius r, then set this local neighborhood probabilities to zero so the next iteration doesn't select the same location. The radius r is a simple hyper-parameter that can be tuned according to the spread of uncertainty present in the dataset, as we will demonstrate in Sec. IV-B.

Full trajectories are then inferred from the sampled end points with a simple fully connected model of 2 hidden layers with 64 features each, taking the history and end point as inputs and trained with the ground truth end points.

D. Model ensembling

Because of the multimodal nature of the predictions, model ensembling is usually tedious in trajectory prediction, as it is not possible to determine which modality should be averaged with which, and even shortest distance matching doesn't guarantee that two predictions highlight the same decision and would make sense averaged together. On the other hand, probability heatmap are a great way of representing information coming from different sources or models in a common system of reference and can be averaged together without any assumption nor risk of mode collapsing.

IV. EXPERIMENTAL RESULTS

A. Experimental settings a) Datasets: The Argoverse Motion Forecasting Dataset [START_REF] Chang | Argoverse: 3d tracking and forecasting with rich maps[END_REF] is made of 205942 training samples, 39472 validation samples and 78143 test samples, with 2 seconds history, and 3 seconds future sampled at 10Hz. The NuScenes Prediction dataset [START_REF] Caesar | Nuscenes: A multimodal dataset for autonomous driving[END_REF] is made of 32186 training samples and 9041 validation samples, with 2 seconds history and 6 seconds future sampled at 2Hz. The Interaction dataset is made of 447626 training samples and 130403, with 1 seconds history and 3 seconds future trajectory sampled at 10Hz. All datasets provide the local HD-Map as a lanelet graph.

b) Metrics: The metrics commonly used by both datasets are the MR k and minFDE k , which are respectively the Miss Rate and the minimum Final Displacement Error for the top k predictions, as well as the minimum Average Displacement Error minADE k . Following their respective leaderboards, we report these metrics for k=1,5 for Argoverse, k=1,5,10 for NuScenes and k=1,6 for Interaction. All metrics are the lower the better. c) Implementation details: All models are trained with batchsize 32 and Adam optimizer. Trainings last 16 epochs for validation evaluation and 32 epochs for test evaluation. The initial learning rate is 1e -3 and is divided by 2 at epochs 3, 6, 9 and 13. All layers have 64 channels, graph convolution and attention layers are followed by Layer Normalization [START_REF] Ba | Layer normalization[END_REF]. ReLU activation is used after every layer. We upsample the HD-Maps lanelets to obtain an average lenght of 10m per lanelet.

The architecture is the same for all datasets, with the exception of the sampling radius r that we can tune according to the uncertainty spread of the dataset and the metrics we want to optimize. The default sampling radius we use

B. Comparison with State-of-the-art

We report our results on the online Argoverse test set in Tab. I, on the online NuScenes leaderboard in Tab. II, and on the Interaction validation set in Tab. III. We compare it to the published methods on each of these benchmarks. On Argoverse, our GOHOME method reaches 2 nd place in MR 6 , with the use of a lighter and faster model than 1 st HOME as will be showed in Sec. IV-C. On NuScenes and Interaction, GOHOME ranks first in multiple metrics as well.

a) Ensembling for increased performance and highlight of model differences: We also report the results of our ensembled models on the test set. We first highlight that the ensemble of two similar HOME models (HO+HO) brings significant improvement compared to HOME alone. As a general rule, the more different and complementary two models are, the greater the performance increase will be. We notice that the combination of HOME and GOHOME models (HO+GO) brings a greater improvement than HO+HO, despite each HOME model being better in single performance than the GOHOME model. Our best ensembling, a weighted combination of 9 HOME and GOHOME models, allows us to improve on the existing state-of-the-art by a significant margin, with a more than 15% MR 6 decrease.

C. Ablation studies

We highlight the gains made by replacing convolution operations with graph operations. To measure inference time, we use a batchsize of 16, which can be considered as an average number of agents to be predicted at a given time. We report only the model forward pass, omitting preprocessing and postprocessing, but notice that image preprocessing is sensibly slower, particularly because of the rasterization of the different semantic layers. All times are measured with a Nvidia 2080 TI. While we report inference time, we also notice that training times record an even greater difference. We mostly consider three different architectures: HOME, GNN-HOME which is a modified HOME model with a GNN encoder but the usual CNN decoder, and our new method GOHOME. All numbers are reported on the Argoverse validation set.

1) Graph operation speed-up: We evaluate the speed-up gained by using graph encoding and lane rasters instead of full convolutions in Tab. IV. The CNN encoding and decoding corresponds to the full HOME model, and the GNN with Lane Rasters (LR) to the GOHOME model. We also estimate the impact from the encoding separately by testing a model with GNN encoding and CNN decoding. We report FLOPs, number of parameters and Frame per Seconds. We measure an average number of 140 lanelets and 10 agents per sample to compute FLOPs.

2) Trade-off from lane ranking: We show in Tab. V the effects of only selecting the top k lanes to extract rasters. We Fig. 4: Inference time with regard to output range Fig. 5: Inference time with regard to pixels per meters fix an input range of 128 and output range of 192 with 0.5m x 0.5m pixel resolution. We observe that this ranked selection doesn't decrease performance, as limiting the number of projected lanes seems to actually improve the metrics, and brings effective speed-up.

3) Image size and resolution scaling: While a 192m image range, which amount to a 88m reach in each direction, may be sufficient in most urban driving predictions with a time horizon of 3s, other datasets can require predictions up to 6 or 8 seconds [START_REF] Caesar | Nuscenes: A multimodal dataset for autonomous driving[END_REF], [START_REF] Ettinger | Large scale interactive motion forecasting for autonomous driving: The waymo open motion dataset[END_REF]. There is therefore a need to increase this output range, which can be done without necessarily increasing the input size, as far distances are reached with long straight trajectories that can be easily extrapolated on highways.

We compare the scaling of our graph-based GOHOME model to the one of an image-based HOME model with regard to output range and resolution. Fig. 4 highlights the output range scaling, where we use a fixed input range of 128 meters, and a resolution of 0.5m per pixel. Whereas the CNN decoders of HOME and GNN-HOME lead to a quadratic scaling, the lane rasters combined with the top 20 lane ranking enable a scaling that is even less than linear.

We show in Fig. 5 the inference time with regard to the number of pixels per meter, which is the inverse of the resolution. While efficient optimization of convolution and constant costs lead to close inference times for the initial 2 pixels per meter, the more efficient scaling shows clearly for GNN inputs and especially Lane Rasters outputs. As the resolution of the lane rasters is also scaled with the total resolution, the quadratic complexity is still applied, but with a much lesser coefficient that allows for realistic training and inference times for finer resolutions.

D. Qualitative results

We show in Fig. 6 some qualitative results of our GO-HOME model. The lane prediction displayed on top can be assimilated to the representation of epistemic uncertainty, as the choice of where the driver will decide to go, whereas the spread of the final heatmap modes models aleatoric uncertainty in the trajectory controls. We observe that the model assign different modes for each lane possibility, and that each of these modes is well aligned with the corresponding lane with a spread along the curvilinear direction.

V. CONCLUSION

In this paper, we propose GOHOME, a trajectory prediction framework generating a global heatmap probability distribution without the use of any image based convolution. Through the use of graph operations, ranking and projections, our model reaches state-of-the-art performance on three datasets with great scaling with regards to the predicted range and resolution.

Fig. 1 :

 1 Fig.1: GOHOME pipeline. The lane graph extracted from the HD-Map is processed through a graph encoder. Each lane then generates a local curvilinear raster that is combined into a predicted probability distribution heatmap.

Fig. 2 :

 2 Fig. 2: GOHOME model architecture

Fig. 3 :

 3 Fig. 3: Lane raster grid projection onto cartesian coordinates. a) A single node of the graph is a lanelet and describes a road segment. b) A rectangular raster is generated along the curvilinear coordinates of the lanelet. c) The lanelet coordinates are then used to project the predicted raster back into cartesian coordinates to complete the final heatmap output.

Fig. 6 :

 6 Fig. 6: Qualitative examples of GOHOME output. Graph lane classification is shown in framed inserts

TABLE I :

 I Argoverse Leaderboard[START_REF]Argoverse motion forecasting competition[END_REF]

		K=1			K=6
		minFDE	MR	minADE minFDE	MR
	LaneGCN [17]	3.78	59.1	0.87	1.36	16.3
	TPCN [28]	3.64	58.6	0.85	1.35	15.9
	Jean [2]	4.24	68.6	1.00	1.42	13.1
	SceneTrans [29]	4.06	59.2	0.80	1.23	12.6
	LaneRCNN [7]	3.69	56.9	0.90	1.45	12.3
	PRIME [18]	3.82	58.7	1.22	1.56	11.5
	DenseTNT [30]	3.70	59.9	0.94	1.49	10.5
	HOME [10]	3.65	57.1	0.93	1.44	9.8
	GOHOME	3.65	57.2	0.94	1.45	10.5
	HO+HO	3.57	56	0.92	1.41	9.4
	HO+GO	3.53	55.8	0.92	1.40	9.1
	Best ensemble	3.68	57.2	0.89	1.29	8.5

TABLE II

 II

		: NuScenes Leaderboard [31]
		K=5		K=10		k=1
		minADE MR	minADE MR	minFDE
	CoverNet [4]	1.96	67	1.48	_	_
	Trajectron++ [32]	1.88	70	1.51	57	9.52
	ALAN [8]	1.87	60	1.22	49	9.98
	SG-Net [33]	1.86	67	1.40	52	9.25
	WIMP [34]	1.84	55	1.11	43	8.49
	MHA-JAM [35]	1.81	59	1.24	46	8.57
	CXX [36]	1.63	69	1.29	60	8.86
	LaPred [9]	1.53	_	1.12	_	8.12
	P2T [37]	1.45	64	1.16	46	10.50
	GOHOME (r=2.6m)	1.42	57	1.15	47	6.99
	GOHOME (r=1.8m)	1.59	46	1.15	34	7.01

TABLE III

 III

	: Interaction Validation
		minFDE 1	minFDE 6
	TNT [6]	_	0.67
	HEAT-I-R [38]	0.66	_
	ReCoG [39]	0.65	_
	ITRA [40]	_	0.49
	GOHOME	0.61	0.45

TABLE IV :

 IV Performance/Complexity comparison

	Model	K=6		#Param	FLOPs
		minFDE MR
	HOME	1.28	6.8	5.1M	4.8G
	GNN-HOME	1.28	7.2	0.43M	0.81G
	GOHOME	1.26	7.1	0.40M	0.09G

TABLE V :

 V Lane ranking speed-up

	# lanes	K=6		FPS
		minFDE MR
	All	1.28	7.5	17
	20	1.26	7.1	34
	10	1.26	7.3	45
	for Argoverse is 1.8m, slightly less than the 2m threshold
	defining the MissRate. Since the nuScenes dataset has a
	longer prediction horizon, it is more uncertain and therefore
	a larger radius of 2.6m is required to optimiwe the minADE 5 ,
	however MissRate remains better for a lower radius of 1.8m.
	Finally, since Interaction perception data comes from drones,
	it is much less noisy and therefore generates much more
	focused probability predictions which we sample with a
	radius of 1.4m.

IoV team, Paris Research Center, Huawei Technologies France

MINES ParisTech, PSL University, Center for robotics Contact: thomas.gilles@mines-paristech.fr


Sun Correspondence

Hur

Yanick J Crow
email: yanickcrow@mac.com.

Maladies Métaboliques De Nancy

Keywords: Aicardi-Goutières syndrome, IFIH1, MDA5, Singleton Merten syndrome, Type I interferonopathy

IFIH1 gain-of-function has been reported as a cause of a type I interferonopathy encompassing a spectrum of autoinflammatory phenotypes including Aicardi-Goutières syndrome and Singleton Merten syndrome. Ascertaining patients through a European and North American collaboration, we set out to describe the molecular, clinical and interferon status of a cohort of individuals with pathogenic heterozygous mutations in IFIH1. We identified 74 individuals from 51 families segregating a total of 27 likely pathogenic mutations in IFIH1. Ten adult individuals, 13.5% of all mutation carriers, were clinically asymptomatic (with seven of these aged over 50 years). All mutations were associated with enhanced type I interferon signaling, including six variants (22%) which were predicted as benign according to multiple in silico pathogenicity programs. The identified mutations cluster close to the ATP binding region of the protein. These data confirm variable expression and nonpenetrance as important characteristics of the IFIH1 genotype, a consistent association with enhanced type I interferon signaling, and a common mutational mechanism involving increased RNA binding affinity or decreased efficiency of ATP hydrolysis and filament disassembly rate.

gene 5 protein: MDA5) which senses viral double-stranded (ds) RNA in the cytosol, leading to the induction of a type I interferon-mediated antiviral response. Consequent to Mendelian determined gain-of-function, it is suggested that IFIH1 inappropriately senses self-derived nucleic acid as viral, leading to an autoinflammatory state classified as a type I interferonopathy [START_REF] Ahmad | Breaching self-tolerance to alu duplex RNA underlies MDA5-mediated inflammation[END_REF][START_REF] Crow | Aicardi-Goutieres syndrome and the type I interferonopathies[END_REF]. In 2015, a p.Arg822Gln substitution in IFIH1 was shown to cause Singleton Merten syndrome (SMS), an autosomal dominant trait variably characterized by a deforming arthropathy, abnormal tooth development and cardiac valve calcification, again in association with enhanced type I interferon signaling [START_REF] Rutsch | A specific IFIH1 gain-of-function mutation causes Singleton-Merten syndrome[END_REF]. Although it was initially considered that SMS was a distinct, mutation-specific disorder, subsequent reports indicate that SMS and the neuroinflammatory phenotypes seen in the context of IFIH1 gain-of-function constitute part of the same disease spectrum [START_REF] Buers | MDA5-associated neuroinflammation and the Singleton-Merten syndrome: Two faces of the same type I interferonopathy spectrum[END_REF][START_REF] Bursztejn | Unusual cutaneous features associated with a heterozygous gain-of-function mutation in IFIH1: Overlap between Aicardi-Goutieres and Singleton-Merten syndromes[END_REF].

Type I interferonopathy associated IFIH1 mutations are either absent from control databases, or only present at very low frequency. However, we have noted previously that in silico algorithms are not always reliable in differentiating IFIH1 disease-causing variants from benign polymorphisms [START_REF] Ruaud | Autosomaldominant early-onset spastic paraparesis with brain calcification due to IFIH1 gain-of-function[END_REF]. Such difficulty in assigning molecular pathogenicity is compounded by marked variability in disease expression, sometimes even within the same family, and the observation of complete non-penetrance in certain pedigrees [START_REF] Rice | Gain-of-function mutations in IFIH1 cause a spectrum of human disease phenotypes associated with upregulated type I interferon signaling[END_REF]. Given this background, we considered it important to provide an update of our experience of sequencing individuals for pathogenic IFIH1 mutations associated with a type I interferonopathy state. In total, we describe molecular and clinical data relating to 74 individuals from 51 families, identifying 27 likely pathogenic mutations that cluster close to the ATP binding region of the protein. Our data confirm variable expression and nonpenetrance as important characteristics of these mutant genotypes, and the consistent association with enhanced type I interferon signaling as assessed by interferon-stimulated gene (ISG) expression, referred to as the interferon score.

| MATERIALS AND METHODS

| Subjects

Patients were ascertained through direct contact and/or collaborating physicians across clinical research laboratories in the UK and France (Crow), the USA (Vanderver), and Italy (Orcesi). The study was approved by the Leeds (East) Research Ethics Committee (10/ H1307/132), the Comite de Protection des Personnes (ID-RCB/EUDRACT: 2014-A01017-40), IRB study protocol (Myelin Disorders Bioregistry Project: IRB# 14-011236) and the local ethics committee of the IRCCS Mondino Foundation, Pavia, Italy (3549/2009of 30/9/2009and 11/12/2009;n.20170035275 of 23/10/2017). Amino acid substitutions were considered as pathogenic mutations when they were seen in the context of a neuroimmune/autoinflammatory state (including AGS, a spastic-dystonic syndrome, nonsyndromic spastic paraparesis or SMS), and when two or more of the following applied: observation of the same variant in an unrelated family; de novo occurrence; documented increase in ISG expression; in vitro data consistent with IFIH1 gain-of-function.

| Mutational analysis

Mutations were identified on a variety of next-generation sequencing platforms. Where Sanger sequencing was undertaken, primers were designed to amplify the coding exons of IFH1, with mutation annotation based on the reference cDNA sequence NM_022168.2.

Variants were assessed using the in silico programs SIFT (http://sift.jcvi.org), Polyphen2 (http://genetics.bwh.harvard.edu/pph2/), and CADD (https://cadd.gs.washington.edu), summarized in VarCards (http://varcards.biols.ac.cn/). Population allele frequencies were obtained from the gnomAD database (http://gnomad.broadinstitute.org).

| Protein modeling

Molecular graphics figures were generated with PyMOL (Schrödinger) using the PDB coordinates (4GL2).

| Interferon score

Interferon scores were calculated on the basis of the expression of ISGs according to previously published protocols. In brief, this involved either a quantitative reverse transcription-polymerase chain reaction (qPCR) analysis using TaqMan probes (Crow laboratory: Rice et al., 2013), or testing on a Nanostring platform (Vanderver laboratory: Adang et al., 2018+). In the former, the relative abundance of IFI27 (Hs01086370_m1), IFI44L (Hs00199115_m1), IFIT1 (Hs00356631_g1), ISG15 (Hs00192713_m1), RSAD2 (Hs01057264_m1), and SIGLEC1 (Hs00988063_m1) transcripts was normalized to the expression levels of HPRT1 (Hs03929096_g1) and 18S (Hs999999001_s1). The median fold change of the six genes, compared to the median of 29 previously collected healthy controls, was then used to create an interferon score for each individual, with an abnormal interferon score being defined as greater than +2 standard deviations above the mean of the control group that is 2.466. Alternatively, the copy number of mRNA transcripts of the six ISGs listed above, and four housekeeping genes (ALAS1, HPRT1, TBP, and TUBB), was quantified using a Nanostring nCounter™ Digital Analyzer. The raw copy number of mRNA transcripts of each ISG was standardized using the geometric mean of the four housekeeping genes for each individual, and the six-gene interferon signature for each individual calculated using the median of the Z scores, with the result considered positive if ≥1.96 (>98th centile; one tail analysis).

| Interferon reporter assay

The pFLAG-CMV4 plasmid encoding IFIH1 has been described elsewhere [START_REF] Rice | Gain-of-function mutations in IFIH1 cause a spectrum of human disease phenotypes associated with upregulated type I interferon signaling[END_REF]. Indicated mutations were introduced using Phusion HiFi DNA polymerase. HEK 293T cells (ATCC) were maintained in 48-well plates in DMEM (Cellgro) supplemented with 10% fetal bovine serum and 1% L-glutamine. At 80% confluence, cells were cotransfected with pFLAGCMV4 plasmids encoding wild-type or mutant IFIH1 (5 ng, unless indicated otherwise), interferon β (IFNb) promoter-driven firefly luciferase reporter plasmid (100 ng), and a constitutively expressed Renilla luciferase reporter plasmid (pRL-TK, 10 ng), by using Lipofectamine 2000 (Life Technologies) according to the manufacturer's protocol. The medium was changed 6 hr after transfection, and cells were subsequently incubated for 18 hr with or without stimulation with poly(I-C) (500 ng; InvivoGen) using Lipofectamine 2000. Cells were lysed with Passive Lysis Buffer (Promega), and IFNb promoter activity was measured using a Dual-Luciferase Reporter Assay (Promega) and a Synergy 2 plate reader (BioTek). Firefly luciferase activity was normalized to Renilla luciferase activity Each experiment was performed in triplicate and data are presented as mean ± standard mean of error. Statistical significance was determined by two-tailed, unpaired Student's t-test with *, **, and *** indicating p values <.05, <.01, and <.001, respectively. Expression levels of individual constructs were tested by western blot analysis.

| RESULTS

| Molecular data

We collected data on 74 individuals from 51 families, identifying 27 distinct mutations in total (Figure 1; Table 1). Fourteen mutations were recorded in a single proband, seven in more than one individual belonging to a single-family, and six in more than one family. Of these six recurrent mutations, the p.Arg720Gln, p.Arg779Cys, and p.Arg779His substitutions were observed most frequently (6, 8, and 10 times, respectively). Twenty-two mutations were recorded to have occurred de novo in at least one individual, whilst four mutations were only ascertained in familial cases demonstrating autosomal dominant transmission (two mutations, p.Ala489Thr and p.Gly495Arg, were transmitted from a father in whom the mutation arose de novo). Three mutations, p.Thr331Arg, p.Arg779Cys, and p.Arg779His, was documented to have occurred both de novo, in association with severe, AGS-like, neurological disease, and in families with transmission across two or more generations.

For six putative mutations (p.Gly389Arg; p.Asn449Lys; p.Ile583Val; p.Ile803Phe; p.Asp848Glu; p.Ile956Val), in silico predictions using both SIFT and Poyphen2 suggested that the substitutions were benign, with relatively poor evolutionary conservation (Figure S1). However, all of these variants were novel (i.e., not recorded in gnomAD), and assays of interferon signaling (ISG expression and in vitro testing) indicate that they represent pathogenic mutations conferring gain-of-function (Table S1; Figure S2). Of note, four of these variants were seen in the context of a spastic paraparesis phenotype with no or minimal cognitive impairment. Clinical nonpenetrance was observed in three of these families (the other three variants arising in the proband de novo).

| Clinical phenotype

Consistent with previous data, we observed a spectrum of phenotypes in our cohort, encompassing classical AGS, less easily defined rapid neuroregression, a spastic-dystonic syndrome, spastic paraparesis, SMS, and clinical nonpenetrance (Figure 2; Table 2; Table S2). A single individual, AGS2222, experienced neonatal hepatitis and then developed chronic fibrotic liver disease in the absence of any other clinical features (note that this same variant was seen in another proband, AGS735, presenting with neuroregression at age 1 year). Unequivocal episodes of rapid neuroregression were noted in at least 20 patients, in seven of whom an acute loss of skills occurred after the age of 1 year on a background of completely normal development. Recognition/onset of symptoms was frequently later in patients with a spastic paraparesis phenotype, with one patient experiencing the development of lower limb spasticity beginning at 13 years of age (AGS531_P4). Six symptomatic patients were recorded to have died. Five of these individuals demonstrated a severe AGS phenotype with features obvious at, or soon after, birth that is indicating prenatal onset. One further deceased patient presented with neuroregression at age 15 months, and died suddenly of a cardiorespiratory arrest at 16 years of age, with pulmonary hypertension documented on postmortem examination. Ten individuals were reported as asymptomatic mutation carriers, across five mutations (p.Gly389Arg, p.Arg779Cys, p.Arg779His p.Asp848Glu, and p.Ile956Val), with seven aged over 50 years.

| Interferon status

Where tested, all mutations (i.e., 26 of 27) were associated with increased expression of ISGs in peripheral blood (Table 1). Samples were unavailable for the single patient carrying the p.Glu773Gln substitution. This variant is not recorded in gnomAD, occurring de novo in the context of a phenotype compatible with IFIH1 upregulation, and conferring a gain-offunction in our in vitro assay (Figure S2). Considering all (51) mutation-positive individuals tested for ISG expression in the Crow laboratory (given that a direct comparison of results across laboratories is not possible), 109 of 117 values were positive (Table S3; Figure S3). Only one clinically symptomatic patient (AGS2154_1) demonstrated a negative interferon signature (on two of three occasions tested). The phenotype, in this case, was unusual; a child with white matter disease confined to the right cerebral hemisphere on MRI and no abnormal neurological signs on examination, having presented at age 8 years with headaches. We leave open the possibility that these two normal results, and three normal results from his mother, might be due to technical artifact, given that the samples had been stored for many months before testing. Sixteen samples from seven clinically nonpenetrant subjects exhibited an upregulation of interferon signaling, with two asymptomatic mutation carriers demonstrating normal interferon signatures (each tested on three occasions).

| Modeling of IFIH1 gain-of-function mutations

Modeling of the 27 mutations described here showed that most residues cluster near the ATP binding site within the helicase domain (Figure 3). Three mutations, p.Ileu583Val, p.Ileu956Val, and p.Leu979Trp were the only residues not situated in the cluster (colored cyan; only p.Ileu583Val and p.Leu979Val are shown since residue p.Ileu956 is disordered in the crystal structure). Within this main cluster, residues can be further categorized into three groups: those at the ATP binding pocket (magenta spheres), those in the double stranded RNA (dsRNA) binding surface (colored blue) and those not directly involved in either ATP or RNA binding (colored green). Three published mutations (p.Leu372phe; p.Ala452Thr; p.Glu813Asp; Table S4) not ascertained in our cohort are also located within the main cluster (colored orange), further supporting the importance of this region in the regulation of IFIH1 signaling activity.

| DISCUSSION

Here we present data on 74 individuals, 41 previously unreported, from 51 families, with a putative gain-of-function mutation in IFIH1. Consistent with previous descriptions, we observed a spectrum of phenotypes, encompassing AGS, spastic-dystonia, spastic paraparesis, SMS and clinical nonpenetrance. Phenotypic variability was common, both in the context of familial inheritance and mutations seen recurrently across families so that no obvious genotype-phenotype correlations could be ascertained.

Acute regression was noted in almost one-third of symptomatic mutation carriers, occurring after the age of 1 year in seven patients demonstrating completely normal development to that time. Beyond acute regression, a slower onset of disease, and subsequent progression, was seen in patients demonstrating a spastic paraparesis phenotype. Together with the observation of clinical nonpenetrance (10:13.5% of 74 mutation-positive individuals in our series), with seven individuals identified to be apparently disease-free beyond the age of 50 years, these data suggest the importance of additive genetic factors and/or environmental triggers in determining phenotypic status. Although we did not formally record neuroimaging features in our cohort, white matter disease and intracranial calcification were observed frequently. Such imaging characteristics can be seen in the absence of overt neurological signs (see [START_REF] Bursztejn | Unusual cutaneous features associated with a heterozygous gain-of-function mutation in IFIH1: Overlap between Aicardi-Goutieres and Singleton-Merten syndromes[END_REF][START_REF] De Carvalho | Musculoskeletal disease in MDA5-related type I interferonopathy: A Mendelian mimic of Jaccoud's arthropathy[END_REF]. Conversely, significant neurological disease, most typically spastic paraparesis, can occur in the context of normal brain and spinal imaging (e.g., the father in family AGS524).

Clinically manifest extraneurological illness was uncommon in our series, but there appears to be a real association between IFIH1 gain-of-function and lupus-like illness, autoimmune hepatitis, and hypothyroidism. Furthermore, psoriatic-like skin disease is a well-recognized feature of the SMS phenotype. As recently described [START_REF] Adang | Aicardi goutieres syndrome is associated with pulmonary hypertension[END_REF], two patients included here were diagnosed with pulmonary hypertension, a feature which was not searched for in most patients and may be under-recognized.

We observed a strong association of mutation status with an enhanced expression of ISGs, with 109 of 117 samples from 51 patients being positive in the experience of one laboratory. A similar conclusion can be drawn from in vitro testing. As such, upregulated interferon signaling represents a reliable biomarker of IFIH1 gain-of-function, and can serve as an indicator of variant pathogenicity where doubt exists as to the significance of a molecular lesion. This is important given that we show here that in silico algorithms do not always accurately predict pathogenicity (involving 22% of the mutations that we recorded). Where tested, clinical nonpenetrance was also associated with a persistent upregulation of interferon signaling, with only two of nine such individuals nonpenetrant on ISG testing in blood. Whether these individuals demonstrate fluctuations in ISG expression is not known at this time.

Despite documented clinical nonpenetrance in some cases, all putative IFIH1 gain-offunction substitutions are rare, with only two of the 30 discrete mutations described here and in previous reports recorded in gnomAD. Furthermore, all ascertained type I interferonopathy associated mutations are missense variants, likely conferring increased sensitivity to a self-derived nucleic acid. Although premature termination mutations in the helicase domain are seen in control populations as common polymorphisms, none has been associated with a type I interferonopathy phenotype, further supporting the role of nucleic acid binding by the helicase domain in disease pathogenesis. Substitutions of the arginine residues at positions 720 and 779 were seen in six and 19 probands, respectively, in our series. Given the focus of our laboratories on pediatric neurological disease, our data are likely to subject to ascertainment bias. Indeed, although only observed once by us, the p.Arg822Gln mutation has been reported in an additional five pedigrees demonstrating a classical SMS phenotype [START_REF] Pettersson | Further evidence for specific IFIH1 mutation as a cause of Singleton-Merten syndrome with phenotypic heterogeneity[END_REF][START_REF] Rutsch | A specific IFIH1 gain-of-function mutation causes Singleton-Merten syndrome[END_REF].

IFIH1 is a member of the retinoic acid-inducible gene I (RIG-I) receptor family [START_REF] Del Toro Duany | MDA5-filament, dynamics and disease[END_REF]. Recognition of cytoplasmic viral dsRNA by IFIH1 induces filament assembly along the dsRNA axis, with the helicase domains and C terminal domain responsible for RNA recognition. Filament formation then induces oligomerization of the tandem CARD domains (2CARD) of IFIH1, leading to the interaction with mitochondrial MAVS and subsequent induction of interferon and other proinflammatory cytokines. IFIH1 filament stability is intrinsically regulated by ATP hydrolysis, which is stimulated upon dsRNA binding. Mutations that impair ATP hydrolysis generally increase filament stability and, often, but not always, confer gain-of-function signaling activity. The clustering of mutations that we ascertained, and of a further three unique published mutations, near the ATP binding region likely highlights common mechanisms, perhaps increasing RNA binding affinity or decreasing the efficiency of ATP hydrolysis and the rate of filament disassembly.

Summarizing, IFIH1 gain-of-function is associated with a spectrum of phenotypes, occurring due to de novo mutations or transmitted as an autosomal dominant trait. Testing for an interferon signature in blood represents a useful biomarker in this context, which can aid in the interpretation of identified sequence variants.

Supplementary Material

Refer to Web version on PubMed Central for supplementary material.

Authors

Gillian I. Rice 1 , Sehoon Park 2,3 , Francesco Gavazzi 4 , Laura A. Adang 4 , Loveline A. Ayuk 5 , Lien Van Eyck 6 , Luis Seabra 6 , Christophe Barrea 7 , Roberta Battini 8,9 , Alexandre Belot 10,11 , Stefan Berg 12 , Thierry Billette de Villemeur 13 , Annette E. Bley 14 , Lubov Blumkin 15,16 Mutation mapping. Structure of human IFIH1 (4GL2) in complex with double stranded RNA (dsRNA; blue stick model in the center). Only the RNA binding domain (helicase domain and C-terminal domain, CTD) are included in the crystal structure. Note that the helicase domain consists of Hel1, Hel2i, and Hel2. Mutations are indicated by spheres using the following color code: residues in the ATP binding pocket (magenta), residues in the dsRNA binding surface (blue), residues within the main cluster but not directly involved in RNA binding or ATP binding (green), residues outside the main cluster (cyan), and residues previously reported by others but not in our cohort (orange). We considered all 27 mutations reported here plus three previously published mutations (p.Leu372Phe; p.Ala452Thr; p.Glu813Asp) not ascertained in our series. Residues p.Arg822, p.Arg824, and p.Ile956 are not shown because they are disordered in the crystal structure, but are expected to be located in the ATP binding (p.Arg822 and p.Arg824) and RNA binding (p.Ile956) pockets

FIGURE 1 .

 1 FIGURE 1.Schematic showing the positions of protein domains and their amino acid boundaries within the 1,025-residue IFIH1 protein. The 27 mutations ascertained in the present study are annotated, with the numbers in brackets indicating the number of families in which each mutation was observed. Three previously published mutations (p.Leu372Phe; p.Ala452Thr; p.Glu813Asp), not ascertained in our series, are also denoted (below the cartoon). CARD, caspase activation recruitment domain; Hel, helicase domain, where Hel1 and Hel2 are the two conserved core helicase domains and Hel2i is an insertion domain that is conserved in the RIG-I-like helicase family; P, pincer or bridge region connecting Hel2 to the C-terminal domain (CTD) involved in binding double stranded RNA

FIGURE 2 .

 2 FIGURE 2. Overview of phenotypes observed in the IFIH1-mutation-positive cohort. Classification of 68 of 74 individuals according to phenotype. For clarity, six individuals displaying characteristics difficult to classify were omitted from this analysis

  FIGURE 3.

Alive

  , Odile Boespflug-Tanguy 17,18 , Tracy A. Briggs 1,19 , Elise Brimble 20 , Russell C. Dale 21 , Niklas Darin 22,23 , François-Guillaume Debray 24 , Valentina De Giorgis 25 , Jonas Denecke 14 , Diane Doummar 26 , Gunilla Drake af Hagelsrum 27 , Despina Eleftheriou 28 , Margherita Estienne 29 , Elisa Fazzi 30,31 , François Feillet 32 , Jessica Galli 30,31 , Nicholas Hartog 33 , Julie Harvengt 34 , Bénédicte Heron 35 , Delphine Heron 36 , Diedre A. Kelly 37 , Dorit Lev 16,38 , Virginie Levrat 39 , John H. Livingston 40 , Itxaso Marti 41 , Cyril Mignot 42 , Fanny Mochel 43 , Marie-Christine Nougues 44 , Ilena Oppermann 14 , Belén Pérez-Dueñas 45 , Bernt Popp 46 , Mathieu P. Rodero 6 , Diana Rodriguez 47,48 , Veronica Saletti 49 , Cia Sharpe 50 , Davide Tonduti 51 , Gayatri Vadlamani 40 , Keith Van Haren 20 , Miguel Tomas Vila 52 , Julie Vogt 53 , Evangeline Wassmer 54 , Arnaud Wiedemann 32 , Callum J. Wilson 55 , Ayelet Zerem 15,16 , Christiane Zweier 46 , Sameer M. Zuberi56,57 , Simona Orcesi 25,58 , Adeline L. Vanderver 4 , Sun Hur 2,3 , Yanick J. Crow6,59,60

TABLE 1

 1 Details of individual IFIH1 mutations identified in the families included in the present data set

cDNA change Protein change Families (de novo inheritance; or, number of symptomatic

	Families (de novo	inheritance; or, Associated	number of symptomatic phenotypes ('/'	and non-within Upregulation	penetrant individuals family)(';' of Assessment by	where between interferon interferon CADD Var-	Associated cDNA change Protein change familial) families) signalling reporter assay gnomAD SIFT Polyphen2 score cards	and non-within Upregulation AGS2154 (1;1); AGS2180 phenotypes ('/' (de novo); AGS2507 (de penetrant individuals family)(';' of Assessment by novo); LD_1030.0 (de where between interferon interferon CADD Var-novo) familial) families) signalling reporter assay gnomAD SIFT Polyphen2 score cards disease/CNP; AGS	c.992C>G p.Thr331Arg AGS674 (de novo); AGS-SMS; SMS Yes Yes (de Carvalho et Novel Deleterious 0 Probably 29.7 22:23 c.2336G>A p.Arg779His AGS163 (de novo); AGS259 (3;2); AGS1351 AGS1972 (2;0) al., 2017) damaging 1.000 (de novo); AGS1509 (de AGS; CNP; NR; SP Yes Yes (Rice et al., 2014) 1/244230 Tolerated 0.05 Probably damaging 0.994 28.9 19:23	c.992C>T p.Thr331Ile AGS1938 (3;0) SMS Yes Yes (de Carvalho et Novel Deleterious 0 Probably 31 22:23 novo); AGS2177 (1;2); Berg_1 (de novo); al., 2017) damaging 1.000 Orc_0098 (de novo);	c.1009A>G p.Arg337Gly AGS237 (de novo) NR Yes Yes (Rice et al., Novel Tolerated 0.12 Probably 26.8 17:23 LD_1199.0 (de novo);	2014) damaging 1.000 LD_1381 (3;1);	LD_1585.0 (de novo)	c.1165G>A p.Gly389Arg AGS848 (2;1) AGS/SP/CNP Yes Yes (this paper) Novel Tolerated 0.88 Benign 0.108 5.325 01:23	c.2336G>T p.Arg779Leu LD_1067.0 (de novo) AGS Yes No Novel Tolerated 0.06 Probably 35 21:23	c.1178A>T p.Asp393Val AGS626 (de novo) NR Yes Yes (Rice et al., Novel Deleterious Probably 28.6 16:23 damaging 1.000	2014) 0.01 damaging 0.998	c.2342G>A p.Gly781Glu LD_0940.0 (de novo); NR; SP Yes No Novel Deleterious 0 Probably 32 19:23	c.1178A>C p.Asp393Ala AGS2586 (de novo) AGS Yes No Novel Deleterious Possibly 24.8 12:23 LD_0943.0 (de novo) damaging 1.000	0.03 damaging 0.913	c.2404A>G p.Asn802Asp AGS2662 (de novo) NR Yes No Novel Tolerated 0.22 Probably 28.1 18:23	c.1331A>G p.Glu444Gly AGS2669 (de novo) AGS Yes Yes (this paper) Novel Deleterious 0 Probably 31 23.23 damaging 1.000	damaging 1	c.2407A>T p.Ile803Phe LD_1488.0 (de novo) AGS Yes Yes (this paper) Novel Tolerated 0.24 Benign 0.043 11.8 04:23	c.1347C>G p.Asn449Lys AGS1001 (de novo) SP Yes Yes (this paper) Novel Tolerated 0.64 Benign 0.163 13.91 03:23	c.1465G>A p.Ala489Thr AGS755 (3;0) a CLL/AGS-SMS/SMS Yes Yes (Bursztejn et al., 2015) Novel Deleterious 0 Probably damaging 1.000 32 21:23 c.2465G>A p.Arg822Gln AGS1514 (de novo) SD-ICC Yes Yes (Rutsch et al., 2015) 6/244096 Deleterious 0 Probably damaging 1.000 35 23:23	c.1483G>A p.Gly495Arg AGS524 (2;0) a SP-LLD/SP Yes Yes (Rice et al., 2014) Novel Deleterious 0.01 Probably damaging 0.982 23.3 14:23 c.2471G>A p.Arg824Lys AGS735 (de novo); AGS2222 (de novo) NR; Isolated liver disease Yes No Novel Deleterious 0 Probably damaging 1.000 34 22:23	c.2486C>G p.Thr829Ser AGS1290 (2 siblings and AGS Yes No Novel Tolerated 0.73 Possibly 16.61 12:23	c.1747A>G p.Ile583Val AGS2369 (de novo) AGS Yes Yes (this paper) Novel Tolerated 0.48 Benign 0.00 0.573 5.23 NPDT) damaging 0.512	c.2156C>T p.Ala719Val Hm_1 (de novo) AGS Yes No Novel Tolerated 0.07 Possibly 27.1 09:23 c.2544T>G p.Asp848Glu AGS531 (3;2) SP-ICC/CNP Yes Yes (Ruaud et al., Novel Tolerated 0.4 Benign 0.004 10.08 02:23	damaging 0.949 2018)	c.2159G>A p.Arg720Gln AGS102 (de novo); AGS; SP Yes Yes (Rice et al., Novel Deleterious 0 Probably 34 17:23 c.2561T>A p.Met854Lys AGS2081 (de novo) AGS/SMS Yes No Novel Deleterious 0 Probably 31 18:23	AGS647 (de novo); 2014) damaging 0.992 damaging 1.000	AGS1504 (de novo);	AGS2422 (NPDT); c.2866A>G p.Ile956Val AGS1430 (2;1) SP-ICC/CNP Yes Yes (this paper) Novel Tolerated 0.77 Benign 0.004 3.576 06:23	AGS2548 (de novo); c.2936T>G LD_0982.0 (de novo) p.Leu979Trp LD_1346.0 (de novo) AGS Yes Yes (this paper) Novel Deleterious 0.01 Probably damaging 1.000 26.6 16:23	c.2317G>C p.Glu773Gln AGS2399 (de novo) NR NA Yes (this paper) Novel Tolerated 0.27 Possibly 24.8 13:23	damaging 0.743 Note: IFIH1 mutation annotation based on the reference complementary DNA sequence NM_022168.2.	c.2335C>T p.Arg779Cys AGS376 (NPDT); AGS-LLD; SP-Yes Yes (Rice et al., Novel Deleterious Probably Abbreviations: AGS, Aicardi-Goutières syndrome; CLL, Chilblain-like lesions; CNP, clinical nonpenetrance; ICC, Intracranial calcification; LLD, Lupus-like disease; NPDT, no parental DNA testing; NR, AGS723 (NPDT); ICC; 2014) 0.01 damaging 1.000	AGS1004 (de novo); NR; unilateral neuro-regression; SD, spastic dystonia; SP, spastic paraparesis; SMS, Singleton Merten syndrome.	AGS1156 (de novo); white matter

a This mutation was shown to have been paternally inherited by the proband and to have occurred de novo in the proband's father.

TABLE 2

 2 Molecular and clinical data by family

			Status at last contact	(age in years)	Deceased (2)	Alive (13)	Deceased (16)		Alive (13)	Alive (54)	Deceased (84)	Deceased (3)
				Clinical phenotype	AGS	AGS	Neuroregression and SD starting at	age 15 months	AGS	Clinically nonpenetrant	Clinically nonpenetrant	AGS with LLD	SP with LLD and AQP4 + TM
			Previously	reported (reference)	Rice et al. (2014)	Rice et al. (2014)	Rice et al. 2014;	Adang et al., 2018	Rice et al. (2014)			Rice et al. (2014)	Rice et al. (2014);	Hacohen et al. 2015;	Crow et al. 2015;	McLellan et al. 2018
	Inheritance (number	of	mutation-positive	Sex cDNA Protein individuals)	M c.2159G>A p.Arg720Gln De novo	M c.2336G>A p.Arg779His De novo	M c.1009A>G p.Arg337Gly De novo		M c.2336G>A p.Arg779His Familial (3)	M	F	M c.2335C>T p.Arg779Cys No parental testing	F c.1483G>A p.Gly495Arg Familial (2)(shown to	have occurred de novo	in P2)
				Individual	P1	P1	P1		P1	P2 (father of P1)	P3 (mother of P2)	P1	P1
				Family	AGS102	AGS163	AGS237	(LD_0762)	AGS259			AGS376	AGS524

Hum Mutat. Author manuscript; available in PMC 2021 April 01.

21:23Hum Mutat. Author manuscript; available in PMC 2021 April 01.

ACKNOWLEDGMENTS

Yanick J. Crow acknowledges The University of Maryland Brain and Tissue Bank of the NIH NeuroBioBank. Yanick J. Crow acknowledges the European Research Council (786142-E-T1IFNs), a state subsidy managed by the National Research Agency (France) under the "Investments for the Future" program bearing the reference ANR-10-

Funding information Public Health Research Programme, Grant/Award Number: TRF-2016-09-002; National Center for Advancing Translational Sciences of the National Institutes of Health, Grant/Award Number: KL2TR001879; CURE Pennsylvania Frontiers in Leukodystrophy, Grant/Award Number: U01HD082806; H2020 European Research Council, Grant/Award Number: 786142-E-T1IFNs; Agence Nationale de la Recherche, Grant/Award Number: ANR-10-IAHU-01; MSDAvenir fund, Grant/Award Number: DEVO-DECODE Project; NIHR Transitional Research Fellowship, Grant/Award Number: TRF-2016-09-002

Data available on request due to privacy/ethical restrictions.

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