Genetic association analysis of complex diseases through information theoretic metrics and linear pleiotropy

The main goal of this thesis was to help in the identification of genetic variants that are responsible for complex traits, combining both linear and nonlinear approaches. First, two one-locus approaches were proposed. The first one defined and characterized a novel nonlinear test of genetic association, based on the mutual information measure. This test takes into account the genetic structure of the population. It was applied to the GAW17 dataset and compared to the standard linear test of association. Since the solution of the GAW17 simulation model was known, this study served to characterize the performance of the proposed nonlinear methods in comparison to the linear one. The proposed nonlinear test was able to recover the results obtained with linear methods but also detected an additional SNP in a gene related with the phenotype. In addition, the performance of both tests in terms of their accuracy in classification (AUC) was similar. In contrast, the second approach was an exploratory study on the relationship between SNP variability among species and SNP association with disease, at different genetic regions. Two sets of SNPs were compared, one containing deleterious SNPs and the other defined by neutral SNPs. Both sets were stratified depending on the region where the polymorphisms were located, a feature that may have influenced their conservation across species. It was observed that, for most functional regions, SNPs associated to diseases tend to be significantly less variable across species than neutral SNPs. Second, a novel nonlinear methodology for multiloci genetic association was proposed with the goal of detecting association between combinations of SNPs and a phenotype. The proposed method was based on the mutual information of statistical significance, called MISS. This approach was compared with MLR, the standard linear method used for genetic association based on multiple linear regressions. Both were applied as a relevance criterion of a new multi-solution floating feature selection algorithm (MSSFFS), proposed in the context of multi-loci genetic association for complex diseases. Both were also compared with MECPM, an algorithm for searching predictive multi-loci interactions with a criterion of maximum entropy. The three methods were tested on the SNPs of the F7 gene, and the FVII levels in blood, with the data from the GAIT project. The proposed nonlinear method (MISS) improved the results of traditional genetic association methods, detecting new SNP-SNP interactions. Most of the obtained sets of SNPs were in concordance with the functional results found in the literature where the obtained SNPs have been described as functional elements correlated with the phenotype. Third, a linear methodological framework for the simultaneous study of several phenotypes was proposed. The methodology consisted in building new phenotypic variables, named metaphenotypes, that capture the joint activity of sets of phenotypes involved in a metabolic pathway. These new variables were used in further association tests with the aim of identifying genetic elements related with the underlying biological process as a whole. As a practical implementation, the methodology was applied to the GAIT project dataset with the aim of identifying genetic markers that could be related to the coagulation process as a whole and thus to thrombosis. Three mathematical models were used for the definition of metaphenotypes, corresponding to one PCA and two ICA models. Using this novel approach, already known associations were retrieved but also new candidates were proposed as regulatory genes with a global effect on the coagulation pathway as a whole

The Central role of KNG1 gene as a genetic determinant of coagulation pathway-related traits: Exploring metaphenotypes

Traditional genetic studies of single traits may be unable to detect the pleiotropic effects involved in complex diseases. To detect the correlation that exists between several phenotypes involved in the same biological process, we introduce an original methodology to analyze sets of correlated phenotypes involved in the coagulation cascade in genome-wide association studies. The methodology consists of a two-stage process. First, we define new phenotypic meta-variables (linear combinations of the original phenotypes), named metaphenotypes, by applying Independent Component Analysis for the multivariate analysis of correlated phenotypes (i.e. the levels of coagulation pathway–related proteins). The resulting metaphenotypes integrate the information regarding the underlying biological process (i.e. thrombus/clot formation). Secondly, we take advantage of a family based Genome Wide Association Study to identify genetic elements influencing these metaphenotypes and consequently thrombosis risk. Our study utilized data from the GAIT Project (Genetic Analysis of Idiopathic Thrombophilia). We obtained 15 metaphenotypes, which showed significant heritabilities, ranging from 0.2 to 0.7. These results indicate the importance of genetic factors in the variability of these traits. We found 4 metaphenotypes that showed significant associations with SNPs. The most relevant were those mapped in a region near the HRG, FETUB and KNG1 genes. Our results are provocative since they show that the KNG1 locus plays a central role as a genetic determinant of the entire coagulation pathway and thrombus/clot formation. Integrating data from multiple correlated measurements through metaphenotypes is a promising approach to elucidate the hidden genetic mechanisms underlying complex diseases.Postprint (published version

Integrated genetic and methylomic analyses identify shared biology between autism and autistic traits

Abstract: Previous studies have identified differences in DNA methylation in autistic individuals compared to neurotypical individuals. Yet, it is unclear if this extends to autistic traits—subclinical manifestation of autism features in the general population. Here, we investigate the association between DNA methylation at birth (cord blood), and scores on the Social and Communication Disorders Checklist (SCDC), a measure of autistic traits, in 701 8-year-olds, by conducting a methylome-wide association study (MWAS). We did not identify significant CpGs associated with SCDC. The most significant CpG site was cg14379490, on chromosome 9 (MWAS beta = − 1.78 ± 0.35, p value = 5.34 × 10−7). Using methylation data for autism in peripheral tissues, we did not identify a significant concordance in effect direction of CpGs with p value 0.5). In contrast, using methylation data for autism from post-mortem brain tissues, we identify a significant concordance in effect direction of CpGs with a p value < 10−4 in the SCDC MWAS (binomial sign test, p value = 0.004). Supporting this, we observe an enrichment for genes that are dysregulated in the post-mortem autism brain (one-sided Wilcoxon rank-sum test, p value = 6.22 × 10−5). Finally, integrating genome-wide association study (GWAS) data for autism (n = 46,350) with mQTL maps from cord-blood (n = 771), we demonstrate that mQTLs of CpGs associated with SCDC scores at p value thresholds of 0.01 and 0.005 are significantly shifted toward lower p values in the GWAS for autism (p < 5 × 10−3). We provide additional support for this using a GWAS of SCDC, and demonstrate a lack of enrichment in a GWAS of Alzheimer’s disease. Our results highlight the shared cross-tissue methylation architecture of autism and autistic traits, and demonstrate that mQTLs associated with differences in DNA methylation associated with childhood autistic traits are enriched for common genetic variants associated with autism and autistic traits

lme4qtl: linear mixed models with flexible covariance structure for genetic studies of related individuals.

BACKGROUND: Quantitative trait locus (QTL) mapping in genetic data often involves analysis of correlated observations, which need to be accounted for to avoid false association signals. This is commonly performed by modeling such correlations as random effects in linear mixed models (LMMs). The R package lme4 is a well-established tool that implements major LMM features using sparse matrix methods; however, it is not fully adapted for QTL mapping association and linkage studies. In particular, two LMM features are lacking in the base version of lme4: the definition of random effects by custom covariance matrices; and parameter constraints, which are essential in advanced QTL models. Apart from applications in linkage studies of related individuals, such functionalities are of high interest for association studies in situations where multiple covariance matrices need to be modeled, a scenario not covered by many genome-wide association study (GWAS) software. RESULTS: To address the aforementioned limitations, we developed a new R package lme4qtl as an extension of lme4. First, lme4qtl contributes new models for genetic studies within a single tool integrated with lme4 and its companion packages. Second, lme4qtl offers a flexible framework for scenarios with multiple levels of relatedness and becomes efficient when covariance matrices are sparse. We showed the value of our package using real family-based data in the Genetic Analysis of Idiopathic Thrombophilia 2 (GAIT2) project. CONCLUSIONS: Our software lme4qtl enables QTL mapping models with a versatile structure of random effects and efficient computation for sparse covariances. lme4qtl is available at https://github.com/variani/lme4qtl

Integrated genetic and methylomic analyses identify shared biology between autism and autistic traits.

Previous studies have identified differences in DNA methylation in autistic individuals compared to neurotypical individuals. Yet, it is unclear if this extends to autistic traits-subclinical manifestation of autism features in the general population. Here, we investigate the association between DNA methylation at birth (cord blood), and scores on the Social and Communication Disorders Checklist (SCDC), a measure of autistic traits, in 701 8-year-olds, by conducting a methylome-wide association study (MWAS). We did not identify significant CpGs associated with SCDC. The most significant CpG site was cg14379490, on chromosome 9 (MWAS beta = - 1.78 ± 0.35, p value = 5.34 × 10 -7 ). Using methylation data for autism in peripheral tissues, we did not identify a significant concordance in effect direction of CpGs with p value  0.5). In contrast, using methylation data for autism from post-mortem brain tissues, we identify a significant concordance in effect direction of CpGs with a p value < 10-4 in the SCDC MWAS (binomial sign test, p value = 0.004). Supporting this, we observe an enrichment for genes that are dysregulated in the post-mortem autism brain (one-sided Wilcoxon rank-sum test, p value = 6.22 × 10-5). Finally, integrating genome-wide association study (GWAS) data for autism (n = 46,350) with mQTL maps from cord-blood (n = 771), we demonstrate that mQTLs of CpGs associated with SCDC scores at p value thresholds of 0.01 and 0.005 are significantly shifted toward lower p values in the GWAS for autism (p < 5 × 10-3). We provide additional support for this using a GWAS of SCDC, and demonstrate a lack of enrichment in a GWAS of Alzheimer's disease. Our results highlight the shared cross-tissue methylation architecture of autism and autistic traits, and demonstrate that mQTLs associated with differences in DNA methylation associated with childhood autistic traits are enriched for common genetic variants associated with autism and autistic traits.This study was funded by grants from the Medical Research Council, the Wellcome Trust, the Autism Research Trust, and the Templeton World Charity Foundation. The research was conducted in association with the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care East of England at Cambridgeshire and Peterborough NHS Foundation Trust. The UK Medical Research Council and Wellcome (grant ref: 102215/2/13/2) and the University of Bristol provide core support for ALSPAC. This study was supported by grant HD073978 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institute of Environmental Health Sciences, and National Institute of Neurological Disorders and Stroke; and by the Beatrice and Samuel A. Seaver Foundation. We acknowledge iPSYCH and The Lundbeck Foundation for providing samples and funding for the MINERvA dataset. The iPSYCH (The Lundbeck Foundation Initiative for Integrative Psychiatric Research) team acknowledges funding from The Lundbeck Foundation (grant numbers R102-A9118 and R155–2014-1724), the Stanley Medical Research Institute, the European Research Council (project number 294838), the Novo Nordisk Foundation for supporting the Danish National Biobank resource, and grants from Aarhus and Copenhagen Universities and University Hospitals, including support to the iSEQ Center, the GenomeDK HPC facility, and the CIRRAU Center. This research has been conducted using the Danish National Biobank resource, supported by the Novo Nordisk Foundation. The SEED study was supported by Centers for Disease Control and Prevention (CDC) Cooperative Agreements announced under the RFAs 01086, 02199, DD11–002, DD06–003, DD04–001, and DD09–002 and the SEED DNA methylation measurements were supported by Autism Speaks Award #7659 to MDF. The SSC was supported by Simons Foundation (SFARI) award and NIH grant MH089606. The project leading to this application has received funding from the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement No 777394. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA and AUTISM SPEAKS, Autistica, SFARI

Ambient-noise tomography of the wider Vienna Basin region

We present a new 3-D shear-velocity model for the top 30 km of the crust in the wider Vienna Basin region based on surface waves extracted from ambient-noise cross-correlations. We use continuous seismic records of 63 broad-band stations of the AlpArray project to retrieve interstation Green’s functions from ambient-noise cross-correlations in the period range from 5 to 25 s. From these Green’s functions, we measure Rayleigh group traveltimes, utilizing all four components of the cross-correlation tensor, which are associated with Rayleigh waves (ZZ, RR, RZ and ZR), to exploit multiple measurements per station pair. A set of selection criteria is applied to ensure that we use high-quality recordings of fundamental Rayleigh modes. We regionalize the interstation group velocities in a 5 km × 5 km grid with an average path density of ∼20 paths per cell. From the resulting group-velocity maps, we extract local 1-D dispersion curves for each cell and invert all cells independently to retrieve the crustal shear-velocity structure of the study area. The resulting model provides a previously unachieved lateral resolution of seismic velocities in the region of ∼15 km. As major features, we image the Vienna Basin and Little Hungarian Plain as low-velocity anomalies, and the Bohemian Massif with high velocities. The edges of these features are marked with prominent velocity contrasts correlated with faults, such as the Alpine Front and Vienna Basin transfer fault system. The observed structures correlate well with surface geology, gravitational anomalies and the few known crystalline basement depths from boreholes. For depths larger than those reached by boreholes, the new model allows new insight into the complex structure of the Vienna Basin and surrounding areas, including deep low-velocity zones, which we image with previously unachieved detail. This model may be used in the future to interpret the deeper structures and tectonic evolution of the wider Vienna Basin region, evaluate natural resources, model wave propagation and improve earthquake locations, among others

Arrival angles of teleseismic fundamental mode Rayleigh waves across the AlpArray

The dense AlpArray network allows studying seismic wave propagation with high spatial resolution. Here we introduce an array approach to measure arrival angles of teleseismic Rayleigh waves. The approach combines the advantages of phase correlation as in the two-station method with array beamforming to obtain the phase-velocity vector. 20 earthquakes from the first two years of the AlpArray project are selected, and spatial patterns of arrival-angle deviations across the AlpArray are shown in maps, depending on period and earthquake location. The cause of these intriguing spatial patterns is discussed. A simple wave-propagation modelling example using an isolated anomaly and a Gaussian beam solution suggests that much of the complexity can be explained as a result of wave interference after passing a structural anomaly along the wave paths. This indicates that arrival-angle information constitutes useful additional information on the Earth structure, beyond what is currently used in inversions

Shear-wave velocity structure beneath the Dinarides from the inversion of Rayleigh-wave dispersion

Highlights • Rayleigh-wave phase velocity in the wider Dinarides region using the two-station method. • Uppermost mantle shear-wave velocity model of the Dinarides-Adriatic Sea region. • Velocity model reveals a robust high-velocity anomaly present under the whole Dinarides. • High-velocity anomaly reaches depth of 160 km in the northern Dinarides to more than 200 km under southern Dinarides. • New structural model incorporating delamination as one of the processes controlling the continental collision in the Dinarides. The interaction between the Adriatic microplate (Adria) and Eurasia is the main driving factor in the central Mediterranean tectonics. Their interplay has shaped the geodynamics of the whole region and formed several mountain belts including Alps, Dinarides and Apennines. Among these, Dinarides are the least investigated and little is known about the underlying geodynamic processes. There are numerous open questions about the current state of interaction between Adria and Eurasia under the Dinaric domain. One of the most interesting is the nature of lithospheric underthrusting of Adriatic plate, e.g. length of the slab or varying slab disposition along the orogen. Previous investigations have found a low-velocity zone in the uppermost mantle under the northern-central Dinarides which was interpreted as a slab gap. Conversely, several newer studies have indicated the presence of the continuous slab under the Dinarides with no trace of the low velocity zone. Thus, to investigate the Dinaric mantle structure further, we use regional-to-teleseismic surface-wave records from 98 seismic stations in the wider Dinarides region to create a 3D shear-wave velocity model. More precisely, a two-station method is used to extract Rayleigh-wave phase velocity while tomography and 1D inversion of the phase velocity are employed to map the depth dependent shear-wave velocity. Resulting velocity model reveals a robust high-velocity anomaly present under the whole Dinarides, reaching the depths of 160 km in the north to more than 200 km under southern Dinarides. These results do not agree with most of the previous investigations and show continuous underthrusting of the Adriatic lithosphere under Europe along the whole Dinaric region. The geometry of the down-going slab varies from the deeper slab in the north and south to the shallower underthrusting in the center. On-top of both north and south slabs there is a low-velocity wedge indicating lithospheric delamination which could explain the 200 km deep high-velocity body existing under the southern Dinarides

Crustal Thinning From Orogen to Back-Arc Basin: The Structure of the Pannonian Basin Region Revealed by P-to-S Converted Seismic Waves

We present the results of P-to-S receiver function analysis to improve the 3D image of the sedimentary layer, the upper crust, and lower crust in the Pannonian Basin area. The Pannonian Basin hosts deep sedimentary depocentres superimposed on a complex basement structure and it is surrounded by mountain belts. We processed waveforms from 221 three-component broadband seismological stations. As a result of the dense station coverage, we were able to achieve so far unprecedented spatial resolution in determining the velocity structure of the crust. We applied a three-fold quality control process; the first two being applied to the observed waveforms and the third to the calculated radial receiver functions. This work is the first comprehensive receiver function study of the entire region. To prepare the inversions, we performed station-wise H-Vp/Vs grid search, as well as Common Conversion Point migration. Our main focus was then the S-wave velocity structure of the area, which we determined by the Neighborhood Algorithm inversion method at each station, where data were sub-divided into back-azimuthal bundles based on similar Ps delay times. The 1D, nonlinear inversions provided the depth of the discontinuities, shear-wave velocities and Vp/Vs ratios of each layer per bundle, and we calculated uncertainty values for each of these parameters. We then developed a 3D interpolation method based on natural neighbor interpolation to obtain the 3D crustal structure from the local inversion results. We present the sedimentary thickness map, the first Conrad depth map and an improved, detailed Moho map, as well as the first upper and lower crustal thickness maps obtained from receiver function analysis. The velocity jump across the Conrad discontinuity is estimated at less than 0.2 km/s over most of the investigated area. We also compare the new Moho map from our approach to simple grid search results and prior knowledge from other techniques. Our Moho depth map presents local variations in the investigated area: the crust-mantle boundary is at 20–26 km beneath the sedimentary basins, while it is situated deeper below the Apuseni Mountains, Transdanubian and North Hungarian Ranges (28–33 km), and it is the deepest beneath the Eastern Alps and the Southern Carpathians (40–45 km). These values reflect well the Neogene evolution of the region, such as crustal thinning of the Pannonian Basin and orogenic thickening in the neighboring mountain belts

A phase field model for snow crystal growth in three dimensions

International audienceSnowflake growth provides a fascinating example of spontaneous pattern formation in nature. Attempts to understand this phenomenon have led to important insights in non-equilibrium dynamics observed in various active scientific fields, ranging from pattern formation in physical and chemical systems, to self-assembly problems in biology. Yet, very few models currently succeed in reproducing the diversity of snowflake forms in three dimensions, and the link between model parameters and thermodynamic quantities is not established. Here, we report a modified phase field model that describes the subtlety of the ice vapour phase transition, through anisotropic water molecules attachment and condensation, surface diffusion, and strong anisotropic surface tension, that guarantee the anisotropy, faceting and dendritic growth of snowflakes. We demonstrate that this model reproduces the growth dynamics of the most challenging morphologies of snowflakes from the Nakaya diagram. We find that the growth dynamics of snow crystals matches the selection theory, consistently with previous experimental observations