Development of azimuth dependent tropospheric mapping functions, based on a high resolution mesoscale numerical weather model, for GNSS data processing

This thesis is dedicated to the development of two new tropospheric mapping functions for GNSS data processing, based on a high resolution mesoscale numerical weather model (NWM). NWMs have proven to be beneficiary in the processing of GNSS and VLBI data, both for deriving mapping functions and for providing a priori information such as zenith hydrostatic delay (ZHD). The mapping functions derived here make a greater use of the NWM information than the mapping functions currently recommended by the International GNSS Service. In addition to using a single vertical pro¯le at the site in order to derive mapping functions under the assumption of an azimuthally symmetric atmosphere, the NWM was also ray traced every thirty degrees in azimuth. This way, a complete volume of the atmosphere is sensed, and better modelling is expected if the NWM does indeed provide an accurate representation of the atmosphere, by accounting for azimuthal variations. An emphasis was put in this thesis on assessing the mathematical models used to vertically interpolate meteorological information, as they play a key role in computing the refractivities in the ray tracing algorithm. Error sources were identified and quantified. As expected, water vapour is the major source of error. However, the results showed that the model used for the total pressure induced a systematic bias. To derive an azimuth dependent mapping function, the Marini model traditionally used had to be left in favor of a cubic spline interpolation (CSI). This new approach was validated by comparing the performance of the new azimuthally symmetric mapping functions against the updated Vienna mapping functions (VMF1), the best mapping functions currently available. Similar positioning performances were obtained, therefore validating the CSI based approach. The performance of new azimuth dependent mapping functions (AMF) in handling the troposphere asymmetry were compared to those obtained when estimating horizontal tropospheric gradients with an azimuthally symmetric mapping function. Results show a good agreement in the modelling of the asymmetry, and that estimating gradients is justified. The gradient solution performed better overall, although it failed for some sites, and better inter-station consistency was obtained with the AMF. This thesis also investigated the role of the tropospheric modelling in the retrieval of the atmospheric pressure loading (APL) in GNSS data processing, which is now part of the IGS 2008 recommendations. The results show that differential height time series obtained with different tropospheric modelling can correlate with the APL signal to a level up to 0.7. In other words, the choice of tropospheric modelling strategy does greatly influence the retrieval of the APL

Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis

While recent advancements in computation and modelling have improved the analysis of complex traits, our understanding of the genetic basis of the time at symptom onset remains limited. Here, we develop a Bayesian approach (BayesW) that provides probabilistic inference of the genetic architecture of age-at-onset phenotypes in a sampling scheme that facilitates biobank-scale time-to-event analyses. We show in extensive simulation work the benefits BayesW provides in terms of number of discoveries, model performance and genomic prediction. In the UK Biobank, we find many thousands of common genomic regions underlying the age-at-onset of high blood pressure (HBP), cardiac disease (CAD), and type-2 diabetes (T2D), and for the genetic basis of onset reflecting the underlying genetic liability to disease. Age-at-menopause and age-at-menarche are also highly polygenic, but with higher variance contributed by low frequency variants. Genomic prediction into the Estonian Biobank data shows that BayesW gives higher prediction accuracy than other approaches

Examination of Late Palaeolithic archaeological sites in northern Europe for the preservation of cryptotephra layers

We report the first major study of cryptotephra (non-visible volcanic ash layers) on Late Palaeolithic archaeological sites in northern Europe. Examination of 34 sites dating from the Last Termination reveals seven with identifiable cryptotephra layers. Preservation is observed in minerogenic and organic deposits, although tephra is more common in organic sediments. Cryptotephra layers normally occur stratigraphically above or below the archaeology. Nearby off-site palaeoclimate archives (peat bogs and lakes <0.3 km distant) were better locations for detecting tephra. However in most cases the archaeology can only be correlated indirectly with such cryptotephras. Patterns affecting the presence/absence of cryptotephra include geographic position of sites relative to the emitting volcanic centre; the influence of past atmospherics on the quantity, direction and patterns of cryptotephra transport; the nature and timing of local site sedimentation; sampling considerations and subsequent taphonomic processes. Overall, while tephrostratigraphy has the potential to improve significantly the chronology of such sites many limiting factors currently impacts the successful application

Development of azimuth dependent tropospheric mapping functions, based on a high resolution mesoscale numerical weather model, for GNSS data processing

This thesis is dedicated to the development of two new tropospheric mapping functions for GNSS data processing, based on a high resolution mesoscale numerical weather model (NWM). NWMs have proven to be beneficiary in the processing of GNSS and VLBI data, both for deriving mapping functions and for providing a priori information such as zenith hydrostatic delay (ZHD). The mapping functions derived here make a greater use of the NWM information than the mapping functions currently recommended by the International GNSS Service. In addition to using a single vertical pro¯le at the site in order to derive mapping functions under the assumption of an azimuthally symmetric atmosphere, the NWM was also ray traced every thirty degrees in azimuth. This way, a complete volume of the atmosphere is sensed, and better modelling is expected if the NWM does indeed provide an accurate representation of the atmosphere, by accounting for azimuthal variations. An emphasis was put in this thesis on assessing the mathematical models used to vertically interpolate meteorological information, as they play a key role in computing the refractivities in the ray tracing algorithm. Error sources were identified and quantified. As expected, water vapour is the major source of error. However, the results showed that the model used for the total pressure induced a systematic bias. To derive an azimuth dependent mapping function, the Marini model traditionally used had to be left in favor of a cubic spline interpolation (CSI). This new approach was validated by comparing the performance of the new azimuthally symmetric mapping functions against the updated Vienna mapping functions (VMF1), the best mapping functions currently available. Similar positioning performances were obtained, therefore validating the CSI based approach. The performance of new azimuth dependent mapping functions (AMF) in handling the troposphere asymmetry were compared to those obtained when estimating horizontal tropospheric gradients with an azimuthally symmetric mapping function. Results show a good agreement in the modelling of the asymmetry, and that estimating gradients is justified. The gradient solution performed better overall, although it failed for some sites, and better inter-station consistency was obtained with the AMF. This thesis also investigated the role of the tropospheric modelling in the retrieval of the atmospheric pressure loading (APL) in GNSS data processing, which is now part of the IGS 2008 recommendations. The results show that differential height time series obtained with different tropospheric modelling can correlate with the APL signal to a level up to 0.7. In other words, the choice of tropospheric modelling strategy does greatly influence the retrieval of the APL.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Development of azimuth dependent tropospheric mapping functions, based on a high resolution mesoscale numerical weather model, for GNSS data processing

This thesis is dedicated to the development of two new tropospheric mapping functions for GNSS data processing, based on a high resolution mesoscale numerical weather model (NWM). NWMs have proven to be beneficiary in the processing of GNSS and VLBI data, both for deriving mapping functions and for providing a priori information such as zenith hydrostatic delay (ZHD). The mapping functions derived here make a greater use of the NWM information than the mapping functions currently recommended by the International GNSS Service. In addition to using a single vertical pro¯le at the site in order to derive mapping functions under the assumption of an azimuthally symmetric atmosphere, the NWM was also ray traced every thirty degrees in azimuth. This way, a complete volume of the atmosphere is sensed, and better modelling is expected if the NWM does indeed provide an accurate representation of the atmosphere, by accounting for azimuthal variations. An emphasis was put in this thesis on assessing the mathematical models used to vertically interpolate meteorological information, as they play a key role in computing the refractivities in the ray tracing algorithm. Error sources were identified and quantified. As expected, water vapour is the major source of error. However, the results showed that the model used for the total pressure induced a systematic bias. To derive an azimuth dependent mapping function, the Marini model traditionally used had to be left in favor of a cubic spline interpolation (CSI). This new approach was validated by comparing the performance of the new azimuthally symmetric mapping functions against the updated Vienna mapping functions (VMF1), the best mapping functions currently available. Similar positioning performances were obtained, therefore validating the CSI based approach. The performance of new azimuth dependent mapping functions (AMF) in handling the troposphere asymmetry were compared to those obtained when estimating horizontal tropospheric gradients with an azimuthally symmetric mapping function. Results show a good agreement in the modelling of the asymmetry, and that estimating gradients is justified. The gradient solution performed better overall, although it failed for some sites, and better inter-station consistency was obtained with the AMF. This thesis also investigated the role of the tropospheric modelling in the retrieval of the atmospheric pressure loading (APL) in GNSS data processing, which is now part of the IGS 2008 recommendations. The results show that differential height time series obtained with different tropospheric modelling can correlate with the APL signal to a level up to 0.7. In other words, the choice of tropospheric modelling strategy does greatly influence the retrieval of the APL

Improving GWAS discovery and genomic prediction accuracy in biobank data

Genetically informed, deep-phenotyped biobanks are an important research resource and it is imperative that the most powerful, versatile, and efficient analysis approaches are used. Here, we apply our recently developed Bayesian grouped mixture of regressions model (GMRM) in the UK and Estonian Biobanks and obtain the highest genomic prediction accuracy reported to date across 21 heritable traits. When compared to other approaches, GMRM accuracy was greater than annotation prediction models run in the LDAK or LDPred-funct software by 15% (SE 7%) and 14% (SE 2%), respectively, and was 18% (SE 3%) greater than a baseline BayesR model without single-nucleotide polymorphism (SNP) markers grouped into minor allele frequency–linkage disequilibrium (MAF-LD) annotation categories. For height, the prediction accuracy R2 was 47% in a UK Biobank holdout sample, which was 76% of the estimated h2SNP. We then extend our GMRM prediction model to provide mixed-linear model association (MLMA) SNP marker estimates for genome-wide association (GWAS) discovery, which increased the independent loci detected to 16,162 in unrelated UK Biobank individuals, compared to 10,550 from BoltLMM and 10,095 from Regenie, a 62 and 65% increase, respectively. The average χ2 value of the leading markers increased by 15.24 (SE 0.41) for every 1% increase in prediction accuracy gained over a baseline BayesR model across the traits. Thus, we show that modeling genetic associations accounting for MAF and LD differences among SNP markers, and incorporating prior knowledge of genomic function, is important for both genomic prediction and discovery in large-scale individual-level studies

Improving genome-wide association discovery and genomic prediction accuracy in biobank data

Genetically informed, deep-phenotyped biobanks are an important research resource and it is imperative that the most powerful, versatile, and efficient analysis approaches are used. Here, we apply our recently developed Bayesian grouped mixture of regressions model (GMRM) in the UK and Estonian Biobanks and obtain the highest genomic prediction accuracy reported to date across 21 heritable traits. When compared to other approaches, GMRM accuracy was greater than annotation prediction models run in the LDAK or LDPred-funct software by 15% (SE 7%) and 14% (SE 2%), respectively, and was 18% (SE 3%) greater than a baseline BayesR model without single-nucleotide polymorphism (SNP) markers grouped into minor allele frequency–linkage disequilibrium (MAF-LD) annotation categories. For height, the prediction accuracy R 2 was 47% in a UK Biobank holdout sample, which was 76% of the estimated h SNP 2 . We then extend our GMRM prediction model to provide mixed-linear model association (MLMA) SNP marker estimates for genome-wide association (GWAS) discovery, which increased the independent loci detected to 16,162 in unrelated UK Biobank individuals, compared to 10,550 from BoltLMM and 10,095 from Regenie, a 62 and 65% increase, respectively. The average χ2 value of the leading markers increased by 15.24 (SE 0.41) for every 1% increase in prediction accuracy gained over a baseline BayesR model across the traits. Thus, we show that modeling genetic associations accounting for MAF and LD differences among SNP markers, and incorporating prior knowledge of genomic function, is important for both genomic prediction and discovery in large-scale individual-level studies

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

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