Funktiomuotoisen heritabiliteetin estimointi bayesiläisillä malleilla

Tiivistelmä. Heritabiliteetti eli perinnöllisyysaste on perinnöllisyystieteen tunnusluku, joka mittaa sitä, kuinka suuri osa jonkin piirteen vaihtelusta tietyssä biologisessa populaatiossa riippuu perintötekijöistä. Sen avulla voidaan muun muassa selvittää, kuinka nopeasti valinnalla voidaan muuttaa piirteen keskiarvoa tulevissa sukupolvissa. Heritabiliteetti ei välttämättä ole vakio, vaan se voi olla ajan funktio. Kirjallisuudessa ei kuitenkaan ole kovin paljon esimerkkejä heritabiliteettifunktion estimoinnista. Tässä työssä esitellään kaksi bayesiläistä menetelmää funktiomuotoisen heritabiliteetin estimointiin. Ensimmäisessä, kaksivaiheisessa menetelmässä heritabiliteetti ja sen luottamusväli estimoidaan jokaisessa aikapisteessä erikseen lineaarista sekamallia ja bootstrap-menetelmää apuna käyttäen, jonka jälkeen muodostuneet aikasarjat silotetaan. Toisessa menetelmässä sekamalli on ikään kuin yleistetty pitkittäisaineistotilanteeseen, jolloin heritabiliteetti saadaan estimoitua jokaisessa aikapisteessä samaan aikaan yksivaiheisesti sopivien silottavien priorien avulla. Tutkimuksessa havaittiin, että yksivaiheisessa menetelmässä estimaatit ovat melko täsmällisiä estimoitaville parametreille asetettavien priorien takia, koska ne muodostavat eri aikapisteiden välille tietynlaisen korrelaatiorakenteen. Toisaalta tämä estimointitapa on kuitenkin laskennallisesti raskas. Kaksivaiheisessa menetelmässä estimaattien luottamusvälit ovat leveämmät, mutta menetelmä ei ole läheskään niin aikaavievä kuin yksivaiheinen estimointi

Neural Network Kalman Filtering for 3-D Object Tracking From Linear Array Ultrasound Data

Many interventional surgical procedures rely on medical imaging to visualise and track instruments. Such imaging methods not only need to be real-time capable, but also provide accurate and robust positional information. In ultrasound applications, typically only two-dimensional data from a linear array are available, and as such obtaining accurate positional estimation in three dimensions is non-trivial. In this work, we first train a neural network, using realistic synthetic training data, to estimate the out-of-plane offset of an object with the associated axial aberration in the reconstructed ultrasound image. The obtained estimate is then combined with a Kalman filtering approach that utilises positioning estimates obtained in previous time-frames to improve localisation robustness and reduce the impact of measurement noise. The accuracy of the proposed method is evaluated using simulations, and its practical applicability is demonstrated on experimental data obtained using a novel optical ultrasound imaging setup. Accurate and robust positional information is provided in real-time. Axial and lateral coordinates for out-of-plane objects are estimated with a mean error of 0.1mm for simulated data and a mean error of 0.2mm for experimental data. Three-dimensional localisation is most accurate for elevational distances larger than 1mm, with a maximum distance of 6mm considered for a 25mm aperture

Estimation of dynamic SNP-heritability with Bayesian Gaussian process models

Abstract Motivation: Improved DNA technology has made it practical to estimate single nucleotide polymorphism (SNP)-heritability among distantly related individuals with unknown relationships. For growth and development related traits, it is meaningful to base SNP-heritability estimation on longitudinal data due to the time-dependency of the process. However, only few statistical methods have been developed so far for estimating dynamic SNP-heritability and quantifying its full uncertainty. Results: We introduce a completely tuning-free Bayesian Gaussian process (GP) based approach for estimating dynamic variance components and heritability as their function. For parameter estimation, we use a modern Markov Chain Monte Carlo (MCMC) method which allows full uncertainty quantification. Several data sets are analysed and our results clearly illustrate that the 95 % credible intervals of the proposed joint estimation method (which "borrows strength" from adjacent time points) are significantly narrower than of a two-stage baseline method that first estimates the variance components at each time point independently and then performs smoothing. We compare the method with a random regression model using MTG2 and BLUPF90 softwares and quantitative measures indicate superior performance of our method. Results are presented for simulated and real data with up to 1000 time points. Finally, we demonstrate scalability of the proposed method for simulated data with tens of thousands of individuals. Availability: The C++ implementation dynBGP and simulated data are available in GitHub (https://github.com/aarjas/dynBGP). The programs can be run in R. Real datasets are available in QTL archive (https://phenome.jax.org/centers/QTLA). Conclusions

Hierarchical deconvolution for incoherent scatter radar data

Abstract We propose a novel method for deconvolving incoherent scatter radar data to recover accurate reconstructions of backscattered powers. The problem is modelled as a hierarchical noise-perturbed deconvolution problem, where the lower hierarchy consists of an adaptive length-scale function that allows for a non-stationary prior and as such enables adaptive recovery of smooth and narrow layers in the profiles. The estimation is done in a Bayesian statistical inversion framework as a two-step procedure, where hyperparameters are first estimated by optimisation and followed by an analytical closed-form solution of the deconvolved signal. The proposed optimisation-based method is compared to a fully probabilistic approach using Markov chain Monte Carlo techniques enabling additional uncertainty quantification. In this paper we examine the potential of the hierarchical deconvolution approach using two different prior models for the length-scale function. We apply the developed methodology to compute the backscattered powers of measured polar mesospheric winter echoes, as well as summer echoes, from the EISCAT VHF radar in Tromsø, Norway. Computational accuracy and performance are tested using a simulated signal corresponding to a typical background ionosphere and a sporadic E layer with known ground truth. The results suggest that the proposed hierarchical deconvolution approach can recover accurate and clean reconstructions of profiles, and the potential to be successfully applied to similar problems

Neural network Kalman filtering for 3D object tracking from linear array ultrasound data

Abstract Many interventional surgical procedures rely on medical imaging to visualise and track instruments. Such imaging methods not only need to be real-time capable, but also provide accurate and robust positional information. In ultrasound applications, typically only two-dimensional data from a linear array are available, and as such obtaining accurate positional estimation in three dimensions is non-trivial. In this work, we first train a neural network, using realistic synthetic training data, to estimate the out-of-plane offset of an object with the associated axial aberration in the reconstructed ultrasound image. The obtained estimate is then combined with a Kalman filtering approach that utilises positioning estimates obtained in previous time-frames to improve localisation robustness and reduce the impact of measurement noise. The accuracy of the proposed method is evaluated using simulations, and its practical applicability is demonstrated on experimental data obtained using a novel optical ultrasound imaging setup. Accurate and robust positional information is provided in real-time. Axial and lateral coordinates for out-of-plane objects are estimated with a mean error of 0.1mm for simulated data and a mean error of 0.2mm for experimental data. Three-dimensional localisation is most accurate for elevational distances larger than 1mm, with a maximum distance of 6mm considered for a 25mm aperture

Neural Network Kalman filtering for 3D object tracking from linear array ultrasound data

Abstract Many interventional surgical procedures rely on medical imaging to visualise and track instruments. Such imaging methods not only need to be real-time capable, but also provide accurate and robust positional information. In ultrasound applications, typically only two-dimensional data from a linear array are available, and as such obtaining accurate positional estimation in three dimensions is non-trivial. In this work, we first train a neural network, using realistic synthetic training data, to estimate the out-of-plane offset of an object with the associated axial aberration in the reconstructed ultrasound image. The obtained estimate is then combined with a Kalman filtering approach that utilises positioning estimates obtained in previous time-frames to improve localisation robustness and reduce the impact of measurement noise. The accuracy of the proposed method is evaluated using simulations, and its practical applicability is demonstrated on experimental data obtained using a novel optical ultrasound imaging setup. Accurate and robust positional information is provided in real-time. Axial and lateral coordinates for out-of-plane objects are estimated with a mean error of 0.1mm for simulated data and a mean error of 0.2mm for experimental data. Three-dimensional localisation is most accurate for elevational distances larger than 1mm, with a maximum distance of 6mm considered for a 25mm aperture