Bayesian Stochastic Search Variable Selection Research Articles

The Genomic Evolution and the Transmission Dynamics of H6N2 Avian Influenza A Viruses in Southern China

In China, the broad prevalence of H6 subtype influenza viruses, increasingly detected in aquatic birds, promotes their exchange materials with other highly pathogenic human-infecting H5N1, H5N6, and H7N9 influenza viruses. Strikingly, some H6 subtype viruses can infect pigs, dogs, and humans, posing risks to public health. In this study, 9 H6N2 viruses recovered from waterfowl species in the Guangdong province of China in 2018 were isolated and sequenced. Phylogenetic analysis revealed that the genome sequences of these H6N2 viruses belonged to Group I, except for the NP gene in Group III. Coalescent analyses demonstrated that the reassortment of NA and NS genes have occurred in two independent clusters, suggesting H6 subtype viruses had been undergoing a complex reassortant. To examine the evolutionary dynamics and the dissemination of the H6 subtype viruses, a Bayesian stochastic search variable selection was performed for results showing higher viral migration rates between closer provinces, including Guangdong, Jiangxi, Guangxi, and Fujian. Notably, the transmission routes of the H6 subtype viruses were concentrated in Jiangxi Province, the most frequent location for input and output transmission and a region containing Poyang Lake, a well-known wintering site for migration birds. We also found that the aquatic birds, especially ducks, were the most common input source of the viral transmission. In addition, we also found that eight positively selected amino acid sites were identified in HA protein. Given their continuous dissemination and the broad prevalence of the H6 subtype influenza viruses, continued surveillance is warranted in the future.

Dating the origin and dispersal of global hepatitis B virus genotype C in humans.

Hepatitis B virus genotype C (HBV/C) is one of the most prevalent HBV strains worldwide, especially in the Western Pacific and the South-East Asia. However, the origin and evolutionary timescale of HBV/C remains largely unresolved. We analyzed the evolutionary rate and molecular clock phylogeny of 101 full-genome HBV/C sequences sampled globally using a Bayesian Markov Chain Monte Carlo (MCMC) approach. We inferred the spatiotemporal dynamics of the HBV/C worldwide by the Bayesian Stochastic Search Variable Selection (BSSVS). We found that the estimated mean evolution rate of the HBV/C genotype full-genome was 4.32 × 10-5 subs/site/year (95% highest posterior density 3.02 × 10-6 - 8.97 × 10-5). Phylogeographic reconstruction was able to identify a single location for the origin of the global HBV/C in Australia around A.D. 715. The subgenotype C4 diverged earliest and mainly circulated in Australia, C1 mainly in Southeast Asia, C2 mainly in East Asia and C3 in Remote Oceania. The effective number of HBV infection presented a rapid exponential increase between the 1760s and 1860s followed by a maintained high level until now. Our study, for the first time, provides an estimated timescale for the HBV/C epidemic, and brings new insight to the dispersal of HBV/C in humans globally. Based on the continuous presence of a highly effective viral population, this study provides further evidence of the challenge from a population-based molecular level to eliminate HBV by 2030, and calls for a concerted effort from policy makers, health providers, and society in the globalized world.

Genetic analyses reveal regional structure and demographic expansion of the predominant tea pest Empoasca onukii (Hemiptera: Cicadellidae) in China.

The tea green leafhopper, Empoasca onukii Matsuda, is the most destructive insect pest of tea plantations in East Asia. Despite its economic importance and previous studies on this species, it remains unclear as to how this small-sized pest can have such wide range. By sequencing three mitochondrial genes and 17 microsatellite loci, we revealed the regional structure and demographic expansion of 59 E. onukii populations in China. Bayesian analysis of population genetic structure (BAPS) on microsatellites identified four genetic groups with spatial discontinuities, while analysis on mitochondrial genes inferred five nested and differentiated clusters. Both the Mantel test and the generalized linear model indicated a significant pattern of isolation by geographic distance in E. onukii populations. Based on the approximate Bayesian computation approach, E. onukii was found to have originated from southwestern China and expanded northward and eastward. While MIGRATE-N and Bayesian stochastic search variable selection (BSSVS) procedure in BEAST confirmed the possible eastward and northward dispersal from Yunnan, they also detected more gene flow from the derived populations in central and southeastern China. Our results suggest that the current distribution and structure of E. onukii is complicatedly influenced by human activities of cultivation, wide dissemination of tea in ancient China as well as recent transportation of tea seedlings for establishing new tea plantations. Insights into genetic differentiation and demographic expansion patterns from this study provide an important basis for the development of area-wide management of the E. onukii populations. © 2022 Society of Chemical Industry.

Molecular Phylogenesis and Spatiotemporal Spread of SARS-CoV-2 in Southeast Asia.

Background: The ongoing coronavirus disease 2019 (COVID-19) pandemic has posed an unprecedented challenge to public health in Southeast Asia, a tropical region with limited resources. This study aimed to investigate the evolutionary dynamics and spatiotemporal patterns of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the region.Materials and Methods: A total of 1491 complete SARS-CoV-2 genome sequences from 10 Southeast Asian countries were downloaded from the Global Initiative on Sharing Avian Influenza Data (GISAID) database on November 17, 2020. The evolutionary relationships were assessed using maximum likelihood (ML) and time-scaled Bayesian phylogenetic analyses, and the phylogenetic clustering was tested using principal component analysis (PCA). The spatial patterns of SARS-CoV-2 spread within Southeast Asia were inferred using the Bayesian stochastic search variable selection (BSSVS) model. The effective population size (Ne) trajectory was inferred using the Bayesian Skygrid model.Results: Four major clades (including one potentially endemic) were identified based on the maximum clade credibility (MCC) tree. Similar clustering was yielded by PCA; the first three PCs explained 46.9% of the total genomic variations among the samples. The time to the most recent common ancestor (tMRCA) and the evolutionary rate of SARS-CoV-2 circulating in Southeast Asia were estimated to be November 28, 2019 (September 7, 2019 to January 4, 2020) and 1.446 × 10−3 (1.292 × 10−3 to 1.613 × 10−3) substitutions per site per year, respectively. Singapore and Thailand were the two most probable root positions, with posterior probabilities of 0.549 and 0.413, respectively. There were high-support transmission links (Bayes factors exceeding 1,000) in Singapore, Malaysia, and Indonesia; Malaysia involved the highest number (7) of inferred transmission links within the region. A twice-accelerated viral population expansion, followed by a temporary setback, was inferred during the early stages of the pandemic in Southeast Asia.Conclusions: With available genomic data, we illustrate the phylogeography and phylodynamics of SARS-CoV-2 circulating in Southeast Asia. Continuous genomic surveillance and enhanced strategic collaboration should be listed as priorities to curb the pandemic, especially for regional communities dominated by developing countries.

Phylogeographic analysis of Pseudogymnoascus destructans partitivirus-pa explains the spread dynamics of white-nose syndrome in North America.

Understanding the dynamics of white-nose syndrome spread in time and space is an important component for the disease epidemiology and control. We reported earlier that a novel partitivirus, Pseudogymnoascus destructans partitivirus-pa, had infected the North American isolates of Pseudogymnoascus destructans, the fungal pathogen that causes white-nose syndrome in bats. We showed that the diversity of the viral coat protein sequences is correlated to their geographical origin. Here we hypothesize that the geographical adaptation of the virus could be used as a proxy to characterize the spread of white-nose syndrome. We used over 100 virus isolates from diverse locations in North America and applied the phylogeographic analysis tool BEAST to characterize the spread of the disease. The strict clock phylogeographic analysis under the coalescent model in BEAST showed a patchy spread pattern of white-nose syndrome driven from a few source locations including Connecticut, New York, West Virginia, and Kentucky. The source states had significant support in the maximum clade credibility tree and Bayesian stochastic search variable selection analysis. Although the geographic origin of the virus is not definite, it is likely the virus infected the fungus prior to the spread of white-nose syndrome in North America. We also inferred from the BEAST analysis that the recent long-distance spread of the fungus to Washington had its root in Kentucky, likely from the Mammoth cave area and most probably mediated by a human. The time to the most recent common ancestor of the virus is estimated somewhere between the late 1990s to early 2000s. We found the mean substitution rate of 2 X 10−3 substitutions per site per year for the virus which is higher than expected given the persistent lifestyle of the virus, and the stamping-machine mode of replication. Our approach of using the virus as a proxy to understand the spread of white-nose syndrome could be an important tool for the study and management of other infectious diseases.

Genetic diversity and evolution of Hantaan virus in China and its neighbors.

Hantaan virus (HTNV; family Hantaviridae, order Bunyavirales) causes hemorrhagic fever with renal syndrome (HFRS), which has raised serious concerns in Eurasia, especially in China, Russia, and South Korea. Previous studies reported genetic diversity and phylogenetic features of HTNV in different parts of China, but the analyses from the holistic perspective are rare. To better understand HTNV genetic diversity and gene evolution, we analyzed all available complete sequences derived from the small (S) and medium (M) segments with bioinformatic tools. Eleven phylogenetic groups were defined and showed geographic clustering; 42 significant amino acid variant sites were found, and 19 of them were located in immune epitopes; nine recombinant events and eight reassortments with highly divergent sequences were found and analyzed. We found that sequences from Guizhou showed high genetic divergence, contributing to multiple lineages of the phylogenetic tree and also to the recombination and reassortment events. Bayesian stochastic search variable selection analysis revealed that Heilongjiang, Shaanxi, and Guizhou played important roles in HTNV evolution and migration; the virus may originate from Zhejiang Province in the eastern part of China; and the virus population size expanded from the 1980s to 1990s. These findings revealed the original and evolutionary features of HTNV, which will help to illustrate hantavirus epidemic trends, thus aiding in disease control and prevention.

Associations and contribution of childhood diseases to fever risk among children less than five years in Uganda

Background Despite massive investment in diseases responsible for fever among children less than five years in Uganda, the burden of the fever symptom remains high and disproportionately distributed across regions. In Uganda, studies assessing the relationship between fever prevalence and multiple childhood diseases, especially at a local scale are scanty. We analyse the 2016 Demographic and Health Survey (DHS) data to quantify the associations and contribution of childhood diseases to fever prevalence among children less than five years in Uganda at the national and regional level. Findings can lead to the development and implementation of preventive and treatment measures especially at a local scale to minimize fever-related diseases among the under-fives in Uganda. Methods Bayesian geostatistical logistic regression models with spatially varying coefficients were fitted to determine the associations and contribution of childhood diseases on fever prevalence at the national and regional levels. Region-specific spatially varying coefficients were modeled by a conditional autoregressive distribution. Cluster-specific random effects were introduced into the model to take into account spatial dependence in fever prevalence. Bayesian geostatistical stochastic search variable selection was applied to determine the most important predictors in explaining variation in fever prevalence. The contribution of childhood diseases to fever prevalence was estimated using population attributable fractions. Results The prevalence of fever was highest in Busoga and Teso regions and lowest in Bunyoro region. At the national level, the population attribution fraction of diarrhoea, acute respiratory infections (ARI) and malaria to the prevalence of fever in the under-five was 38.12 (95% Bayesian credible intervals, BCI=25.15-41.59), 30.99 (95% BCI=9.82-34.26) and 9.50 (95% BCI=2.34-25.15), respectively. The attribution of diarrhoea was common in all regions except Bunyoro, while ARI was more common in Bugisu, Karamoja and West Nile, and malaria was commonest in Bunyoro. In Lango, the attribution of diarrhoea and ARI was similar. Conclusions Majority of fevers among the under-five are due to diarrhoea, followed by ARI. Hand washing with soap and water/detergent should be strengthened in all regions. Vaccination against ARI should be encouraged, in the regions of Central 2, Bugisu, Teso, Karamoja, Lango, West-Nile and Tooro. The health system should be reinforced to treat diarrhoea and ARI.

Phylogeny and Biogeography of the Amazonian Pachyptera (Bignonieae, Bignoniaceae)

Abstract—The Amazon houses a large proportion of the overall biodiversity currently found on Earth. Despite that, our knowledge of Amazonian biodiversity is still limited. In this study, we reconstruct the phylogeny of Pachyptera (Bignoniaceae), a genus of neotropical lianas that is centered in the Amazon. We then use this phylogenetic framework to re-evaluate species limits and study the biogeographic history of the genus. We sampled three molecular markers (i.e. ndhF, rpl32-trnL, and PepC) and 51 individuals representing the breadth of morphological variation and geographic distribution of all species recognized in the genus. We used this information to reconstruct phylogenetic relationships among individuals of Pachyptera using Bayesian and maximum likelihood approaches. The resulting molecular phylogeny was used as a basis to test species limits within the P. kerere species complex using a cpDNA coalescent approach (GMYC). GMYC identified five potential species within the P. kerere species complex that were subsequently evaluated in the light of morphology. Morphological data supported the recognition of four of the five potential species suggested by GMYC, all of which were also supported by a multispecies coalescent model in a Bayesian framework. The phylogeny of Pachyptera was time-calibrated and used to reconstruct the biogeographical history of the genus. We identified historically important migration pathways using our comprehensive cpDNA dataset and a Bayesian stochastic search variable selection (BSSVS) framework. Our results indicate that the genus originated in lowland Amazonia during the Middle Eocene, and subsequently occupied Central America and the Andes. Most of the diversification of Pachyptera occurred in the Miocene, a period of intense perturbations in South America.

Multibreed genomic prediction using multitrait genomic residual maximum likelihood and multitask Bayesian variable selection

Genomic prediction is applicable to individuals of different breeds. Empirical results to date, however, show limited benefits in using information on multiple breeds in the context of genomic prediction. We investigated a multitask Bayesian model, presented previously by others, implemented in a Bayesian stochastic search variable selection (BSSVS) model. This model allowed for evidence of quantitative trait loci (QTL) to be accumulated across breeds or for both QTL that segregate across breeds and breed-specific QTL. In both cases, single nucleotide polymorphism effects were estimated with information from a single breed. Other models considered were a single-trait and multitrait genomic residual maximum likelihood (GREML) model, with breeds considered as different traits, and a single-trait BSSVS model. All single-trait models were applied to each of the 2 breeds separately and to the pooled data of both breeds. The data used included a training data set of 6,278 Holstein and 722 Jersey bulls, as well as 374 Jersey validation bulls. All animals had genotypes for 474,773 single nucleotide polymorphisms after editing and phenotypes for milk, fat, and protein yields. Using the same training data, BSSVS consistently outperformed GREML. The multitask BSSVS, however, did not outperform single-trait BSSVS, which used pooled Holstein and Jersey data for training. Thus, the rigorous assumption that the traits are the same in both breeds yielded a slightly better prediction than a model that had to estimate the correlation between the breeds from the data. Adding the Holstein data significantly increased the accuracy of the single-trait GREML and BSSVS in predicting the Jerseys for milk and protein, in line with estimated correlations between the breeds of 0.66 and 0.47 for milk and protein yields, whereas only the BSSVS model significantly improved the accuracy for fat yield with an estimated correlation between breeds of only 0.05. The relatively high genetic correlations for milk and protein yields, and the superiority of the pooling strategy, is likely the result of the observed admixture between both breeds in our data. The Bayesian model was able to detect several QTL in Holsteins, which likely enabled it to outperform GREML. The inability of the multitask Bayesian models to outperform a simple pooling strategy may be explained by the fact that the pooling strategy assumes equal effects in both breeds; furthermore, this assumption may be valid for moderate- to large-sized QTL, which are important for multibreed genomic prediction.

Bayesian variable selection and coefficient estimation in heteroscedastic linear regression model

ABSTRACTIn many real applications, such as econometrics, biological sciences, radio-immunoassay, finance, and medicine, the usual assumption of constant error variance may be unrealistic. Ignoring heteroscedasticity (non-constant error variance), if it is present in the data, may lead to incorrect inferences and inefficient estimation. In this paper, a simple and effcient Gibbs sampling algorithm is proposed, based on a heteroscedastic linear regression model with an penalty. Then, a Bayesian stochastic search variable selection method is proposed for subset selection. Simulations and real data examples are used to compare the performance of the proposed methods with other existing methods. The results indicate that the proposal performs well in the simulations and real data examples. R code is available upon request.

House Prices and Monetary Policy in the Euro Area: Evidence from Structural VARs

We use a structural Bayesian stochastic search variable selection VAR model to investigate the heterogeneous effects of housing demand shocks on economic and lending activities and the role of house prices in the monetary policy transmission across euro area countries, as well as the United Kingdom and the United States. A novel identification scheme which combines zero and sign restrictions based on benchmark models is proposed. We document significant asymmetries in the responses of house prices to monetary policy across euro area countries. We show that house prices and loans are more sensitive to changes in monetary policy in the euro area, characterised by bankcentric financial systems, than in the United States where the financial system is more market-oriented. House prices also play an important role in the availability of credit to households but a limited role in driving consumption in the euro area consistently with the restricted use of mortgage equity withdrawal. Overall, the housing wealth effect seems to be related to the development of the mortgage system.

Bayesian phylogeography of influenza A/H3N2 for the 2014-15 season in the United States using three frameworks of ancestral state reconstruction.

Ancestral state reconstructions in Bayesian phylogeography of virus pandemics have been improved by utilizing a Bayesian stochastic search variable selection (BSSVS) framework. Recently, this framework has been extended to model the transition rate matrix between discrete states as a generalized linear model (GLM) of genetic, geographic, demographic, and environmental predictors of interest to the virus and incorporating BSSVS to estimate the posterior inclusion probabilities of each predictor. Although the latter appears to enhance the biological validity of ancestral state reconstruction, there has yet to be a comparison of phylogenies created by the two methods. In this paper, we compare these two methods, while also using a primitive method without BSSVS, and highlight the differences in phylogenies created by each. We test six coalescent priors and six random sequence samples of H3N2 influenza during the 2014–15 flu season in the U.S. We show that the GLMs yield significantly greater root state posterior probabilities than the two alternative methods under five of the six priors, and significantly greater Kullback-Leibler divergence values than the two alternative methods under all priors. Furthermore, the GLMs strongly implicate temperature and precipitation as driving forces of this flu season and nearly unanimously identified a single root state, which exhibits the most tropical climate during a typical flu season in the U.S. The GLM, however, appears to be highly susceptible to sampling bias compared with the other methods, which casts doubt on whether its reconstructions should be favored over those created by alternate methods. We report that a BSSVS approach with a Poisson prior demonstrates less bias toward sample size under certain conditions than the GLMs or primitive models, and believe that the connection between reconstruction method and sampling bias warrants further investigation.

House Prices and Monetary Policy in the Euro Area: Evidence from Structural VARs

We use a Bayesian stochastic search variable selection structural VAR model to investigate the heterogeneous impact of housing demand shocks on the macroeconomy and the role of house prices in the monetary policy transmission, across euro area countries. A novel set of identification restrictions, which combines zero and sign restrictions, is proposed. By exploiting the cross-sectional dimension of our data, we explore the differences in the propagation channels of house prices and monetary policy and the challenges they pose in the process of real and nominal convergence in the Eurozone. Among the main results, we find a comparatively stronger housing wealth effect on consumption in Ireland and Spain. We provide new evidence in support of the financial accelerator hypothesis, showing that house prices play an important role in the availability of loans. A significant and highly heterogeneous effect of monetary policy on house price dynamics is also documented. JEL Classification: C22, E21, E31, E44, E52

Research on spatiotemporal spread of avian influenza A(H7N9) virus based on discrete geographic information

Objective: To establish a dynamic spatiotemporal spread modes of influenza A (H7N9) virus by using discrete geographic information and explore the spatiotemporal transmission of the virus. Methods: The gene sequences of H7N9 virus isolated from human in China, which were available from Global Initiative on Sharing Avian Influenza Data (GISAID), were used in alignment by using software BioEdit 7.0. Spatiotemporal spread model of H7N9 virus was established in a Bayesian statistical framework by using software BEAST 1.8.2. The symmetric substitution model and Bayesian stochastic search variable selection (BSSVS) were used to infer and verify the historical transmission route of H7N9 virus. Finally the spatiotemporal transmission route was presented by Google Earth software. Results: The transmission of avian influenza A (H7N9) virus originated in Shanghai and Hangzhou, and can be dated back to October 2012. In March and April 2013, it began to spread to the neighboring provinces. The transmission speed up in August and September and affected more than ten geographic positions within 3 months. Conclusion: Based on gene sequences and spatial geographic information, the transmission route of H7N9 virus was traced, which would support the avian influenza prevention and control as well as avian influenza virus tracing.

Spatial mapping and prediction of Plasmodium falciparum infection risk among school-aged children in Côte d’Ivoire

BackgroundIn Côte d’Ivoire, malaria remains a major public health issue, and thus a priority to be tackled. The aim of this study was to identify spatially explicit indicators of Plasmodium falciparum infection among school-aged children and to undertake a model-based spatial prediction of P. falciparum infection risk using environmental predictors.MethodsA cross-sectional survey was conducted, including parasitological examinations and interviews with more than 5,000 children from 93 schools across Côte d’Ivoire. A finger-prick blood sample was obtained from each child to determine Plasmodium species-specific infection and parasitaemia using Giemsa-stained thick and thin blood films. Household socioeconomic status was assessed through asset ownership and household characteristics. Children were interviewed for preventive measures against malaria. Environmental data were gathered from satellite images and digitized maps. A Bayesian geostatistical stochastic search variable selection procedure was employed to identify factors related to P. falciparum infection risk. Bayesian geostatistical logistic regression models were used to map the spatial distribution of P. falciparum infection and to predict the infection prevalence at non-sampled locations via Bayesian kriging.ResultsComplete data sets were available from 5,322 children aged 5–16 years across Côte d’Ivoire. P. falciparum was the predominant species (94.5 %). The Bayesian geostatistical variable selection procedure identified land cover and socioeconomic status as important predictors for infection risk with P. falciparum. Model-based prediction identified high P. falciparum infection risk in the north, central-east, south-east, west and south-west of Côte d’Ivoire. Low-risk areas were found in the south-eastern area close to Abidjan and the south-central and west-central part of the country.ConclusionsThe P. falciparum infection risk and related uncertainty estimates for school-aged children in Côte d’Ivoire represent the most up-to-date malaria risk maps. These tools can be used for spatial targeting of malaria control interventions.Electronic supplementary materialThe online version of this article (doi:10.1186/s13071-016-1775-z) contains supplementary material, which is available to authorized users.

Spatio-Temporal Dynamics of HIV-I Subtype B and Circulating Recombinant Form 01_AE Clades in Iran: A Phylogeographic Approach

Background: The origins and spatio-temporal dynamics of the HIV-1 subtype B and CRF01_AE clades are largely unknown in Iran. Objectives: This study investigates the origins and spatio-temporal dynamics of HIV-1 subtype B and CRF01_AE clades in Iran using the most recent phylogeographic methods. Methods: This Bayesian phylogeographic study examined HIV-1 sequence data available in the Los Alamos HIV databases. For the purposes of this study, we retrieved all the eligible time-stamped and geo-referenced HIV-1 subtype B (pol p66 and p51 RT) and CRF01_-AE (gp160) sequences from Iran. The majority of these data were obtained from studies using convenience and snowball sampling. Evolutionary and phylogeographic parameters were jointly estimated for each clade using BEAST software v.1.8.1. A Bayesian stochastic search variable selection method, with a threshold of 3.0 for the Bayes factors test, was used to obtain significant migration rates of the virus. Results: Our results suggest that the subtype B epidemic was introduced to Iran from Southern China, Germany and the USA. Although the migration rate from the USA to Iran was found to be statistically insignificant, the results suggested that the USA was the earliest exporter of the subtype B to Iran (i.e. since 1974, 95% Highest Posterior Density (HPD): 1966-1974). Moreover, based on our findings, the CRF01_AE clade was first introduced to Iran from Thailand (1988, 95% HPD: 1986 - 1990). Among the factors contributing to the transmission of subtype B to Iran from Germany is the transit of goods and long-distance truck driving. However, the factors behind the transmission of this clade from the USA and Southern China remain unknown. Similar to many countries, the introduction of CRF01_AE from Thailand might be attributed to sex tourism. Conclusions: There is evidence to suggest that subtype B and CRF01_AE were introduced to Iran through multiple pathways and origins, including Germany, Southern China, the USA, and Thailand. Factors such as international trade, the transit of goods, and tourism are among the most likely methods for transmission of these clades. However, the existing preventive strategies in Iran have been mostly focused on other risk groups, including sex workers and intravenous drug users. The findings of our study highlight the need for the further development of targeted preventive strategies, such as educational interventions, aimed at tourists, truck drivers, and people involved in international trade and transactions in order to prevent the onward transmission of disease.

Efficient genomic prediction based on whole-genome sequence data using split-and-merge Bayesian variable selection.

BackgroundUse of whole-genome sequence data is expected to increase persistency of genomic prediction across generations and breeds but affects model performance and requires increased computing time. In this study, we investigated whether the split-and-merge Bayesian stochastic search variable selection (BSSVS) model could overcome these issues. BSSVS is performed first on subsets of sequence-based variants and then on a merged dataset containing variants selected in the first step.ResultsWe used a dataset that included 4,154,064 variants after editing and de-regressed proofs for 3415 reference and 2138 validation bulls for somatic cell score, protein yield and interval first to last insemination. In the first step, BSSVS was performed on 106 subsets each containing ~39,189 variants. In the second step, 1060 up to 472,492 variants, selected from the first step, were included to estimate the accuracy of genomic prediction. Accuracies were at best equal to those achieved with the commonly used Bovine 50k-SNP chip, although the number of variants within a few well-known quantitative trait loci regions was considerably enriched. When variant selection and the final genomic prediction were performed on the same data, predictions were biased. Predictions computed as the average of the predictions computed for each subset achieved the highest accuracies, i.e. 0.5 to 1.1 % higher than the accuracies obtained with the 50k-SNP chip, and yielded the least biased predictions. Finally, the accuracy of genomic predictions obtained when all sequence-based variants were included was similar or up to 1.4 % lower compared to that based on the average predictions across the subsets. By applying parallelization, the split-and-merge procedure was completed in 5 days, while the standard analysis including all sequence-based variants took more than three months.ConclusionsThe split-and-merge approach splits one large computational task into many much smaller ones, which allows the use of parallel processing and thus efficient genomic prediction based on whole-genome sequence data. The split-and-merge approach did not improve prediction accuracy, probably because we used data on a single breed for which relationships between individuals were high. Nevertheless, the split-and-merge approach may have potential for applications on data from multiple breeds.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-016-0225-x) contains supplementary material, which is available to authorized users.

Spatio-Temporal History of HIV-1 CRF35_AD in Afghanistan and Iran.

HIV-1 Circulating Recombinant Form 35_AD (CRF35_AD) has an important position in the epidemiological profile of Afghanistan and Iran. Despite the presence of this clade in Afghanistan and Iran for over a decade, our understanding of its origin and dissemination patterns is limited. In this study, we performed a Bayesian phylogeographic analysis to reconstruct the spatio-temporal dispersion pattern of this clade using eligible CRF35_AD gag and pol sequences available in the Los Alamos HIV database (432 sequences available from Iran, 16 sequences available from Afghanistan, and a single CRF35_AD-like pol sequence available from USA). Bayesian Markov Chain Monte Carlo algorithm was implemented in BEAST v1.8.1. Between-country dispersion rates were tested with Bayesian stochastic search variable selection method and were considered significant where Bayes factor values were greater than three. The findings suggested that CRF35_AD sequences were genetically similar to parental sequences from Kenya and Uganda, and to a set of subtype A1 sequences available from Afghan refugees living in Pakistan. Our results also showed that across all phylogenies, Afghan and Iranian CRF35_AD sequences formed a monophyletic cluster (posterior clade credibility> 0.7). The divergence date of this cluster was estimated to be between 1990 and 1992. Within this cluster, a bidirectional dispersion of the virus was observed across Afghanistan and Iran. We could not clearly identify if Afghanistan or Iran first established or received this epidemic, as the root location of this cluster could not be robustly estimated. Three CRF35_AD sequences from Afghan refugees living in Pakistan nested among Afghan and Iranian CRF35_AD branches. However, the CRF35_AD-like sequence available from USA diverged independently from Kenyan subtype A1 sequences, suggesting it not to be a true CRF35_AD lineage. Potential factors contributing to viral exchange between Afghanistan and Iran could be injection drug networks and mass migration of Afghan refugees and labours to Iran, which calls for extensive preventive efforts.

Bayesian model selection in ordinal quantile regression

A Bayesian stochastic search variable selection (BSSVS) method is presented for variable selection in quantile regression (QReg) for ordinal models. A Markov Chain Monte Carlo (MCMC) method is adopted to draw the unknown quantities from the full posteriors. Through simulations and analysis of an educational attainment dataset, the performance of the proposed approach is compared with some existing approaches, showing that the proposed approach performs quite good in comparison to some other methods.

Genomic prediction using imputed whole-genome sequence data in Holstein Friesian cattle.

BackgroundIn contrast to currently used single nucleotide polymorphism (SNP) panels, the use of whole-genome sequence data is expected to enable the direct estimation of the effects of causal mutations on a given trait. This could lead to higher reliabilities of genomic predictions compared to those based on SNP genotypes. Also, at each generation of selection, recombination events between a SNP and a mutation can cause decay in reliability of genomic predictions based on markers rather than on the causal variants. Our objective was to investigate the use of imputed whole-genome sequence genotypes versus high-density SNP genotypes on (the persistency of) the reliability of genomic predictions using real cattle data.MethodsHighly accurate phenotypes based on daughter performance and Illumina BovineHD Beadchip genotypes were available for 5503 Holstein Friesian bulls. The BovineHD genotypes (631,428 SNPs) of each bull were used to impute whole-genome sequence genotypes (12,590,056 SNPs) using the Beagle software. Imputation was done using a multi-breed reference panel of 429 sequenced individuals. Genomic estimated breeding values for three traits were predicted using a Bayesian stochastic search variable selection (BSSVS) model and a genome-enabled best linear unbiased prediction model (GBLUP). Reliabilities of predictions were based on 2087 validation bulls, while the other 3416 bulls were used for training.ResultsPrediction reliabilities ranged from 0.37 to 0.52. BSSVS performed better than GBLUP in all cases. Reliabilities of genomic predictions were slightly lower with imputed sequence data than with BovineHD chip data. Also, the reliabilities tended to be lower for both sequence data and BovineHD chip data when relationships between training animals were low. No increase in persistency of prediction reliability using imputed sequence data was observed.ConclusionsCompared to BovineHD genotype data, using imputed sequence data for genomic prediction produced no advantage. To investigate the putative advantage of genomic prediction using (imputed) sequence data, a training set with a larger number of individuals that are distantly related to each other and genomic prediction models that incorporate biological information on the SNPs or that apply stricter SNP pre-selection should be considered.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-015-0149-x) contains supplementary material, which is available to authorized users.