Polygenic effect on accelerated tau pathology accumulation in Alzheimer's disease: Implications for patient selection in clinical trials

Progression of fibrillar tau is a key driver of dementia symptoms in Alzheimer's disease (AD), but predictors of the rate of tau accumulation at patient-level are missing. Here we combined the to-date largest number of genetic risk variants of AD (n=85 lead SNPs) from recent GWAS to generate a polygenic score (PGS) predicting the rate of change in fibrillar tau. We found that a higher PGS was associated with higher rates of PET-assessed fibrillar-tau accumulation over a mean of 1.8 yrs (range = 0.6 - 4 yrs). This, in turn, mediated the effects of the PGS on faster rates of cognitive decline. Sensitivity analysis showed that the effects were similar for men and women but pronounced in individuals with elevated levels of beta-amyloid and strongest for lead SNPs expressed in microglia. Together, our results demonstrate that the PGS predicts tau progression in Alzheimer's disease, which could afford sample size savings by up to 34% when used alone and up to 61% when combined with APOE ϵ4 genotype in clinical trials targeting tau pathology.

Genetic contributions to alcohol use disorder treatment outcomes: a genome-wide pharmacogenomics study

Naltrexone can aid in reducing alcohol consumption, while acamprosate supports abstinence; however, not all patients with alcohol use disorder (AUD) benefit from these treatments. Here we present the first genome-wide association study of AUD treatment outcomes based on data from the COMBINE and PREDICT studies of acamprosate and naltrexone, and the Mayo Clinic CITA study of acamprosate. Primary analyses focused on treatment outcomes regardless of pharmacological intervention and were followed by drug-stratified analyses to identify treatment-specific pharmacogenomic predictors of acamprosate and naltrexone response. Treatment outcomes were defined as: (1) time until relapse to any drinking (TR) and (2) time until relapse to heavy drinking (THR; ≥ 5 drinks for men, ≥4 drinks for women in a day), during the first 3 months of treatment. Analyses were performed within each dataset, followed by meta-analysis across the studies (N = 1083 European ancestry participants). Single nucleotide polymorphisms (SNPs) in the BRE gene were associated with THR (min p = 1.6E−8) in the entire sample, while two intergenic SNPs were associated with medication-specific outcomes (naltrexone THR: rs12749274, p = 3.9E−8; acamprosate TR: rs77583603, p = 3.1E−9). The top association signal for TR (p = 7.7E−8) and second strongest signal in the THR (p = 6.1E−8) analysis of naltrexone-treated patients maps to PTPRD, a gene previously implicated in addiction phenotypes in human and animal studies. Leave-one-out polygenic risk score analyses showed significant associations with TR (p = 3.7E−4) and THR (p = 2.6E−4). This study provides the first evidence of a polygenic effect on AUD treatment response, and identifies genetic variants associated with potentially medication-specific effects on AUD treatment response.

Importance of correcting genomic relationships in single-locus QTL mapping model with an Advanced Backcross population

Advanced Backcross (AB) populations have been widely used to identify and utilize beneficial alleles in various crops such as rice, tomato, wheat and barley. For the development of an AB population, a controlled crossing scheme is used and this controlled crossing along with the selection (both natural and artificial) of agronomically-adapted alleles during the development of AB population may lead to unbalanced allele frequencies in the population. However, it is commonly believed that interval mapping mapping of traits in experimental crosses such as AB populations are immune to the deviations from the expected frequencies under Mendelian segregation. Using two AB populations and simulated data sets as examples, we describe the severity of the problem caused by unbalanced allele frequencies in quantitative trait loci (QTL) mapping and demonstrate how it can be corrected using the linear mixed model having a polygenic effect with the covariance structure (genomic relationship matrix) calculated from molecular markers.

On the use of whole-genome sequence data for across-breed genomic prediction and fine-scale mapping of QTL

Background Whole-genome sequence (WGS) data are increasingly available on large numbers of individuals in animal and plant breeding and in human genetics through second-generation resequencing technologies, 1000 genomes projects, and large-scale genotype imputation from lower marker densities. Here, we present a computationally fast implementation of a variable selection genomic prediction method, that could handle WGS data on more than 35,000 individuals, test its accuracy for across-breed predictions and assess its quantitative trait locus (QTL) mapping precision. Methods The Monte Carlo Markov chain (MCMC) variable selection model (Bayes GC) fits simultaneously a genomic best linear unbiased prediction (GBLUP) term, i.e. a polygenic effect whose correlations are described by a genomic relationship matrix (G), and a Bayes C term, i.e. a set of single nucleotide polymorphisms (SNPs) with large effects selected by the model. Computational speed is improved by a Metropolis–Hastings sampling that directs computations to the SNPs, which are, a priori, most likely to be included into the model. Speed is also improved by running many relatively short MCMC chains. Memory requirements are reduced by storing the genotype matrix in binary form. The model was tested on a WGS dataset containing Holstein, Jersey and Australian Red cattle. The data contained 4,809,520 genotypes on 35,549 individuals together with their milk, fat and protein yields, and fat and protein percentage traits. Results The prediction accuracies of the Jersey individuals improved by 1.5% when using across-breed GBLUP compared to within-breed predictions. Using WGS instead of 600 k SNP-chip data yielded on average a 3% accuracy improvement for Australian Red cows. QTL were fine-mapped by locating the SNP with the highest posterior probability of being included in the model. Various QTL known from the literature were rediscovered, and a new SNP affecting milk production was discovered on chromosome 20 at 34.501126 Mb. Due to the high mapping precision, it was clear that many of the discovered QTL were the same across the five dairy traits. Conclusions Across-breed Bayes GC genomic prediction improved prediction accuracies compared to GBLUP. The combination of across-breed WGS data and Bayesian genomic prediction proved remarkably effective for the fine-mapping of QTL.

An IBD-based mixed model approach for QTL mapping in multiparental populations

Multiparental populations (MPPs) have become popular for QTL detection. Tools for QTL mapping in MPPs are mostly developed for specific MPPs and do not generalize to other MPPs. We present an IBD-based mixed model approach for QTL mapping in all kinds of MPP designs, e.g., diallel, Nested Association Mapping (NAM), and Multiparental Advanced Generation Intercrosses (MAGIC) designs. The first step is to compute identity-by-descent (IBD) probabilities using a general Hidden Markov model framework, called reconstructing ancestry blocks bit by bit (RABBIT). Next, functions of IBD information are used as design matrices, or genetic predictors, in a mixed model approach. The estimated parameters are random allelic effects associated with the parents. Family-specific residual genetic effects are added, and a polygenic effect is structured by kinship relations between individuals. Case studies of simulated diallel, NAM, and MAGIC designs proved that the IBD-based mixed model approach could increase true positive rates and mapping resolutions of QTLs in comparison to a widely used benchmark association mapping method. Successful analyses of various real data cases confirmed the wide applicability of our IBD-based mixed model methodology.

Genetic Contributions to Alcohol Use Disorder Treatment Outcomes: A Genome-wide Pharmacogenomics Study

ABSTRACTNaltrexone can aid in reducing alcohol consumption, while acamprosate supports abstinence; however, not all patients with alcohol use disorder (AUD) benefit from these treatments. Here we present the first genome-wide association study of AUD treatment outcomes based on data from the COMBINE and PREDICT studies of acamprosate and naltrexone, and the Mayo Clinic CITA study of acamprosate. Primary analyses focused on treatment outcomes regardless of pharmacological intervention and were followed by drug-stratified analyses to identify treatment-specific pharmacogenomic predictors of acamprosate and naltrexone response. Treatment outcomes were defined as: (1) time until relapse to any drinking (TR) and (2) time until relapse to heavy drinking (THR; ≥5 drinks for men, ≥4 drinks for women in a day), during the first three months of treatment. Analyses were performed within each dataset, followed by meta-analysis across the studies (N=1090 European ancestry participants). Single nucleotide polymorphisms (SNPs) in the BRE gene were associated with THR (min p=1.6E-08) in the entire sample, while two intergenic SNPs were associated with medication-specific outcomes (naltrexone THR: rs12749274, p=3.9E-08; acamprosate TR: rs77583603, p=3.1E-09). The top association signals for TR (p=7.7E-08) and second strongest signal in the THR (p=6.1E-08) analysis of the naltrexone-treated subset map to PTPRD, a gene previously implicated in addiction phenotypes in human and animal studies. Leave-one-out polygenic risk score analyses showed significant associations with TR (p=3.7E-04) and THR (p=2.6E-04). This study provides the first evidence of a polygenic effect on AUD treatment response, and identifies genetic variants associated with potentially medication-specific effects on AUD treatment response.

Genome-wide meta-analysis of pneumonia suggests a role for mucin biology and provides novel drug repurposing opportunities

ABSTRACTPneumonia remains one of the leading causes of death worldwide, particularly amongst the elderly and young children. We performed a genome-wide meta-analysis of lifetime pneumonia diagnosis (N=266,277), that encompassed the largest collection of cases published to date. Genome-wide significant associations with pneumonia were uncovered for the first time beyond the major histocompatibility complex region, with three novel loci, including a signal fine-mapped to a cluster of mucin genes. Moreover, we demonstrated evidence of a polygenic effect of common and low frequency pneumonia associated variation impacting several other mucin genes and O-glycosylation, further suggesting a role for these processes in pneumonia pathophysiology. The pneumonia GWAS was then leveraged to identify drug repurposing opportunities, including evidence that supports the use of lipid modifying agents in the prevention and treatment of the disorder. We also propose how polygenic risk could be utilised for precision drug repurposing through pneumonia risk scores constructed using variants mapped to pathways with known drug targets. In summary, we provide novel insights into the genetic architecture of pneumonia susceptibility, with future study warranted to functionally interrogate novel association signals and evaluate the suitability of the compounds prioritised by this study as repositioning candidates.

330 A hybrid model for genomic selection using prioritized SNPs based on FST scores in the presence of non-genotyped animals

The dramatic advancement in genotyping technology has greatly reduced the complexity and cost of genotyping. The continuous increase in the density of marker panels is resulting in little to no improvement in the accuracy of genomic selection. Direct inversion of the genomic relationship matrix is infeasible for some livestock populations due to the excessive computational cost. In addition, most animals in genetic evaluation programs are non-genotyped. Including these animals in a genomic evaluation requires the imputation of the missing genotypes when using regression methods. To overcome these challenges, a hybrid approach is proposed. This approach fits a subset of SNP markers selected based on FST scores and a classical polygenic effect. The method was first tested using only genotyped animals and then extended to accommodate non-genotyped animals. The proposed approach was evaluated using simulated data for a trait with heritability of 0.1 and 0.4 and weaning weight in a crossbred beef cattle population. When all animals were genotyped, the hybrid approach using only 2.5% of prioritized SNPs exceeded the prediction accuracies of BayesB, BayesC, and GBLUP by more than 7%. When non-genotyped animals were incorporated, the proposed approach significantly outperformed ss-GBLUP method in terms of prediction accuracy under both simulated heritability scenarios. Although the results seem to depend on the genetic complexity of the trait, the proposed approach resulted in higher prediction accuracies than current methods. Furthermore, its computational costs in terms of CPU time and peak memory are substantially lower than the current methods.

Ghost QTL and hotspots in experimental crosses - novel solution by mixed model with nonzero mean

Abstract“Ghost-QTL” are the false discoveries in QTL mapping, that arise due to the “accumulation” of the polygenic effects, uniformly distributed over the genome. The locations on the chromosome which are strongly correlated with the summary polygenic effect depend on a specific sample correlation structure determined by the genotype at all loci. During the analysis of e-QTL data or recombinant inbred lines this correlation structure is preserved for all traits under consideration, and may lead to the so called “hot-spots” via the detection of the summary polygenic effect at exactly the same positions for most of the considered traits. We illustrate that the problem can be solved by the application of the extended mixed effect model, where the random effects are allowed to have a nonzero mean. We provide formulas for estimating the thresholds for the corresponding t-test statistics and use them in the stepwise selection strategy, which allows for a simultaneous detection of several QTL. Extensive simulation studies illustrate that our approach allows to eliminate ghost-QTL/false hot spot effects, while preserving a high power of detection of true QTL effects.