An integrative analysis of genomic and exposomic data for complex traits and phenotypic prediction

Complementary to the genome, the concept of exposome has been proposed to capture the totality of human environmental exposures. While there has been some recent progress on the construction of the exposome, few tools exist that can integrate the genome and exposome for complex trait analyses. Here we propose a linear mixed model approach to bridge this gap, which jointly models the random effects of the two omics layers on phenotypes of complex traits. We illustrate our approach using traits from the UK Biobank (e.g., BMI and height for N ~ 35,000) with a small fraction of the exposome that comprises 28 lifestyle factors. The joint model of the genome and exposome explains substantially more phenotypic variance and significantly improves phenotypic prediction accuracy, compared to the model based on the genome alone. The additional phenotypic variance captured by the exposome includes its additive effects as well as non-additive effects such as genome–exposome (gxe) and exposome–exposome (exe) interactions. For example, 19% of variation in BMI is explained by additive effects of the genome, while additional 7.2% by additive effects of the exposome, 1.9% by exe interactions and 4.5% by gxe interactions. Correspondingly, the prediction accuracy for BMI, computed using Pearson’s correlation between the observed and predicted phenotypes, improves from 0.15 (based on the genome alone) to 0.35 (based on the genome and exposome). We also show, using established theories, that integrating genomic and exposomic data can be an effective way of attaining a clinically meaningful level of prediction accuracy for disease traits. In conclusion, the genomic and exposomic effects can contribute to phenotypic variation via their latent relationships, i.e. genome-exposome correlation, and gxe and exe interactions, and modelling these effects has a potential to improve phenotypic prediction accuracy and thus holds a great promise for future clinical practice.

29 Effects of Recent and Ancient Inbreeding on Growth in American Angus Cattle

The accumulation of inbreeding can lead to an unfavorable change in the phenotypic value of individuals for traits related to fitness, also known as inbreeding depression. However, inbreeding accumulated at a more distant past (ancient inbreeding) is expected to have a smaller depressive effect than that accumulated more recently due to the loss of detrimental alleles caused by purifying selection. Therefore, the aim of this study was to quantify the inbreeding depression caused by recent and ancient inbreeding for birth weight, weaning weight, and post-weaning gain. Pedigree and genomic information were obtained from Angus Genetics, Inc. (St. Joseph, MO) for 569,364 individuals from the American Angus breed. Pedigree inbreeding and genomic inbreeding based on runs of homozygosity (ROH) were estimated using the SNP1101 software. Model-based genomic inbreeding based on the probability a marker is part of a homozygous-by-descent segment (HBD) was estimated using the RZooROH in R. The generational cutoffs for designating inbreeding as recent was that acquired 5 generations ago or sooner for pedigree, 6.25 generations ago or sooner for ROH, and 8 generations ago or sooner for HBD inbreeding. The effect of a 1% increase in inbreeding was modeled in males and females using a linear mixed model approach. Recent pedigree inbreeding was found to decrease birth weight by 0.04 and 0.03 kg, decrease weaning weight by 0.50 and 0.48 kg, and decrease post-weaning gain by 0.62 and 0.32 kg, in males and females respectively. Ancient pedigree inbreeding was generally found to have no effect on growth. For genomic inbreeding, when both recent and ancient inbreeding had a detrimental effect on growth, recent inbreeding generally had a larger effect. The results of this study demonstrate that inbreeding accumulated recently should be quantified and managed in beef cattle populations to avoid economic losses.

The ABCC4 gene is associated with pyometra in golden retriever dogs

Pyometra is one of the most common diseases in female dogs, presenting as purulent inflammation and bacterial infection of the uterus. On average 20% of intact female dogs are affected before 10 years of age, a proportion that varies greatly between breeds (3–66%). The clear breed predisposition suggests that genetic risk factors are involved in disease development. To identify genetic risk factors associated with the disease, we performed a genome-wide association study (GWAS) in golden retrievers, a breed with increased risk of developing pyometra (risk ratio: 3.3). We applied a mixed model approach comparing 98 cases, and 96 healthy controls and identified an associated locus on chromosome 22 (p = 1.2 × 10–6, passing Bonferroni corrected significance). This locus contained five significantly associated SNPs positioned within introns of the ATP-binding cassette transporter 4 (ABCC4) gene. This gene encodes a transmembrane transporter that is important for prostaglandin transport. Next generation sequencing and genotyping of cases and controls subsequently identified four missense SNPs within the ABCC4 gene. One missense SNP at chr22:45,893,198 (p.Met787Val) showed complete linkage disequilibrium with the associated GWAS SNPs suggesting a potential role in disease development. Another locus on chromosome 18 overlapping the TESMIN gene, is also potentially implicated in the development of the disease.

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

Key message The identity-by-descent (IBD)-based mixed model approach introduced in this study can detect quantitative trait loci (QTLs) referring to the parental origin and simultaneously account for multilevel relatedness of individuals within and across families. This unified approach is proved to be a powerful approach for all kinds of multiparental population (MPP) designs. Abstract Multiparental populations (MPPs) have become popular for quantitative trait loci (QTL) detection. Tools for QTL mapping in MPPs are mostly developed for specific MPPs and do not generalize well 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 Intercross (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 to estimate variance components for multiallelic genetic effects associated with 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 advanced IBD-based multi-QTL mixed model approach incorporating both kinship relations and family-specific residual variances (IBD.MQMkin_F) is robust across a variety of MPP designs and allele segregation patterns in comparison to a widely used benchmark association mapping method, and in most cases, outperformed or behaved at least as well as other tools developed for specific MPP designs in terms of mapping power and resolution. Successful analyses of real data cases confirmed the wide applicability of our IBD-based mixed model methodology.

Understanding norovirus reporting patterns in England: a mixed model approach

Background Norovirus has a higher level of under-reporting in England compared to other intestinal infectious agents such as Campylobacter or Salmonella, despite being recognised as the most common cause of gastroenteritis globally. In England, this under-reporting is a consequence of the frequently mild/self-limiting nature of the disease, combined with the passive surveillance system for infectious diseases reporting. We investigated heterogeneity in passive surveillance system in order to improve understanding of differences in reporting and laboratory testing practices of norovirus in England. Methods The reporting patterns of norovirus relating to age and geographical region of England were investigated using a multivariate negative binomial model. Multiple model formulations were compared, and the best performing model was determined by proper scoring rules based on one-week-ahead predictions. The reporting patterns are represented by epidemic and endemic random intercepts; values close to one and less than one imply a lower number of reports than expected in the given region and age-group. Results The best performing model highlighted atypically large and small amounts of reporting by comparison with the average in England. Endemic random intercept varied from the lowest in East Midlands in those in the under 5 year age-group (0.36, CI 0.18–0.72) to the highest in the same age group in South West (3.00, CI 1.68–5.35) and Yorkshire & the Humber (2.93, CI 1.74–4.94). Reporting by age groups showed the highest variability in young children. Conclusion We identified substantial variability in reporting patterns of norovirus by age and by region of England. Our findings highlight the importance of considering uncertainty in the design of forecasting tools for norovirus, and to inform the development of more targeted risk management approaches for norovirus disease.

Evaluating General Combining Ability for Multiple Traits in Tetraploid Bahiagrass

Recurrent selection based on combining ability has been successfully used in tetraploid bahiagrass (Paspalum notatum Flüggé) to accumulate heterotic effects and exploit hybrid vigor. However, its efficiency depends on an accurate selection of the best genotypes to form a new recombinant population. The objective of this work was to assess the general combining ability of female parents of bahiagrass based on the performance of their progeny for agronomic and morphological traits using a mixed model approach, biplot analysis and selection index. There were evaluated 29 half-sib families generated by crossing 29 sexual tetraploid genotypes from a sexual synthetic tetraploid population and a group of apomictic tetraploid genotypes. Agronomic and morphological traits were analyzed using a mixed model approach (BLUP). The multi-trait analysis was based on a biplot analysis and a selection index using the family BLUPs. BLUP analysis showed significant differences among families for most of the evaluated traits. Sexual female parents of families 5, 9, 8, 28, 21 and 16 were identified as those with greater general combining ability. Biplot showed variability among families and allowed identifying six sexual parents with greater general combining ability. The same sexual parents that exhibited greater general combining ability by BLUP were identified with greater general combining ability by biplot. Selection index was variable and allowed identifying the same best sexual parents that BLUP and biplot. The three analysis methods were equally effective to estimate general combining ability of a group of sexual parents of tetraploid bahiagrass based on the performance of their progeny.