Identify known and novel candidate genes associated with backfat thickness in Duroc pigs by large-scale genome-wide association analysis

Backfat thickness (BFT) is complex and economically important traits in the pig industry, since it reflects fat deposition and can be used to measure the carcass lean meat percentage in pigs. In this study, all 6,550 pigs were genotyped using the Geneseek Porcine 50K SNP Chip to identify SNPs related to BFT and to search for candidate genes through genome-wide association analysis in two Duroc populations. In total, 80 SNPs, including 39 significant and 41 suggestive SNPs, and 6 QTLs were identified significantly associated with the BFT. In addition, 9 candidate genes, including a proven major gene MC4R, 3 important candidate genes (RYR1, HMGA1 and NUDT3) which were previously described as related to BFT, and 5 novel candidate genes (SIRT2, NKAIN2, AMH, SORCS1 and SORCS3) were found based on their potential functional roles in BFT. The functions of candidate genes and gene set enrichment analysis indicate that most important pathways are related to energy homeostasis and adipogenesis. Finally, our data suggests that most of the candidate genes can be directly used for genetic improvement through molecular markers, except that the MC4R gene has an antagonistic effect on growth rate and carcass lean meat percentage in breeding. Our results will advance our understanding of the complex genetic architecture of BFT traits, and laid the foundation for additional genetic studies to increase carcass lean meat percentage of pig through marker-assisted selection and/or genomic selection.

Genome-wide association analysis of chickpea germplasms differing for salinity tolerance based on DArTseq markers

Soil salinity is significant abiotic stress that severely limits global crop production. Chickpea (Cicer arietinum L.) is an important grain legume that plays a substantial role in nutritional food security, especially in the developing world. This study used a chickpea population collected from the International Center for Agricultural Research in the Dry Area (ICARDA) genebank using the focused identification of germplasm strategy. The germplasm included 186 genotypes with broad Asian and African origins and genotyped with 1856 DArTseq markers. We conducted phenotyping for salinity in the field (Arish, Sinai, Egypt) and greenhouse hydroponic experiments at 100 mM NaCl concentration. Based on the performance in both hydroponic and field experiments, we identified seven genotypes from Azerbaijan and Pakistan (IGs: 70782, 70430, 70764, 117703, 6057, 8447, and 70249) as potential sources for high salinity tolerance. Multi-trait genome-wide association analysis (mtGWAS) detected one locus on chromosome Ca4 at 10618070 bp associated with salinity tolerance under hydroponic and field conditions. In addition, we located another locus specific to the hydroponic system on chromosome Ca2 at 30537619 bp. Gene annotation analysis revealed the location of rs5825813 within the Embryogenesis-associated protein (EMB8-like), while the location of rs5825939 is within the Ribosomal Protein Large P0 (RPLP0). Utilizing such markers in practical breeding programs can effectively improve the adaptability of current chickpea cultivars in saline soil. Moreover, researchers can use our markers to facilitate the incorporation of new genes into commercial cultivars.

Genome-wide association study and genomic heritabilities for blood protein levels in Lori-Bakhtiari sheep

Serum protein levels are related to physiological and pathological status of animals and could be affected by both genetic and environmental factors. This study aimed to evaluate genetic variation of serum protein profile in sheep. Blood samples were randomly collected from 96 Lori-Bakhtiari ewes, a heavy meat-type breed. Total protein, albumin, globulin, α1, α2, β and γ globulins and IgG levels were measured in blood serum. The samples were genotyped using the Illumina OvineSNP50 BeadChip. The studied traits adjusted for age, birth type, birth season and estimate of breeding value for body weight were considered as pseudo-phenotypes in genome-wide association analysis. In the GWAS model, the first five principal components were fitted as covariates to correct the biases due to possible population stratification. The Plink, R and GCTA software were used for genome-wide association analysis, construction of Q-Q and Manhattan plots and estimation of genetic variances, respectively. Noticeable genomic heritabilities ± SE were estimated for total and γ globulins (0.868 ± 0.262 and 0.831 ± 0.364, respectively), but other protein fractions had zero or close to zero estimates. Based on the Bonferroni adjusted p values, four QTLs located on 181.7 Mbp of OAR3, 107.7 Mbp of OAR4, 86.3 Mbp of OAR7 and 83.0 Mbp of OAR8 were significantly associated with α1, β, β and γ globulins, respectively. The results showed that the PKP2, IGF2R, SLC22A1 and SLC22A2 genes could be considered as candidate genes for blood serum proteins. The present study showed significant genetic variations of some blood protein fractions.

IRF7 and RNH1 are modifying factors of HIV-1 reservoirs: a genome-wide association analysis

Background Combination antiretroviral treatment (cART) cannot eradicate HIV-1 from the body due to the establishment of persisting viral reservoirs which are not affected by therapy and reinitiate new rounds of HIV-1 replication after treatment interruption. These HIV-1 reservoirs mainly comprise long-lived resting memory CD4+ T cells and are established early after infection. There is a high variation in the size of these viral reservoirs among virally suppressed individuals. Identification of host factors that contribute to or can explain this observed variation could open avenues for new HIV-1 treatment strategies. Methods In this study, we conducted a genome-wide quantitative trait locus (QTL) analysis to probe functionally relevant genetic variants linked to levels of cell-associated (CA) HIV-1 DNA, CA HIV-1 RNA, and RNA:DNA ratio in CD4+ T cells isolated from blood from a cohort of 207 (Caucasian) people living with HIV-1 (PLHIV) on long-term suppressive antiretroviral treatment (median = 6.6 years). CA HIV-1 DNA and CA HIV-1 RNA levels were measured with corresponding droplet digital PCR (ddPCR) assays, and genotype information of 522,455 single-nucleotide variants was retrieved via the Infinium Global Screening array platform. Results The analysis resulted in one significant association with CA HIV-1 DNA (rs2613996, P < 5 × 10−8) and two suggestive associations with RNA:DNA ratio (rs7113204 and rs7817589, P < 5 × 10−7). Then, we prioritized PTDSS2, IRF7, RNH1, and DEAF1 as potential HIV-1 reservoir modifiers and validated that higher expressions of IRF7 and RNH1 were accompanied by rs7113204-G. Moreover, RNA:DNA ratio, indicating relative HIV-1 transcription activity, was lower in PLHIV carrying this variant. Conclusions The presented data suggests that the amount of CA HIV-1 DNA and RNA:DNA ratio can be influenced through PTDSS2, RNH1, and IRF7 that were anchored by our genome-wide association analysis. Further, these observations reveal potential host genetic factors affecting the size and transcriptional activity of HIV-1 reservoirs and could indicate new targets for HIV-1 therapeutic strategies.

A multivariate to multivariate approach for voxel-wise genome-wide association analysis

The joint analysis of imaging-genetics data facilitates the systematic investigation of genetic effects on brain structures and functions with spatial specificity. We focus on voxel-wise genome-wide association analysis, which may involve trillions of single nucleotide polymorphism (SNP)-voxel pairs. We attempt to identify underlying organized association patterns of SNP-voxel pairs and understand the polygenic and pleiotropic networks on brain imaging traits. We propose a bi-clique graph structure (i.e., a set of SNPs highly correlated with a cluster of voxels) for the systematic association pattern. Next, we develop computational strategies to detect latent SNP-voxel bi-cliques and inference model for statistical testing. We further provide theoretical results to guarantee the accuracy of our computational algorithms and statistical inference. We validate our method by extensive simulation studies and then apply it to the whole genome genetic and voxel-level white matter integrity data collected from 1052 participants of the human connectome project (HCP). The results demonstrate multiple genetic loci influencing white matter integrity measures on splenium and genu of the corpus callosum.

25 Gut Microbiome Information Enables Additional Discovery in Genome-wide Association Studies in Swine

Several studies have highlighted the relevance of gut microbiome composition in shaping fat deposition in mammals. In contrast, other studies have highlighted how the host genome can control the abundance of individual species in the gut microbiota’s make-up. There is the need to incorporate the different ‘-omics’ data (host genome, gut microbiome, high-throughput phenotyping) in a model that allows to extract information beyond the simple sum of each component’s contribution. We propose a systematic approach to detect host genomic variants that control the gut microbiome, which in turn contributes to the host fat deposition, when this latter is based on multiple phenotypic measures. Using a dataset that included longitudinal records of fat deposition on 1,180 pigs, we implemented a mediation test to describe how fat deposition in swine (Sus scrofa) is affected by the host genotype and the gut microbiome. The phenotypic outcome was described both by measured and latent variables, taking advantage of structural equation modeling. We also implemented a ‘traditional’ genome-wide association analysis, testing the (total) effect of host genomic variants on the phenotype. Results for all models were validated using both bootstrapping and permutation tests. The models identified several host genomic features having microbiome-mediated effects on fat deposition. Our work demonstrates how the host genome can affect the phenotypic trait by inducing a change in gut microbiome composition that leads to a change in the phenotype. The host genomic features identified through the mediation analysis do not entirely overlap the group of features identified by traditional GWAS. Microbiome-mediated analyses can help understand the genetic determination of complex phenotypes. The host genomic features that exert a mediated effect could not be identified by traditional genome-wide association analysis. These can contribute to filling the missing heritability gap and provide further insights into the host genome – gut microbiome interplay.