Bovine host genome acts on specific metabolism, communication and genetic processes of rumen microbes host-genomically linked to methane emissions

Whereas recent studies in different species showed that the host genome shapes the microbial community profile, our new research strategy revealed substantial host genomic control of comprehensive functional microbial processes in the rumen of bovines by utilising microbial gene profiles from whole metagenomic sequencing. Of 1,107/225/1,141 rumen microbial genera/metagenome assembled uncultured genomes (RUGs)/genes identified, 203/16/352 were significantly (P<2.02 x10-5) heritable (0.13 to 0.61), revealing substantial variation in host genomic control. We found 29/22/115 microbial genera/RUGs/genes host-genomically correlated (-0.93 to 0.92) with emissions of the potent greenhouse gas methane (CH4), highlighting the strength of host genomic control of specific microbial processes impacting on CH4. Only one of these microbial genes was directly involved in methanogenesis (cofG), whereas others were involved in providing substrates for archaea (e.g. bcd and pccB), important microbial interspecies communication mechanisms (ABC.PE.P), host-microbiome interaction (TSTA3) and genetic information processes (RP-L35). In our population, selection based on abundances of the 30 most informative microbial genes provided a mitigation potential of 17% of mean CH4 emissions per generation, which is higher than for selection based on measured CH4 using respiration chambers (13%), indicating the high potential of microbiome-driven breeding to cumulatively reduce CH4 emissions and mitigate climate change.

A Shortcut Approach for Large-scale Mixed Model Associations with Binary Traits

Generalized linear mixed models exhibit computationally intensive and biasness in mapping quantitative trait nucleotides for binary diseases. In genomic logit regression, we consider genomic breeding values estimated in advance as a known predictor, and then correct the deflated association test statistics by using genomic control, thereby successfully extending GRAMMAR-Lambda to analyze binary diseases in a complex structured population. Because there is no need to estimate genomic heritability and genomic breeding values can be estimated by a small number of sampling markers, the generalized mixed-model association analysis has been extremely simplified to handle large-scale data. With almost perfect genomic control, joint analysis for the candidate quantitative trait nucleotides chosen by multiple testing offered a significant improvement in statistical power.

A Shortcut Approach for Large-scale Mixed Model Associations with Binary Traits

Generalized linear mixed models exhibit computationally intensive and biasness in mapping quantitative trait nucleotides for binary diseases. In genomic logit regression, we consider genomic breeding values estimated in advance as a known predictor, and then correct the deflated association test statistics by using genomic control, thereby successfully extending GRAMMAR-Lambda to analyze binary diseases in a complex structured population. Because there is no need to estimate genomic heritability and genomic breeding values can be estimated by a small number of sampling markers, the generalized mixed-model association analysis has been extremely simplified to handle large-scale data. With almost perfect genomic control, joint analysis for the candidate quantitative trait nucleotides chosen by multiple testing offered a significant improvement in statistical power.

Efficient Genomic Control for Mixed Model Associations in Large-scale Population

Among linear mixed model-based association methods, GRAMMAR has the lowest computing complexity for association tests, but it produces a high false-negative rate due to the deflation of test statistics for complex population structure. Here, we present an optimized GRAMMAR method by efficient genomic control, Optim-GRAMMAR, that estimates the phenotype residuals by regulating downward genomic heritability in the genomic best linear unbiased prediction. Even though using the fewer sampling markers to evaluate genomic relationship matrices and genomic controls, Optim-GRAMMAR retains a similar statistical power to the exact mixed model association analysis, which infers an extremely efficient approach to handle large-scale data. Moreover, joint association analysis significantly improved statistical power over existing methods.

118 Awardee Talk - Genomic control of fatty acid profile in beef

Steers (n = 868) were raised, feedlot-finished with ad libitum access to a high-energy diet and harvested to determine if there is genomic control of fatty acid profile traits in beef breeds common to the United States. Cattle included purebred and crossbred progeny of Angus, Red Angus, Hereford, Shorthorn, Simmental, Charolais, Limousin, Gelbvieh, Maine Anjou, Chiangus, Braunvieh, Salers, Brahman, Brangus, Santa Gertrudis, and Beefmaster as well as three composite lines. Either directly or through imputation, genotypes were determined for &gt; 133,000 functional single nucleotide polymorphisms (SNP). At approximately 38 h postmortem, a 2.54-cm-thick longissimus steak was obtained from the 13th rib region of the left side of each carcass. At 14 d postmortem, steaks were cooked and subsequently longissimus was pulverized in liquid nitrogen. Fatty acid profile was determined by gas chromatography and mass spectrometry. A genome-wide association study was conducted for fatty acid profile traits expressed as a deviation from the contemporary group mean using the Mixed Linear Model Analysis of SNP & Variation Suite 8.8.3 (Golden Helix) and Pre-computed Kinship Matrix using the GBLUP Genomic Relationship Matrix. A SNP in coiled coil domain containing 57 (CCDC57; Chromosome 19 at 51,349,695) affected the percentage of C14:0 (P &lt; 10–46), short-chain fatty acids (P &lt; 10–36), and saturated fatty acids (P &lt; 10–17). Also, a SNP in thyroid hormone responsive (THRSP; Chromosome 29 at 18,090,403) affected the percentage of C14:0 (P &lt; 10–16) and short-chain fatty acids (P &lt; 10-10). The percentage of polyunsaturated fatty acids was affected by SNP in myostatin (Chromosome 2 at 6,213,980; P &lt; 10–15). These results show that fatty acid profile of beef can be changed through genetic selection but, it is not clear if the level of change will be great enough to impact human health.

Perceived Racial Discrimination and DNA Methylation Among African American Women in the InterGEN Study

Introduction: Experiences of racial discrimination have been associated with poor health outcomes. Little is known, however, about how perceived racial discrimination influences DNA methylation (DNAm) among African Americans (AAs). We examined the association of experiences of discrimination with DNAm among AA women in the Intergenerational Impact of Genetic and Psychological Factors on Blood Pressure (InterGEN) study. Methods: The InterGEN study examines the effects of genetic and psychological factors on blood pressure among AA women and their children. Measures include the Major Life Discrimination (MLD) and the Race-Related Events (RES) scales. In the present analysis, we examined discrimination and DNAm at baseline in the InterGEN study. The 850K EPIC Illumina BeadChip was used for evaluating DNAm in this epigenome-wide association study (EWAS). Results: One hundred and fifty-two women contributed data for the RES-EWAS analysis and 147 for the MLD-EWAS analysis. Most were 30–39 years old, nonsmokers, had some college education, and had incomes <US$15,000/year. After controlling for age, smoking, and cell composition, MLD was significantly associated with DNAm at nine CpG (regions of DNA where a cytosine nucleotide is followed by a guanine nucleotide) sites (false discovery rate [FDR]-corrected p < .05). For the RES-EWAS analysis, no DNAm sites passed the epigenome-wide significance level after genomic control, though suggestive associations were observed at CpG sites after genomic control (raw p < 10−5). Conclusion: We observed significant epigenetic associations between disease-associated genes (e.g., schizophrenia, bipolar disorder, and asthma) and perceived discrimination as measured by the MLD Scale. Future health disparities research should include epigenetics in high-risk populations to elucidate functional consequences induced by the psychosocial environment.