21 Unravelling Transmission Ratio Distortion Across the Genome of a Crossbreed Beef Cattle Population

Transmission ratio distortion (TRD) is a process when one allele from either parent is preferentially transmitted to the offspring. The identification of genomic regions affected by TRD might help in the detection of lethal alleles or potential genes affecting reproduction. Here, we investigated TRD in crossbreed beef cattle population aiming to identify genomic regions showing altered deviations in segregation that could be affecting reproduction performance. A total of 237 genotyped animals were used including 46 sires, 80 dams, and 111 parent-offspring (trios). The predominant breeds of these animals were Angus (61.83%), Simmental (18.99%), Gelbvieh (6.12%), Charolais (3.65%), Hereford (2.46%) and Limousin (1.57%). After excluding SNPs with minor allele frequency lower than 0.05 and call-rate lower than 0.90, a total of 369,902 autosomal SNPs were retained for further analyses. The SNP-by-SNP analysis was performed within a Bayesian framework using TRDscanv.2.0 software, using 100,000 iterations, with 10,000 iterations being discarded as burn-in. As table 1 shows, 33 SNPs were identified with TRD, considering a Bayes Factor (BF)≥100 and the approximate empirical null distribution of TRD at 0.01% margin error. Among them, 26 SNPs were parent-unspecific and 7 SNPs were parent-specific TRD. For parent-specific TRD, 214 were identified for sire- and 162 for dam-TRD (BF≥100). Among them, 4 SNPs were detected with sire- and dam-TRD in opposite direction of preference of transmission. Preliminary functional and positional analysis was performed using the list of TRD regions with BF≥100 and the approximate empirical null distribution of TRD at 0.01% margin error. For sire-TRD, 14% of the identified QTL (n = 254) were related to non-return rate. For dam-TRD, 21 regions related to conception rate were found (1.5%) and 13 regions related to stillbirth (0.93%). Haplotype analysis is in progress to identify additional candidate regions and alleles with TRD to better understand this phenomenon in a crossbreed beef population.

Controlling bias and inflation in epigenome- and transcriptome-wide association studies using the empirical null distribution

ABSTRACTAssociation studies on omic-level data other then genotypes (GWAS) are becoming increasingly common, i.e., epigenome-and transcriptome-wide association studies (EWAS/TWAS). However, a tool box for the analysis of EWAS and TWAS studies is largely lacking and often approaches from GWAS are applied despite the fact that epigenome and transcriptome data have vedifferent characteristics than genotypes. Here, we show that EWASs and TWASs are prone not only to significant inflation but also bias of the test statistics and that these are not properly addressed by GWAS-based methodology (i.e. genomic control) and state-of-the-art approaches to control for unmeasured confounding (i.e. RUV, sva and cate). We developed a novel approach that is based on the estimation of the empirical null distribution using Bayesian statistics. Using simulation studies and empirical data, we demonstrate that our approach maximizes power while properly controlling the false positive rate. Finally, we illustrate the utility of our method in the application of meta-analysis by performing EWASs and TWASs on age and smoking which highlighted an overlap in differential methylation and expression of associated genes. An implementation of our new method is available from http://bioconductor.org/packages/bacon/.