Genome-wide association study for antibody response to Mycobacterium avium ssp. paratubeculosis in goats

A high-density genome-wide approach reveals novel genetic markers linked to small ruminant lentivirus susceptibility in sheep

Visna/Maedi virus (VMV) is lentiviral disease of sheep responsible for severe production losses. Multiple genomic regions associated with infection were reported indicating genetic complexity. In this study, a combined genome-wide approach using a high-density SNP array has been performed, comparing VMV-infected (n = 78) and non-infected (n = 66) individuals of the Valle del Belice breed. The serological tests showed a seroprevalence of 26%. The comparison among results from different approaches (GWAS, Fisher's exact test and the FST analysis) revealed two association signals: on OAR03 close to the GRIN2B gene and on OAR05 close to the TMEM232 gene. To the best of our knowledge, there has been no previous association between these genes and lentiviral infection in any species. The GRIN2B gene plays a role in pain response, synaptic transmission, and receptor clustering, while TMEM232 is involved in the development of immune-related disorders. The results highlighted new aspects of the genetic complexity related to the resistance/susceptibility to VMV in sheep, confirming that studies on different breeds can lead to different results. The ideal approach for validation of the markers identified in our study is to use samples from a population independent from the discovery population with the same phenotype used in the discovery stage.

The effects of case/control ratio and sample size on genome-wide association studies: A simulation study

BACKGROUND

Genome-wide association studies (GWAS) is a useful tool for the detection of disease or quantitative trait-related genetic variations in the veterinary field. For a binary trait, a case/control experiment is designed in GWAS. However, there is limited information on the optimal case/control and sample size in GWAS.

OBJECTIVES

In this study, it was aimed to detect the effects of case/control ratio and sample size for GWAS using computer simulation under certain assumptions.

METHOD

Using the PLINK software, we simulated three different disease scenarios. In scenario 1, we simulated 10 different case/control ratios with increasing ratio of cases to controls. In scenario 2, we did versa of scenario 1 with the increasing ratio of controls to cases. In scenarios 1 and 2, sample size gradually was increased with the change case/control ratios. In scenario 3, the total sample size was fixed to 2000 to see real effects of case/control ratio on the number of disease-related single nucleotide polymorphisms (SNPs).

RESULTS

The results showed that the number of disease-related SNPs were the highest when the case/control ratio is close to 1:1 in scenarios 1 and 2 and did not change with an increase in sample size. Similarly, the number of disease-related SNPs was the highest in case/control ratios 1:1 in scenario 3. However, unbalanced case/control ratio caused the detection of lower number of disease-related SNPs in scenario 3. The estimated average power of SNPs was highest when case/control ratio is 1:1 in all scenarios.

CONCLUSIONS

All findings led to the conclusion that an increase in sample size may enhance the statistical power of GWAS when the number of cases is small. In addition, case/control ratio 1:1 may be the optimal ratio for GWAS. These findings may be valuable not only for veterinary field but also for human clinical experiments.