This protocol details the data quality assessment and control steps that are typically carried out during case-control association studies. The steps described involve the identification and removal of DNA samples and markers that introduce bias to the study. These critical steps are paramount to the success of a case-control study and are necessary before statistically testing for association. We describe how to use PLINK, a tool for handling SNP data, to carry out assessments of failure rate per-individual and per-SNP and to assess the degree of relatedness between individuals. We also detail other quality control procedures, including the use of SMARTPCA for the identification of ancestral outliers. These platforms were selected because they are user-friendly, widely used, and computationally efficient. Steps needed to detect and establish a disease association using case-control data are not discussed, as these are provided in a further protocol in the series. Issues concerning the study design and marker selection in case-control studies have been discussed in our earlier protocols. The protocol should take approximately 8 hours to complete.

This protocol describes how to perform basic statistical analysis in a population-based genetic association case-control study. The steps described involve the (i) appropriate selection of measures of association and relevance of disease models; (ii) appropriate selection of tests of association; (iii) visualization and interpretation of results; (iv) consideration of appropriate methods to control for multiple testing; and (v) replication strategies. Assuming no previous experience with software such as PLINK, R or Haploview, we describe how to use these popular tools for handling single-nucleotide polymorphism data in order to carry out tests of association and visualize and interpret results. This protocol assumes that data quality assessment and control has been performed, as described in a previous protocol, so that samples and markers deemed to have the potential to introduce bias to the study have been identified and removed. Study design, marker selection and quality control of casecontrol studies have also been discussed in earlier protocols. The protocol should take ~1 h to complete.

Leprosy is an infectious disease caused by the obligate intracellular pathogen Mycobacterium leprae and remains endemic in many parts of the world. Despite several major studies on susceptibility to leprosy, few genomic loci have been replicated independently. We have conducted an association analysis of more than 1,500 individuals from different case-control and family studies, and observed consistent associations between genetic variants in both TLR1 and the HLA-DRB1/DQA1 regions with susceptibility to leprosy (TLR1 I602S, case-control P = 5.7×10−8, OR = 0.31, 95% CI = 0.20–0.48, and HLA-DQA1 rs1071630, case-control P = 4.9×10−14, OR = 0.43, 95% CI = 0.35–0.54). The effect sizes of these associations suggest that TLR1 and HLA-DRB1/DQA1 are major susceptibility genes in susceptibility to leprosy. Further population differentiation analysis shows that the TLR1 locus is extremely differentiated. The protective dysfunctional 602S allele is rare in Africa but expands to become the dominant allele among individuals of European descent. This supports the hypothesis that this locus may be under selection from mycobacteria or other pathogens that are recognized by TLR1 and its co-receptors. These observations provide insight into the long standing host-pathogen relationship between human and mycobacteria and highlight the key role of the TLR pathway in infectious diseases.