Brief overview of human genetics: from SNP to causal allele

There are approximately 10 million common SNPs in the human genome. A fundamental challenge in human genetics is to systematically test each of these 10 million common SNPs for its role in disease. Advances in genomic technology have made this feasible. Contemporary GWASs test several hundred thousand SNPs across the entire human genome, most of which are common (minor allele frequency >5%) in the general, healthy population. To test the remaining more than 9 million common SNPs, the GWAS approach relies on the correlation structure of nearby SNPs. That is, 9 of 10 SNPs are highly correlated, and testing 1 SNP serves to tag the remaining 9 nearby SNPs. This concept is known as linkage disequilibrium (LD).

But the properties of LD that make it powerful for gene mapping also underscore the challenges that remain once an SNP is associated with disease risk; it is unknown if the SNP genotyped (and associated with risk in the genetic study) is the actual causal allele or whether the genotyped/associated SNP is simply in LD with the causal allele. Here causal allele is the single genetic mutation that is responsible for disrupting gene function and giving rise to the phenotype of interest. Given the sheer number of common alleles, the genotyped/associated SNP is most likely just a proxy for the actual causal allele. An example of the correlation structure of an RA risk locus is shown in Fig. 1 .

Case-control association results and LD structure in the TRAF1-C5 locus. Panel A shows results for SNPs genotyped across 1 Mb as part of the original genome-wide association scan in samples from 1522 case subjects with anti–CCP-positive RA and 1850 control subjects. Each diamond indicates a genotyped SNP; the color of each diamond is based on the correlation coefficient (r ² ), with the CEPH (Centre d’Etude du Polymorphisme Humain) from Utah (CEU) HapMap with the most significant SNP in the study (rs3761847). The blue diamond indicates the P value for all samples in the study (the original scan plus replication samples) as determined by the Cochran-Mantel-Haenszel method in the samples of both the North American Rheumatoid Arthritis Consortium (NARAC) and the Epidemiological Investigation of Rheumatoid Arthritis (EIRA). The recombination rate (in cM/Mb) with the CEU HapMap is shown in light blue along the x axis; the red arrow indicates the block of LD shown in panel B. The blue arrows indicate gene location. Panel B shows the LD structure across 200 kb of the TRAF1-C5 locus, based on pairwise r ² with the CEU HapMap. The intron-exon structure of each gene is at the top of the figure. Putative functional SNPs in LD with either rs3761847 or rs2900180 are indicated by hatched bars in which red indicates r ² >0.80 and pink indicates r ² = 0.20 to 0.80; the specific SNPs, frequency, pairwise r ² with the CEU HapMap, and the putative annotated function are listed at the bottom of the figure. CpG denotes cytidine and guanosine joined by a phosphodiester bond.

Modified from Plenge RM, Seielstad M, Padyukov L, et al. TRAF1-C5 as a risk locus for rheumatoid arthritis—a genomewide study. N Engl J Med 2007;357(12):119–209; with permission.

There is often more than 1 gene in the region of LD that harbors the genotyped/associated SNP, which makes it difficult to pinpoint definitively which gene is the causal gene. On the other hand, there may be no nearby gene in the region of LD. Here causal gene is the single gene that is altered by a mutation to give rise to the phenotype of interest (eg, risk of RA). For convenience, the best biologic gene, based on its known function, is often nominated as the “causal gene.” Fig. 1 illustrates that there are 3 genes in a region of LD at the locus on chromosome 9, 2 of which are very strong biologic candidate genes: TRAF1 (encoding tumor necrosis factor (TNF) α receptor–associated factor 1) and C5 (encoding complement component 5). Thus, this locus is referred to as the TRAF1-C5 RA risk locus.

For most of the 20 RA risk alleles shown in Table 1 , the causal mutation and the causal gene are yet to be identified. Outside of the MHC, the 1 exception is PTPN22 in which the associated mutation alters protein structure and function. Although it may be reasonable to nominate the most likely biologic candidate gene to be the causal gene, direct evidence is not yet available.

There are at least 2 reasons why it is important to identify (or “fine map”) the causal mutation. First, knowing the causal mutation helps guide functional studies. For drug discovery, it is crucial to understand if the risk allele is a gain-of-function or loss-of-function allele. Second, knowing the causal allele provides more accurate estimates of risk that could facilitate disease prediction. If the associated SNP is highly but not perfectly correlated with the causal allele, then risk estimates will be deflated.

A limitation of contemporary GWASs is that they only test common SNPs. For every common allele (defined as having an allele frequency of >5% in the general population), there is at least 1, and likely many more, rare alleles (frequency <5%). In addition, there are other forms of genetic variation besides SNPs, including copy number variants (in which a gene may be duplicated or deleted). Next-generation sequencing and genotyping technologies are required to identify and test rare variants and structural variants.