A tag SNP is a representative single nucleotide polymorphism (SNP) in a region of the genome with high linkage disequilibrium that represents a group of SNPs called a haplotype. It is possible to identify genetic variation and association to phenotypes without genotyping every SNP in a chromosomal region. This reduces the expense and time of mapping genome areas associated with disease, since it eliminates the need to study every individual SNP. Tag SNPs are useful in whole-genome SNP association studies in which hundreds of thousands of SNPs across the entire genome are genotyped.
Introduction
Linkage disequilibrium
Two loci are said to be in linkage equilibrium (LE) if their inheritance is an independent event. If the alleles at those loci are non-randomly inherited then we say that they are at linkage disequilibrium (LD). LD is most commonly caused by physical linkage of genes. When two genes are inherited on the same chromosome, depending on their distance and the likelihood of recombination between the loci they can be at high LD. However, LD can be also observed due to functional interactions where even genes from different chromosomes can jointly confer an evolutionarily selected phenotype or can affect the viability of potential offspring.
In families LD is highest because of the lowest numbers of recombination events (fewest meiosis events). This is especially true between inbred lines. In populations LD exists because of selection, physical closeness of the genes that causes low recombination rates or due to recent crossing or migration. On a population level, processes that influence linkage disequilibrium include genetic linkage, epistatic natural selection, rate of recombination, mutation, genetic drift, random mating, genetic hitchhiking and gene flow.
When a group of SNPs are inherited together because of high LD there tends to be redundant information. The selection of a tag SNP as a representative of these groups reduces the amount of redundancy when analyzing parts of the genome associated with traits/diseases. The regions of the genome in high LD that harbor a specific set of SNPs that are inherited together are also known as haplotypes. Therefore, tag SNPs are representative of all SNPs within a haplotype.
Haplotypes
The selection of tag SNPs is dependent on the haplotypes present in the genome. Most sequencing technologies provide the genotypic information and not the haplotypes i.e. they provide information on the specific bases that are present but do not provide phasic information (at which specific chromosome each of the bases appear). Determination of haplotypes can be done through molecular methods (allele specific PCR, somatic cell hybrids). These methods distinguish which allele is present at which chromosome by separating the chromosomes before genotyping. They can be very time-consuming and expensive, so statistical inference methods have been developed as a less expensive and automated option. These statistical-inference software packages utilize parsimony, maximum likelihood, and Bayesian algorithms to determine haplotypes. Disadvantage of statistical-inference is that a proportion of the inferred haplotypes could be wrong.
Population differences
Often different populations will have different patterns of LD. One example of differentiating patterns are African-descended populations vs. European and Asian-descended populations. Since humans originated in Africa and spread into Europe and then the Asian and American continents, the African populations are the most genetically diverse and have smaller regions of LD while European and Asian-descended populations have larger regions of LD due to founder effect. When LD patterns differ in populations, SNPs can become disassociated with each other due to the changes in haplotype blocks. This means that tag SNPs, as representatives of the haplotype blocks, are unique in populations and population differences should be taken into account when performing association studies.
Application
Genome-wide association studies
Almost every trait has both genetic and environmental influence. Heritability is the proportion of phenotypic variance that is inherited from our ancestors. Association studies are used to determine the genetic influence on phenotypic presentation. Although mostly used for mapping diseases to genomic areas, they can also be used to map heritability of any phenotype like height, eye color etc.
Genome-wide association studies (GWAS) use single-nucleotide polymorphisms (SNP) to identify genetic associations with clinical conditions and phenotypic traits. They are hypothesis free and use a whole-genome approach to investigate traits by comparing a large group of individuals that express a phenotype with a large group of people that don't. The ultimate goal of GWAS is to determine genetic risk factors that can be used to make predictions about who is at risk for a disease, what are the biological underpinnings of disease susceptibility and creating new prevention and treatment strategies. The National Human Genome Research Institute and the European Bioinformatics Institute publishes the GWAS Catalog, a catalog of published genome-wide association studies that highlights statistically significant associations between hundreds of SNPs with a broad range of phenotypes.
Due to the large number of possible SNP variants (more than 149 million as of June 2015) it is still very expensive to sequence all SNPs. That is why GWAS use customizable arrays (SNP chips) to genotype only a subset of the variants identified as tag snps. Most GWAS use products from the two primary genotyping platforms. The Affymetrix platform prints DNA probes on a glass or silicone chip that hybridize to specific alleles in the sample DNA. The Illumina platform uses bead-based technology, with longer DNA sequences and produces better specificity. Both platforms are able to genotype more than a million tag SNPs using either pre-made or custom DNA oligos.
Genome-wide studies are predicated on the common disease-common variant (CD/CV) hypothesis which states that common disorders are influenced by common genetic variation. Effect size (penetrance) of the common variants needs to be smaller relative to those found in rare disorders. That means that the common SNP can explain only a small portion of the variance due to genetic factors and that common diseases are influenced by multiple common alleles of small effect size. Another hypothesis is that common diseases are caused by rare variants that are synthetically linked to common variants. In that case the signal produced from GWAS is an indirect (synthetic) association between one or more rare causal variants in linkage disequilibrium. It is important to recognize that this phenomenon is possible when selecting a group for tag SNPs. When a disease is found to be associated with a haplotype, some SNPs in that haplotype will have synthetic association with the disease. To pinpoint the causal SNPs we need a greater resolution in the selection of haplotype blocks. Since whole genome sequencing technologies are rapidly changing and becoming less expensive it is likely that they will replace the current genotyping technologies providing the resolution needed to pinpoint causal variants.
HapMap
Because whole genome sequencing of individuals is still cost prohibitive, the international HapMap Project was constructed with a goal to map the human genome to haplotype groupings (haplotype blocks) that can describe common patterns of human genetic variation. By mapping the entire genome to haplotypes, tag SNPs can be identified to represent the haplotype blocks examined by genetic studies. An important factor to consider when planning a genetic study is the frequency and risk incurred by specific alleles. These factors can vary in different populations so the HapMap project used a variety of sequencing techniques to discover and catalog SNPs from different sets of populations. Initially the project sequenced individuals from Yoruba population of African origin (YRI), residents of Utah with western European ancestry (CEU), unrelated individuals from Tokyo, Japan (JPT) and unrelated Han Chinese individuals from Beijing, China (CHB). Recently their datasets have been expanded to include other populations (11 groups).
Selection and evaluation
Steps for tag SNP selection
Selection of maximum informative tag SNPs is an NP complete problem. However, algorithms can be devised to provide approximate solution within a margin of error. The criteria that are needed to define each tag SNP selection algorithm is the following:
Define area to search - the algorithm will attempt to locate tag SNPs in neighborhood N(t) of a target SNP t Define a metric to assess the quality of tagging - the metric needs to measure how well a target SNP t can be predicted using a set of its neighbors N(t) i.e. how well a tag SNP as a representative of the SNPs in a neighborhood N(t) can predict a target SNP t. It can be defined as a probability that the target SNP t has different values for any pair of haplotypes i and j where the value of the SNP s is also different for the same haplotypes. The informativeness of the metric can be represented in terms of a graph theory, where every SNP s is represented as a graph Gs whose nodes are haplotypes. Gs has an edge between the nodes (i,j) if and only if the values of s are different for the haplotypes Hi, Hj. Derive the algorithm to find representative SNPs - the goal of the algorithm is to find the minimal subset of tag SNPs selected with maximum informativeness between each tag SNP with every other target SNP Validate the algorithm
Feature selection
Methods for selecting features fall into two categories: filter methods and wrapper methods. Filter algorithms are general preprocessing algorithms that do not assume the use of a specific classification method. Wrapper algorithms, in contrast, "wrap" the feature selection around a specific classifier and select a subset of features based on the classifier's accuracy using cross-validation.
The feature selection method suitable for selecting tag SNPs must have the following characteristics: scale well for large number of SNPs; not require explicit class labeling and should not assume the use of a specific classifier because classification is not the goal of tagging SNP selection; allow the user to select different numbers of tag SNPs for different amounts of tolerated information loss; have comparable performance with other methods satisfying the three first conditions.
Selection algorithms
Several algorithms have been proposed for selecting tag SNPs. The first approach was based on the measure of goodness of SNP sets and searched for SNP subsets that are small but attain high value of the defined measure. Examining every SNP subset to find good ones is computationally feasible only for small data sets.
Another approach uses principal component analysis (PCA) to find subsets of SNPs capturing majority of the data variance. A sliding windows method is employed to repeatedly apply PCA to short chromosomal regions. This reduces the data produced and also does not require exponential search time. Yet it is not feasible to apply the PCA method to large chromosomal data sets as it is computationally complex.