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Study of Rare Variants in Pinpointing Disease-Causing Genes
Genome-wide association studies (GWAS) use gene chips in automated systems
that analyze about 500,000 to one million sites where SNPs tend to occur. In using
these SNP chips over the past decade in comparing DNA samples between healthy
subjects and patients, scientists have identifi ed thousands of SNPs that associate
with common complex diseases. However, SNPs investigated by the gene chips do
not themselves cause a disease, but instead serve as a marker linked to the actual
causal mutations that may reside in a nearby region. After a GWAS fi nds SNPs
linked to a disease, researchers then perform a “fi ne-mapping” study by additional
genotyping, i.e. sequencing of the gene regions near the SNP signal, to uncover an
altered gene that harbors a mutation responsible for the disease.
GWAS have been successful in identifying disease susceptibility loci, but pin-
pointing of the causal variants in subsequent fi ne-mapping studies remains a chal-
lenge. A conventional fi ne-mapping effort starts by sequencing dozens of randomly
selected samples at susceptibility loci to discover candidate variants, which are then
placed on custom arrays and algorithms are used to fi nd the causal variants. A new
study challenges the prevailing view that common diseases are usually caused by
common gene variants (mutations) but the culprits may be numerous rare variants,
located in DNA sequences farther away from the original “hot spots” than scientists
have been accustomed to look (Wang et al. 2010 ). The authors propose that one or
several rare or low-frequency causal variants can hitchhike the same common tag
SNP so that they may not be easily unveiled by conventional efforts. They demon-
strated that the true effect size and proportion of variance is explained by a collec-
tion of rare causal variants, which can be underestimated by a common tag SNP,
thereby accounting for some of the “missing heritability” in GWAS. Sequencing
DNA in subset of patients most likely to carry causative mutations leads to identifi -
cation of more actual mutations. This refi ned technique may identify individuals
more likely to have mutations in causal genes. By applying their methods to real
DNA samples from patients with genetic hearing loss, the researchers’ approach
helped them to select from GWAS data a subset of cases for sequencing analysis
that were most likely to carry causative mutations. Sequencing the DNA in this
subset, the study team found that the majority of those patients carried an actual
mutation known to cause hearing loss. This approach will facilitate personalized
medicine, in which treatment will be tailored to an individual’s genetic profi le.
Identifying causal variants in disease genes provides an opportunity to develop
drugs to rectify the biological consequences of these mutated genes.
GWAS have identifi ed multiple loci associated with plasma lipid concentrations.
Common variants at these loci together explain <10 % of variation in each lipid
trait. Rare variants with large individual effects may also contribute to the heritabil-
ity of lipid traits. A study has shown an accumulation of rare variants, or a mutation
skew, in GWAS-identifi ed genes in individuals with hypertriglyceridemia (Johansen
et al. 2010 ). Through GWAS, the authors identifi ed common variants in APOA5,
GCKR, LPL and APOB associated with hypertriglyceridemia. Resequencing of
these genes revealed a signifi cant burden of rare missense or nonsense variants in
16 Personalized Management of Genetic Disorders