![]() Project leader Professor Peter Visscher said because the cost of a single whole genome sequence is approximately $1000 per sample, the case for genotyping using SNP arrays is made compelling with current prices of around $50 per sample. “SNP arrays only study a fraction of the genome, but the DNA variation of the rest genome can be imputed with very high accuracy using the imputation analysis technique.” “Statistical imputation works like magic by accurately guessing that DNA variants that we haven’t directly measured are present, by pulling this information from libraries of data that have already been collected. “Some data sets currently have half a million people, so there is an astronomical cost to studying samples using whole genome sequencing. “Like a political opinion poll, a greater sample size increases the statistical power of the result, and the more likely you are to have an accurate representation,” he said. “When we study the genome for genes that are linked to a disease we need to test tens or even hundreds of thousands of samples, because even for most common diseases such as schizophrenia and obesity the effect of a single gene on the risk to a disease is tiny,” Associate Professor Yang said. Study lead author Associate Professor Jian Yang from UQ’s Queensland Brain Institute said by using the cheaper method studies may then be able to analyse more samples, which increases the statistical power of the findings.Īssociate Professor Yang said genomics research relies on gathering as many samples as possible to discover genes associated with illnesses, which results in possible targets for therapeutics. ![]() Research at The University of Queensland has found that using a vastly cheaper strategy of genetic analysis can be almost as efficient as a more expensive method.įindings published in Nature Genetics show that the use of cheaper single nucleotide polymorphism (SNP) arrays followed by a statistical trick called imputation can perform almost as efficiently as whole genome sequencing in identifying genes associated with human complex traits or diseases. ![]()
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