In PLOS One, Translational Genomics Research Institute researcher Daniel Von Hoff and his colleagues describe findings from a whole-genome sequencing study of pancreatic cancer. The group, which included investigators from TGen, Scottsdale's Mayo Clinic, the Virginia G. Piper Cancer Center Clinical Trials, and Arizona State University, sequenced matched tumor and normal samples from three individuals with pancreatic adenocarcinoma. Analyses of these sequences unearthed 142 somatic alterations, including point mutations, small insertion/deletions, and copy number changes. Some of the genetic glitches affected genes implicated in pancreatic cancer before, though the team also linked new genes, pathways, and mutation types to the disease. Folding RNA sequence data for two of the tumors also helped in exploring the gene expression consequences of cancer-related mutations, the study's authors say, and pointed to potential treatment targets for pancreatic adenocarcinoma.
A transcriptome study of shrimp in PLOS One offers a peek at the genes and pathways that are active in the animal's larval stage. Researchers based in China and the US used Illumina's GAII platform to sequence RNA from 100 larvae that had been collected from Pacific white shrimp, Litopenaeus vannamei, at a Chinese shrimp farm. In addition to assembling the sequence data and annotating tens of thousands of unigenes in the Pacific white shrimp transcriptome, the team organized these unigenes based on their similarity to known genes as well as their predicted functional roles, and pathways. Together, those involved in the study say, this dataset "provides useful information for the studies on genomes and functional genes of L. vannamei and crustaceans."
A team from 23andMe and Stanford University provides a comparison of complex disease risk prediction based on family history or SNP-based strategies in PLOS Genetics. The researchers used quantitative genetic theory to assess the performance of family history- or SNP-based methods for predicting risk for 23 diseases, looking at the influences that disease heritability and population frequency had on the reliability of each approach. For instance, their findings suggest that family history performs fairly well for predicting risk for diseases that are very common and highly heritable, such as Alzheimer's disease or coronary artery disease, whereas SNP-based tests seemed to have an edge for predicting conditions that are less common in the population, including schizophrenia and celiac disease. Based on their findings, the study's authors say that "while family history-based methods are sometimes more effective for highly common diseases, SNP-based risk assessments tend to be more powerful for less common disorders."
"We use these findings not to argue that SNP-based assessments should replace the use of family history in the clinic," 23andMe's Chuong Do and his colleagues add, "but rather to suggest that SNP-based assessments and family history are best viewed as complementary tools for understanding an individual's predisposition to disease."