Population genetic screening efficiently identifies carriers of autosomal dominant diseases.

Three inherited autosomal dominant conditions-BRCA-related hereditary breast and ovarian cancer (HBOC), Lynch syndrome (LS) and familial hypercholesterolemia (FH)-have been termed the Centers for Disease Control and Prevention Tier 1 (CDCT1) genetic conditions, for which early identification and intervention have a meaningful potential for clinical actionability and a positive impact on public health1. In typical medical practice, genetic testing for these conditions is based on personal or family history, ethnic background or other demographic characteristics2. In this study of a cohort of 26,906 participants in the Healthy Nevada Project (HNP), we first evaluated whether population screening could efficiently identify carriers of these genetic conditions and, second, we evaluated the impact of genetic risk on health outcomes for these participants. We found a 1.33% combined carrier rate for pathogenic and likely pathogenic (P/LP) genetic variants for HBOC, LS and FH. Of these carriers, 21.9% of participants had clinically relevant disease, among whom 70% had been diagnosed with relevant disease before age 65. Moreover, 90% of the risk carriers had not been previously identified, and less than 19.8% of these had documentation in their medical records of inherited genetic disease risk, including family history. In a direct follow-up survey with all carriers, only 25.2% of individuals reported a family history of relevant disease. Our experience with the HNP suggests that genetic screening in patients could identify at-risk carriers, who would not be otherwise identified in routine care.

Genome-wide association analysis identifies genetic variations in subjects with myalgic encephalomyelitis/chronic fatigue syndrome.

Myalgic encephalomyelitis, also known as chronic fatigue syndrome or ME/CFS, is a multifactorial and debilitating disease that has an impact on over 4 million people in the United States alone. The pathogenesis of ME/CFS remains largely unknown; however, a genetic predisposition has been suggested. In the present study, we used a DNA single-nucleotide polymorphism (SNP) chip representing over 906,600 known SNPs to analyze DNA from ME/CFS subjects and healthy controls. To the best of our knowledge, this study represents the most comprehensive genome-wide association study (GWAS) of an ME/CFS cohort conducted to date. Here 442 SNPs were identified as candidates for association with ME/CFS (adjusted P-value<0.05). Whereas the majority of these SNPs are represented in non-coding regions of the genome, 12 SNPs were identified in the coding region of their respective gene. Among these, two candidate SNPs resulted in missense substitutions, one in a pattern recognition receptor and the other in an uncharacterized coiled-coil domain-containing protein. We also identified five SNPs that cluster in the non-coding regions of T-cell receptor loci. Further examination of these polymorphisms may help identify contributing factors to the pathophysiology of ME/CFS, as well as categorize potential targets for medical intervention strategies.

Adding confidence to gene expression clustering.

It has been well established that gene expression data contain large amounts of random variation that affects both the analysis and the results of microarray experiments. Typically, microarray data are either tested for differential expression between conditions or grouped on the basis of profiles that are assessed temporally or across genetic or environmental conditions. While testing differential expression relies on levels of certainty to evaluate the relative worth of various analyses, cluster analysis is exploratory in nature and has not had the benefit of any judgment of statistical inference. By using a novel dissimilarity function to ascertain gene expression clusters and conditional randomization of the data space to illuminate distinctions between statistically significant clusters of gene expression patterns, we aim to provide a level of confidence to inferred clusters of gene expression data. We apply both permutation and convex hull approaches for randomization of the data space and show that both methods can provide an effective assessment of gene expression profiles whose coregulation is statistically different from that expected by random chance alone.