Genome-wide association studies have identified hundreds of genetic variants associated with complex human diseases and traits, and have provided valuable insights into their genetic architecture. Most variants identified so far confer relatively small increments in risk, and explain only a small proportion of familial clustering, leading many to question how the remaining, 'missing' heritability can be explained. Here we examine potential sources of missing heritability and propose research strategies, including and extending beyond current genome-wide association approaches, to illuminate the genetics of complex diseases and enhance its potential to enable effective disease prevention or treatment.Many common human diseases and traits are known to cluster in families and are believed to be influenced by several genetic and environmental factors, but until recently the identification of genetic variants contributing to these 'complex diseases' has been slow and arduous 1 . Genome-wide association studies (GWAS), in which several hundred thousand to more than a million single nucleotide polymorphisms (SNPs) are assayed in thousands of individuals, represent a powerful new tool for investigating the genetic architecture of complex diseases 1, 2. In the past few years, these studies have identified hundreds of genetic variants associated with such conditions and have provided valuable insights into the complexities of their genetic architecture3 , 4.The genome-wide association (GWA) method represents an important advance compared to 'candidate gene' studies, in which sample sizes are generally smaller and the variants assayed are limited to a selected few, often on the basis of imperfect understanding of biological pathways and often yielding associations that are difficult to replicate 5,6. GWAS are also an important step beyond family-based linkage studies, in which inheritance patterns are related to several hundreds to thousands of genomic markers. Despite many clear successes in singlegene 'Mendelian' disorders7 , 8, the limited success of linkage studies in complex diseases has been attributed to their low power and resolution for variants of modest effect 9-11 .The underlying rationale for GWAS is the 'common disease, common variant' hypothesis, positing that common diseases are attributable in part to allelic variants present in more than 1-5% of the population12 -14. They have been facilitated by the development of commercial 'SNP chips' or arrays that capture most, although not all, common variation in the genome. Although the allelic architecture of some conditions, notably age-related macular degeneration, for the most part reflects the contributions of several variants of large effect (defined loosely here as those increasing disease risk by twofold or more), most common variants individually or in combination confer relatively small increments in risk (1.1-1.5-fold) and explain only a small proportion of heritability-the portion of phenotypic variance in a population attributable to additive ...

Genome-wide association studies have identified thousands of loci for common diseases, but, for the majority of these, the mechanisms underlying disease susceptibility remain unknown. Most associated variants are not correlated with protein-coding changes, suggesting that polymorphisms in regulatory regions probably contribute to many disease phenotypes. Here we describe the Genotype-Tissue Expression (GTEx) project, which will establish a resource database and associated tissue bank for the scientific community to study the relationship between genetic variation and gene expression in human tissues

A novel coronavirus is associated with this outbreak, and the evidence indicates that this virus has an etiologic role in SARS. Because of the death of Dr. Carlo Urbani, we propose that our first isolate be named the Urbani strain of SARS-associated coronavirus.

Understanding the functional consequences of genetic variation, and how it affects complex human disease and quantitative traits, remains a critical challenge for biomedicine. We present an analysis of RNA sequencing data from 1641 samples across 43 tissues from 175 individuals, generated as part of the pilot phase of the Genotype-Tissue Expression (GTEx) project. We describe the landscape of gene expression across tissues, catalog thousands of tissue-specific and shared regulatory expression quantitative trait loci (eQTL) variants, describe complex network relationships, and identify signals from genome-wide association studies explained by eQTLs. These findings provide a systematic understanding of the cellular and biological consequences of human genetic variation and of the heterogeneity of such effects among a diverse set of human tissues.

N. M. (2003). Enhancement of antibody responses to influenza vaccination in the elderly following a cognitivebehavioural stress management intervention. Psychotherapy and Psychosomatics, 72(5), 245-252.

Genome-wide association studies (GWAS) have identified thousands of variants robustly associated with complex traits. However, the biological mechanisms underlying these associations are, in general, not well understood. We propose a gene-based association method called PrediXcan that directly tests the molecular mechanisms through which genetic variation affects phenotype. The approach estimates the component of gene expression determined by an individual’s genetic profile and correlates the “imputed” gene expression with the phenotype under investigation to identify genes involved in the etiology of the phenotype. The genetically regulated gene expression is estimated using whole-genome tissue-dependent prediction models trained with reference transcriptome datasets. PrediXcan enjoys the benefits of gene-based approaches such as reduced multiple testing burden and a principled approach to the design of follow-up experiments. Our results demonstrate that PrediXcan can detect known and novel genes associated with disease traits and provide insights into the mechanism of these associations.

Since its identification in April 2009 an A(H1N1) virus containing a unique combination of gene segments from both North American and Eurasian swine lineages has continued to circulate in humans. The 2009 A(H1N1) virus is distantly related to its nearest relatives, indicating that its gene segments have been circulating undetected for an extended period. Low genetic diversity among the viruses suggests the introduction into humans was a single event or multiple events of similar viruses. Molecular markers predicted for adaptation to humans are not currently present in 2009 A(H1N1) viruses, suggesting previously unrecognized molecular determinants could be responsible for the transmission among humans. Antigenically the viruses are homogeneous and similar to North American swine A(H1N1) viruses but distinct from seasonal human A(H1N1).

Context Respiratory viral infections are responsible for a large number of hospitalizations in the United States each year. Objective To estimate annual influenza-associated hospitalizations in the United States by hospital discharge category, discharge type, and age group. Design, Setting, and Participants National Hospital Discharge Survey (NHDS) data and World Health Organization Collaborating Laboratories influenza surveillance data were used to estimate annual average numbers of hospitalizations associated with the circulation of influenza viruses from the 1979-1980 through the 2000-2001 seasons in the United States using age-specific Poisson regression models. Main Outcome Measures We estimated influenza-associated hospitalizations for primary and any listed pneumonia and influenza and respiratory and circulatory hospitalizations. Results Annual averages of 94735 (range, 18 908-193 561) primary and 133900 (range, 30 757-271 529) any listed pneumonia and influenza hospitalizations were associated with influenza virus infections. Annual averages of 226 054 (range, 54 523-430 960) primary and 294128 (range, 86 494-544 909) any listed respiratory and circulatory hospitalizations were associated with influenza virus infections. Persons 85 years or older had the highest rates of influenza-associated primary respiratory and circulatory hospitalizations (1194.9 per 100 000 persons). Children younger than 5 years (107.9 primary respiratory and circulatory hospitalizations per 100 000 persons) had rates similar to persons aged 50 through 64 years. Estimated rates of influenzaassociated hospitalizations were highest during seasons in which A(H3N2) viruses predominated, followed by B and A(H1N1) seasons. After adjusting for the length of each influenza season, influenza-associated primary pneumonia and influenza hospitalizations increased over time among the elderly. There were no significant increases in influenza-associated primary respiratory and circulatory hospitalizations after adjusting for the length of the influenza season. Conclusions Significant numbers of influenza-associated hospitalizations in the United States occur among the elderly, and the numbers of these hospitalizations have increased substantially over the last 2 decades due in part to the aging of the population. Children younger than 5 years had rates of influenza-associated hospitalizations similar to those among individuals aged 50 through 64 years. These findings highlight the need for improved influenza prevention efforts for both young and older US residents.