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Circulating proteins can be used to diagnose and predict disease-related outcomes. A deep serum proteome survey recently revealed close associations between serum protein networks and common disease. In the current study, 54,469 low-frequency and common exome-array variants were compared to 4782 protein measurements in the serum of 5343 individuals from the AGES Reykjavik cohort. This analysis identifies a large number of serum proteins with genetic signatures overlapping those of many diseases. More specifically, using a study-wide significance threshold, we find that 2021 independent exome array variants are associated with serum levels of 1942 proteins. These variants reside in genetic loci shared by hundreds of complex disease traits, highlighting serum proteins' emerging role as biomarkers and potential causative agents of a wide range of diseases. Finding the genetic basis of protein expression can elucidate the genetic mechanisms of disease. Here, the authors link low-frequency and common DNA sequence variants to thousands of serum proteins, finding genetic overlap between circulating proteins and a wide range of common diseases. [ABSTRACT FROM AUTHOR]

Age-related macular degeneration (AMD) is one of the most common causes of visual impairment in the elderly, with a complex and still poorly understood etiology. Whole-genome association studies have discovered 34 genomic regions associated with AMD. However, the genes and cognate proteins that mediate the risk, are largely unknown. In the current study, we integrate levels of 4782 human serum proteins with all genetic risk loci for AMD in a large population-based study of the elderly, revealing many proteins and pathways linked to the disease. Serum proteins are also found to reflect AMD severity independent of genetics and predict progression from early to advanced AMD after five years in this population. A two-sample Mendelian randomization study identifies several proteins that are causally related to the disease and are directionally consistent with the observational estimates. In this work, we present a robust and unique framework for elucidating the pathobiology of AMD. Age related macular degeneration is a common cause of visual impairment in the elderly, but the etiology is not fully understood. Here, the authors use genetic data, serum proteomics, and AMD phenotypic data from a large Icelandic cohort to discover proteins altered in, causally related to AMD or signifying progression of advanced AMD. [ABSTRACT FROM AUTHOR]

With the growing number of genetic association studies, the genotype-phenotype atlas has become increasingly more complex, yet the functional consequences of most disease associated alleles is not understood. The measurement of protein level variation in solid tissues and biofluids integrated with genetic variants offers a path to deeper functional insights. Here we present a large-scale proteogenomic study in 5,368 individuals, revealing 4,035 independent associations between genetic variants and 2,091 serum proteins, of which 36% are previously unreported. The majority of both cis- and trans-acting genetic signals are unique for a single protein, although our results also highlight numerous highly pleiotropic genetic effects on protein levels and demonstrate that a protein's genetic association profile reflects certain characteristics of the protein, including its location in protein networks, tissue specificity and intolerance to loss of function mutations. Integrating protein measurements with deep phenotyping of the cohort, we observe substantial enrichment of phenotype associations for serum proteins regulated by established GWAS loci, and offer new insights into the interplay between genetics, serum protein levels and complex disease. Circulating proteins have been linked to many conditions, and understanding their genetic control can lead to understanding of disease. Here, the authors associate common genetic variants with protein levels, finding overlap of genetic associations with circulating proteins and complex disease. [ABSTRACT FROM AUTHOR]

Common human diseases result from the interplay of many genes and environmental factors. Therefore, a more integrative biology approach is needed to unravel the complexity and causes of such diseases. To elucidate the complexity of common human diseases such as obesity, we have analysed the expression of 23,720 transcripts in large population-based blood and adipose tissue cohorts comprehensively assessed for various phenotypes, including traits related to clinical obesity. In contrast to the blood expression profiles, we observed a marked correlation between gene expression in adipose tissue and obesity-related traits. Genome-wide linkage and association mapping revealed a highly significant genetic component to gene expression traits, including a strong genetic effect of proximal (cis) signals, with 50% of the cis signals overlapping between the two tissues profiled. Here we demonstrate an extensive transcriptional network constructed from the human adipose data that exhibits significant overlap with similar network modules constructed from mouse adipose data. A core network module in humans and mice was identified that is enriched for genes involved in the inflammatory and immune response and has been found to be causally associated to obesity-related traits. [ABSTRACT FROM AUTHOR]

Identifying variations in DNA that increase susceptibility to disease is one of the primary aims of genetic studies using a forward genetics approach. However, identification of disease-susceptibility genes by means of such studies provides limited functional information on how genes lead to disease. In fact, in most cases there is an absence of functional information altogether, preventing a definitive identification of the susceptibility gene or genes. Here we develop an alternative to the classic forward genetics approach for dissecting complex disease traits where, instead of identifying susceptibility genes directly affected by variations in DNA, we identify gene networks that are perturbed by susceptibility loci and that in turn lead to disease. Application of this method to liver and adipose gene expression data generated from a segregating mouse population results in the identification of a macrophage-enriched network supported as having a causal relationship with disease traits associated with metabolic syndrome. Three genes in this network, lipoprotein lipase (Lpl), lactamase β (Lactb) and protein phosphatase 1-like (Ppm1l), are validated as previously unknown obesity genes, strengthening the association between this network and metabolic disease traits. Our analysis provides direct experimental support that complex traits such as obesity are emergent properties of molecular networks that are modulated by complex genetic loci and environmental factors. [ABSTRACT FROM AUTHOR]

We conducted a genome-wide association study for type 2 diabetes (T2D) in Icelandic cases and controls, and we found that a previously described variant in the transcription factor 7-like 2 gene (TCF7L2) gene conferred the most significant risk. In addition to confirming two recently identified risk variants, we identified a variant in the CDKAL1 gene that was associated with T2D in individuals of European ancestry (allele-specific odds ratio (OR) = 1.20 (95% confidence interval, 1.13–1.27), P = 7.7 × 10−9) and individuals from Hong Kong of Han Chinese ancestry (OR = 1.25 (1.11–1.40), P = 0.00018). The genotype OR of this variant suggested that the effect was substantially stronger in homozygous carriers than in heterozygous carriers. The ORs for homozygotes were 1.50 (1.31–1.72) and 1.55 (1.23–1.95) in the European and Hong Kong groups, respectively. The insulin response for homozygotes was approximately 20% lower than for heterozygotes or noncarriers, suggesting that this variant confers risk of T2D through reduced insulin secretion. [ABSTRACT FROM AUTHOR]

We recently described an association between risk of type 2diabetes and variants in the transcription factor 7-like 2 gene (TCF7L2; formerly TCF4), with a population attributable risk (PAR) of 17%–28% in three populations of European ancestry. Here, we refine the definition of the TCF7L2 type 2diabetes risk variant, HapBT2D, to the ancestral T allele of a SNP, rs7903146, through replication in West African and Danish type 2 diabetes case-control studies and an expanded Icelandic study. We also identify another variant of the same gene, HapA, that shows evidence of positive selection in East Asian, European and West African populations. Notably, HapA shows a suggestive association with body mass index and altered concentrations of the hunger-satiety hormones ghrelin and leptin in males, indicating that the selective advantage of HapA may have been mediated through effects on energy metabolism. [ABSTRACT FROM AUTHOR]

We have previously reported suggestive linkage of type 2 diabetes mellitus to chromosome 10q. We genotyped 228 microsatellite markers in Icelandic individuals with type 2 diabetes and controls throughout a 10.5-Mb interval on 10q. A microsatellite, DG10S478, within intron 3 of the transcription factor 7–like 2 gene (TCF7L2; formerly TCF4) was associated with type 2 diabetes (P = 2.1 × 10−9). This was replicated in a Danish cohort (P = 4.8 × 10−3) and in a US cohort (P = 3.3 × 10−9). Compared with non-carriers, heterozygous and homozygous carriers of the at-risk alleles (38% and 7% of the population, respectively) have relative risks of 1.45 and 2.41. This corresponds to a population attributable risk of 21%. The TCF7L2 gene product is a high mobility group box–containing transcription factor previously implicated in blood glucose homeostasis. It is thought to act through regulation of proglucagon gene expression in enteroendocrine cells via the Wnt signaling pathway. [ABSTRACT FROM AUTHOR]