Skeptics have questioned the value of genomewide association studies. Dr. Joel Hirschhorn writes that the main goal of these studies is not prediction of individual risk but rather discovery of ...biologic pathways underlying polygenic diseases and traits.
Human geneticists seek to understand the inherited basis of human biology and disease, aiming either to gain insights that could eventually improve treatment or to produce useful diagnostic or predictive tests. As recently as 2004, few genetic variants were known to reproducibly influence common polygenic diseases (including cancer, coronary artery disease, and diabetes) or quantitative phenotypes (including lipid levels and blood pressure). This relative ignorance limited potential insights into the pathophysiology of common diseases.
The completion of the human genome sequence in 2005 and the provision of an initial catalogue of human genetic variation and a haplotype map (known as . . .
Genetic factors strongly affect susceptibility to common diseases and also influence disease-related quantitative traits. Identifying the relevant genes has been difficult, in part because each ...causal gene only makes a small contribution to overall heritability. Genetic association studies offer a potentially powerful approach for mapping causal genes with modest effects, but are limited because only a small number of genes can be studied at a time. Genome-wide association studies will soon become possible, and could open new frontiers in our understanding and treatment of disease. However, the execution and analysis of such studies will require great care.
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Dostopno za:
DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Unhealthful dietary habits are leading risk factors for life-altering diseases and mortality. Large-scale biobanks now enable genetic analysis of traits with modest heritability, such as diet. We ...perform a genomewide association on 85 single food intake and 85 principal component-derived dietary patterns from food frequency questionnaires in UK Biobank. We identify 814 associated loci, including olfactory receptor associations with fruit and tea intake; 136 associations are only identified using dietary patterns. Mendelian randomization suggests our top healthful dietary pattern driven by wholemeal vs. white bread consumption is causally influenced by factors correlated with education but is not strongly causal for coronary artery disease or type 2 diabetes. Overall, we demonstrate the value in complementary phenotyping approaches to complex dietary datasets, and the utility of genomic analysis to understand the relationships between diet and human health.
The main challenge for gaining biological insights from genetic associations is identifying which genes and pathways explain the associations. Here we present DEPICT, an integrative tool that employs ...predicted gene functions to systematically prioritize the most likely causal genes at associated loci, highlight enriched pathways and identify tissues/cell types where genes from associated loci are highly expressed. DEPICT is not limited to genes with established functions and prioritizes relevant gene sets for many phenotypes.
An important computational step following genome-wide association studies (GWAS) is to assess whether disease or trait-associated single-nucleotide polymorphisms (SNPs) enrich for particular ...biological annotations. SNP-based enrichment analysis needs to account for biases such as co-localization of GWAS signals to gene-dense and high linkage disequilibrium (LD) regions, and correlations of gene size, location and function. The SNPsnap Web server enables SNP-based enrichment analysis by providing matched sets of SNPs that can be used to calibrate background expectations. Specifically, SNPsnap efficiently identifies sets of randomly drawn SNPs that are matched to a set of query SNPs based on allele frequency, number of SNPs in LD, distance to nearest gene and gene density.
SNPsnap server is available at http://www.broadinstitute.org/mpg/snpsnap/.
joelh@broadinstitute.org
Supplementary data are available at Bioinformatics online.
Genetic Evaluation of Short Stature Dauber, Andrew; Rosenfeld, Ron G; Hirschhorn, Joel N
The journal of clinical endocrinology and metabolism,
09/2014, Letnik:
99, Številka:
9
Journal Article
Recenzirano
Odprti dostop
Context:
Genetics plays a major role in determining an individual's height. Although there are many monogenic disorders that lead to perturbations in growth and result in short stature, there is ...still no consensus as to the role that genetic diagnostics should play in the evaluation of a child with short stature.
Evidence Acquisition:
A search of PubMed was performed, focusing on the genetic diagnosis of short stature as well as on specific diagnostic subgroups included in this article. Consensus guidelines were reviewed.
Evidence Synthesis:
There are a multitude of rare genetic causes of severe short stature. There is no high-quality evidence to define the optimal approach to the genetic evaluation of short stature. We review genetic etiologies of a number of diagnostic subgroups and propose an algorithm for genetic testing based on these subgroups.
Conclusion:
Advances in genomic technologies are revolutionizing the diagnostic approach to short stature. Endocrinologists must become facile with the use of genetic testing in order to identify the various monogenic disorders that present with short stature.
Whole-exome sequencing has enabled new approaches for discovering genes associated with monogenic disorders. One such approach is gene-based burden testing, in which the aggregate frequency of ...“qualifying variants” is compared between case and control subjects for each gene. Despite substantial successes of this approach, the genetic causes for many monogenic disorders remain unknown or only partially known. It is possible that particular genetic architectures lower rates of discovery, but the influence of these factors on power has not been rigorously evaluated. Here, we leverage large-scale exome-sequencing data to create an empirically based simulation framework to evaluate the impact of key parameters (background variation rates, locus heterogeneity, mode of inheritance, penetrance) on power in gene-based burden tests in the context of monogenic disorders. Our results demonstrate that across genes, there is a wide range in sample sizes needed to achieve power due to differences in the background rate of rare variants in each gene. Increasing locus heterogeneity results in rapid increases in sample sizes needed to achieve adequate power, particularly when individual genes contribute to less than 5% of cases under a dominant model. Interestingly, incomplete penetrance as low as 10% had little effect on power due to the low prevalence of monogenic disorders. Our results suggest that moderate incomplete penetrance is not an obstacle in this gene-based burden testing approach but that dominant disorders with high locus heterogeneity will require large sample sizes. Our simulations also provide guidance on sample size needs and inform study design under various genetic architectures.
The genetic causes of many Mendelian disorders remain undefined. Factors such as lack of large multiplex families, locus heterogeneity, and incomplete penetrance hamper these efforts for many ...disorders. Previous work suggests that gene-based burden testing—where the aggregate burden of rare, protein-altering variants in each gene is compared between case and control subjects—might overcome some of these limitations. The increasing availability of large-scale public sequencing databases such as Genome Aggregation Database (gnomAD) can enable burden testing using these databases as controls, obviating the need for additional control sequencing for each study. However, there exist various challenges with using public databases as controls, including lack of individual-level data, differences in ancestry, and differences in sequencing platforms and data processing. To illustrate the approach of using public data as controls, we analyzed whole-exome sequencing data from 393 individuals with idiopathic hypogonadotropic hypogonadism (IHH), a rare disorder with significant locus heterogeneity and incomplete penetrance against control subjects from gnomAD (n = 123,136). We leveraged presumably benign synonymous variants to calibrate our approach. Through iterative analyses, we systematically addressed and overcame various sources of artifact that can arise when using public control data. In particular, we introduce an approach for highly adaptable variant quality filtering that leads to well-calibrated results. Our approach “re-discovered” genes previously implicated in IHH (FGFR1, TACR3, GNRHR). Furthermore, we identified a significant burden in TYRO3, a gene implicated in hypogonadotropic hypogonadism in mice. Finally, we developed a user-friendly software package TRAPD (Test Rare vAriants with Public Data) for performing gene-based burden testing against public databases.
We present an approximate conditional and joint association analysis that can use summary-level statistics from a meta-analysis of genome-wide association studies (GWAS) and estimated linkage ...disequilibrium (LD) from a reference sample with individual-level genotype data. Using this method, we analyzed meta-analysis summary data from the GIANT Consortium for height and body mass index (BMI), with the LD structure estimated from genotype data in two independent cohorts. We identified 36 loci with multiple associated variants for height (38 leading and 49 additional SNPs, 87 in total) via a genome-wide SNP selection procedure. The 49 new SNPs explain approximately 1.3% of variance, nearly doubling the heritability explained at the 36 loci. We did not find any locus showing multiple associated SNPs for BMI. The method we present is computationally fast and is also applicable to case-control data, which we demonstrate in an example from meta-analysis of type 2 diabetes by the DIAGRAM Consortium.
Celotno besedilo
Dostopno za:
DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
The cellular NADH/NAD
ratio is fundamental to biochemistry, but the extent to which it reflects versus drives metabolic physiology in vivo is poorly understood. Here we report the in vivo application ...of Lactobacillus brevis (Lb)NOX
, a bacterial water-forming NADH oxidase, to assess the metabolic consequences of directly lowering the hepatic cytosolic NADH/NAD
ratio in mice. By combining this genetic tool with metabolomics, we identify circulating α-hydroxybutyrate levels as a robust marker of an elevated hepatic cytosolic NADH/NAD
ratio, also known as reductive stress. In humans, elevations in circulating α-hydroxybutyrate levels have previously been associated with impaired glucose tolerance
, insulin resistance
and mitochondrial disease
, and are associated with a common genetic variant in GCKR
, which has previously been associated with many seemingly disparate metabolic traits. Using LbNOX, we demonstrate that NADH reductive stress mediates the effects of GCKR variation on many metabolic traits, including circulating triglyceride levels, glucose tolerance and FGF21 levels. Our work identifies an elevated hepatic NADH/NAD
ratio as a latent metabolic parameter that is shaped by human genetic variation and contributes causally to key metabolic traits and diseases. Moreover, it underscores the utility of genetic tools such as LbNOX to empower studies of 'causal metabolism'.
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