With the advent of very large scale genome‐wide association studies (GWASs), the promise of Mendelian randomization (MR) has begun to be fulfilled. However, whilst GWASs have provided essential ...information on the single nucleotide polymorphisms (SNPs) associated with modifiable risk factors needed for MR, the availability of large numbers of SNP instruments raises issues of how best to use this information and how to deal with potential problems such as pleiotropy. Here we provide commentary on some of the recent advances in the MR analysis, including an overview of the different genetic architectures that are being uncovered for a variety of modifiable risk factors and how users ought to take that into consideration when designing MR studies.
The vast majority of genome-wide association studies (GWASs) are performed in Europeans, and their transferability to other populations is dependent on many factors (e.g., linkage disequilibrium, ...allele frequencies, genetic architecture). As medical genomics studies become increasingly large and diverse, gaining insights into population history and consequently the transferability of disease risk measurement is critical. Here, we disentangle recent population history in the widely used 1000 Genomes Project reference panel, with an emphasis on populations underrepresented in medical studies. To examine the transferability of single-ancestry GWASs, we used published summary statistics to calculate polygenic risk scores for eight well-studied phenotypes. We identify directional inconsistencies in all scores; for example, height is predicted to decrease with genetic distance from Europeans, despite robust anthropological evidence that West Africans are as tall as Europeans on average. To gain deeper quantitative insights into GWAS transferability, we developed a complex trait coalescent-based simulation framework considering effects of polygenicity, causal allele frequency divergence, and heritability. As expected, correlations between true and inferred risk are typically highest in the population from which summary statistics were derived. We demonstrate that scores inferred from European GWASs are biased by genetic drift in other populations even when choosing the same causal variants and that biases in any direction are possible and unpredictable. This work cautions that summarizing findings from large-scale GWASs may have limited portability to other populations using standard approaches and highlights the need for generalized risk prediction methods and the inclusion of more diverse individuals in medical genomics.
The familial aggregation indicated the inheritance of cancer risk. Recent genome‐wide association studies (GWASs) have identified a number of common single‐nucleotide polymorphisms (SNPs). Following ...heritability analyses have shown that SNPs could explain a moderate amount of variance for different cancer phenotypes among Caucasians. However, little information was available in Chinese population. We performed a genome‐wide complex trait analysis for common cancers at nine anatomical sites in Chinese population (14,629 cancer cases vs. 17,554 controls) and estimated the heritability of these cancers based on the common SNPs. We found that common SNPs explained certain amount of heritability with significance for all nine cancer sites: gastric cancer (20.26%), esophageal squamous cell carcinoma (19.86%), colorectal cancer (16.30%), lung cancer (LC) (15.17%), and epithelial ovarian cancer (13.31%), and a similar heritability around 10% for hepatitis B virus‐related hepatocellular carcinoma, prostate cancer, breast cancer and nasopharyngeal carcinoma. We found that nearly or less than 25% change was shown when removing the regions expanding 250 kb or 500 kb upward and downward of the GWAS‐reported SNPs. We also found strong linear correlations between variance partitioned by each chromosome and chromosomal length only for LC (R2 = 0.641, p = 0.001) and esophageal squamous cell cancer (R2 = 0.633, p = 0.002), which implied us the complex heterogeneity of cancers. These results indicate polygenic genetic architecture of the nine common cancers in Chinese population. Further efforts should be made to discover the hidden heritability of different cancer types among Chinese.
What's new?
Almost every cancer exhibits familial aggregation. Here, the authors conducted a genome‐wide complex trait analysis in Chinese participants in previous genome‐wide association studies to estimate heritability explained by single‐nucleotide polymorphisms for nine common cancers (gastric, esophageal, colorectal, lung, ovarian, hepatocellular, prostrate, breast, and nasopharyngeal). The explained heritability ranged from 10.19% to 20.26% indicating a polygenic architecture of all examined cancer types. The authors recommend performing even larger studies to better analyze the hidden heritability of each cancer type.
Genetics of Hypertriglyceridemia Dron, Jacqueline S.; Hegele, Robert A.
Frontiers in endocrinology,
07/2020, Volume:
11
Journal Article
Peer reviewed
Open access
Hypertriglyceridemia, a commonly encountered phenotype in cardiovascular and metabolic clinics, is surprisingly complex. A range of genetic variants, from single-nucleotide variants to large-scale ...copy number variants, can lead to either the severe or mild-to-moderate forms of the disease. At the genetic level, severely elevated triglyceride levels resulting from familial chylomicronemia syndrome (FCS) are caused by homozygous or biallelic loss-of-function variants in
LPL, APOC2, APOA5, LMF1
, and
GPIHBP1
genes. In contrast, susceptibility to multifactorial chylomicronemia (MCM), which has an estimated prevalence of ~1 in 600 and is at least 50–100-times more common than FCS, results from two different types of genetic variants: (1) rare heterozygous variants (minor allele frequency <1%) with variable penetrance in the five causal genes for FCS; and (2) common variants (minor allele frequency >5%) whose individually small phenotypic effects are quantified using a polygenic score. There is indirect evidence of similar complex genetic predisposition in other clinical phenotypes that have a component of hypertriglyceridemia, such as combined hyperlipidemia and dysbetalipoproteinemia. Future considerations include: (1) evaluation of whether the specific type of genetic predisposition to hypertriglyceridemia affects medical decisions or long-term outcomes; and (2) searching for other genetic contributors, including the role of genome-wide polygenic scores, novel genes, non-linear gene-gene or gene-environment interactions, and non-genomic mechanisms including epigenetics and mitochondrial DNA.
C4 photosynthesis is a complex trait that boosts productivity in tropical conditions. Compared with C3 species, the C4 state seems to require numerous novelties, but species comparisons can be ...confounded by long divergence times. Here, we exploit the photosynthetic diversity that exists within a single species, the grass Alloteropsis semialata, to detect changes in gene expression associated with different photosynthetic phenotypes. Phylogenetically informed comparative transcriptomics show that intermediates with a weak C4 cycle are separated from the C3 phenotype by increases in the expression of 58 genes (0.22% of genes expressed in the leaves), including those encoding just three core C4 enzymes: aspartate aminotransferase, phosphoenolpyruvate carboxykinase, and phosphoenolpyruvate carboxylase. The subsequent transition to full C4 physiology was accompanied by increases in another 15 genes (0.06%), including only the core C4 enzyme pyruvate orthophosphate dikinase. These changes probably created a rudimentary C4 physiology, and isolated populations subsequently improved this emerging C4 physiology, resulting in a patchwork of expression for some C4 accessory genes. Our work shows how C4 assembly in A. semialata happened in incremental steps, each requiring few alterations over the previous step. These create short bridges across adaptive landscapes that probably facilitated the recurrent origins of C4 photosynthesis through a gradual process of evolution.
Long non-coding RNA (lncRNA) genes have well-established and important impacts on molecular and cellular functions. However, among the thousands of lncRNA genes, it is still a major challenge to ...identify the subset with disease or trait relevance. To systematically characterize these lncRNA genes, we used Genotype Tissue Expression (GTEx) project v8 genetic and multi-tissue transcriptomic data to profile the expression, genetic regulation, cellular contexts, and trait associations of 14,100 lncRNA genes across 49 tissues for 101 distinct complex genetic traits. Using these approaches, we identified 1,432 lncRNA gene-trait associations, 800 of which were not explained by stronger effects of neighboring protein-coding genes. This included associations between lncRNA quantitative trait loci and inflammatory bowel disease, type 1 and type 2 diabetes, and coronary artery disease, as well as rare variant associations to body mass index.
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•29% of lncRNA genes with eQTLs show tissue-specific genetic regulation•Co-expression networks and single-cell data provide annotations for 94% of lncRNAs•Rare variants near lncRNA expression outliers impact complex traits, like BMI•We identify 800 lncRNA-trait relationships not explained by protein-coding genes
A systematic analysis of NIH Genotype Tissue Expression (GTEx) project data provides insights into lncRNA expression patterns and functions, explores the impact of genetic variation on lncRNAs, and connects lncRNAs to complex traits and human disease.
Although genome-wide association studies (GWASs) have identified thousands of risk loci for many complex traits and diseases, the causal variants and genes at these loci remain largely unknown. Here, ...we introduce a method for estimating the local genetic correlation between gene expression and a complex trait and utilize it to estimate the genetic correlation due to predicted expression between pairs of traits. We integrated gene expression measurements from 45 expression panels with summary GWAS data to perform 30 multi-tissue transcriptome-wide association studies (TWASs). We identified 1,196 genes whose expression is associated with these traits; of these, 168 reside more than 0.5 Mb away from any previously reported GWAS significant variant. We then used our approach to find 43 pairs of traits with significant genetic correlation at the level of predicted expression; of these, eight were not found through genetic correlation at the SNP level. Finally, we used bi-directional regression to find evidence that BMI causally influences triglyceride levels and that triglyceride levels causally influence low-density lipoprotein. Together, our results provide insight into the role of gene expression in the susceptibility of complex traits and diseases.
Background and Aims
Twin and family studies suggest that genetic influences are shared across substances of abuse. However, despite evidence of heritability, genome‐wide association and candidate ...gene studies have indicated numerous markers of limited effects, suggesting that much of the heritability remains missing. We estimated (1) the aggregate effect of common single nucleotide polymorphisms (SNPs) on multiple indicators of comorbid drug problems that are typically employed across community and population‐based samples, and (2) the genetic covariance across these measures.
Participants
A total of 2596 unrelated subjects from the Study of Addiction: Genetics and Environment provided information on alcohol, tobacco, cocaine, cannabis and other illicit substance dependence. Phenotypic measures included: (1) a factor score based on DSM‐IV drug dependence diagnoses (DD), (2) a factor score based on problem use (PU; i.e. 1+ DSM‐IV symptoms) and (3) dependence vulnerability (DV; a ratio of DSM‐IV symptoms to the number of substances used).
Findings
Univariate and bivariate genome‐wide complex trait analyses of this selected sample indicated that common SNPs explained 25–36% of the variance across measures, with DD and DV having the largest effects h2SNP (standard error) = 0.36 (0.13) and 0.33 (0.13), respectively; PU = 0.25 (0.13). Genetic effects were shared across the three phenotypic measures of comorbid drug problems rDD‐PU = 0.92 (0.08), rDD‐DV = 0.97 (0.08) and rPU‐DV = 0.96 (0.07).
Conclusion
At least 20% of the variance in the generalized vulnerability to substance dependence is attributable to common single nucleotide polymorphisms. The additive effect of common single nucleotide polymorphisms is shared across important indicators of comorbid drug problems.
Causal genes and variants within genome-wide association study (GWAS) loci can be identified by integrating GWAS statistics with expression quantitative trait loci (eQTL) and determining which ...variants underlie both GWAS and eQTL signals. Most analyses, however, consider only the marginal eQTL signal, rather than dissect this signal into multiple conditionally independent signals for each gene. Here we show that analyzing conditional eQTL signatures, which could be important under specific cellular or temporal contexts, leads to improved fine mapping of GWAS associations. Using genotypes and gene expression levels from post-mortem human brain samples (n = 467) reported by the CommonMind Consortium (CMC), we find that conditional eQTL are widespread; 63% of genes with primary eQTL also have conditional eQTL. In addition, genomic features associated with conditional eQTL are consistent with context-specific (e.g., tissue-, cell type-, or developmental time point-specific) regulation of gene expression. Integrating the 2014 Psychiatric Genomics Consortium schizophrenia (SCZ) GWAS and CMC primary and conditional eQTL data reveals 40 loci with strong evidence for co-localization (posterior probability > 0.8), including six loci with co-localization of conditional eQTL. Our co-localization analyses support previously reported genes, identify novel genes associated with schizophrenia risk, and provide specific hypotheses for their functional follow-up.
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