Genome-wide linkage scan on estimated breeding values for a quantitative trait

Background: A genome-wide linkage scan was performed on Replicate 1 of the simulated data for fasting triglyceride levels. The aim of this study was to implement mixed-model methodology to estimate breeding values for each individual for this trait and to assess the merit of these breeding values in linkage analysis. These breeding values utilize all the pedigree information, and the genetic and phenotypic correlations with other measured traits across the two cohorts. A genome-wide linkage scan was run on both the new breeding value traits and the original traits.Results: Using breeding values, a maximum LOD of 7.78 was found on chromosome 5 at a position very close to a gene underlying the triglyceride levels. This effect was not detected using the original trait.Conclusion: The results imply that estimating breeding values may be a suitable method of deriving traits for use in genome-wide scans

Common genetic variation near MC4R is associated with waist circumference and insulin resistance

We carried out a genome-wide association study (318,237 SNPs) for insulin resistance and related phenotypes in 2,684 Indian Asians, with further testing in 11,955 individuals of Indian Asian or European ancestry. We found associations of rs12970134 near MC4R with waist circumference (P = 1.7 × 10−9) and, independently, with insulin resistance. Homozygotes for the risk allele of rs12970134 have ∼2 cm increased waist circumference. Common genetic variation near MC4R is associated with risk of adiposity and insulin resistance

Genome-wide analysis of health-related biomarkers in the UK Household Longitudinal Study reveals novel associations

Serum biomarker levels are associated with the risk of complex diseases. Here, we aimed to gain insights into the genetic architecture of biomarker traits which can reflect health status. We performed genome-wide association analyses for twenty serum biomarkers involved in organ function and reproductive health. 9,961 individuals from the UK Household Longitudinal Study were genotyped using the Illumina HumanCoreExome array and variants imputed to the 1000 Genomes Project and UK10K haplotypes. We establish a polygenic heritability for all biomarkers, confirm associations of fifty-four established loci, and identify five novel, replicating associations at genome-wide significance. A low-frequency variant, rs28929474, (beta = 0.04, P = 2 × 10-10) was associated with levels of alanine transaminase, an indicator of liver damage. The variant is located in the gene encoding serine protease inhibitor, low levels of which are associated with alpha-1 antitrypsin deficiency which leads to liver disease. We identified novel associations (rs78900934, beta = 0.05, P = 6 × 10-12; rs2911280, beta = 0.09, P = 6 × 10-10) for dihydroepiandrosterone sulphate, a precursor to major sex-hormones, and for glycated haemoglobin (rs12819124, beta = -0.03, P = 4 × 10-9; rs761772, beta = 0.05, P = 5 × 10-9). rs12819124 is nominally associated with risk of type 2 diabetes. Our study offers insights into the genetic architecture of well-known and less well-studied biomarkers.Please visit the publisher's website for further information

A Genome-Wide Association Study of the Metabolic Syndrome in Indian Asian Men

We conducted a two-stage genome-wide association study to identify common genetic variation altering risk of the metabolic syndrome and related phenotypes in Indian Asian men, who have a high prevalence of these conditions. In Stage 1, approximately 317,000 single nucleotide polymorphisms were genotyped in 2700 individuals, from which 1500 SNPs were selected to be genotyped in a further 2300 individuals. Selection for inclusion in Stage 1 was based on four metabolic syndrome component traits: HDL-cholesterol, plasma glucose and Type 2 diabetes, abdominal obesity measured by waist to hip ratio, and diastolic blood pressure. Association was tested with these four traits and a composite metabolic syndrome phenotype. Four SNPs reaching significance level p<5×10−7 and with posterior probability of association >0.8 were found in genes CETP and LPL, associated with HDL-cholesterol. These associations have already been reported in Indian Asians and in Europeans. Five additional loci harboured SNPs significant at p<10−6 and posterior probability >0.5 for HDL-cholesterol, type 2 diabetes or diastolic blood pressure. Our results suggest that the primary genetic determinants of metabolic syndrome are the same in Indian Asians as in other populations, despite the higher prevalence. Further, we found little evidence of a common genetic basis for metabolic syndrome traits in our sample of Indian Asian men

HAPRAP: a haplotype-based iterative method for statistical fine mapping using GWAS summary statistics

Motivation Fine mapping is a widely used approach for identifying the causal variant(s) at disease-associated loci. Standard methods (e.g. multiple regression) require individual level genotypes. Recent fine mapping methods using summary-level data require the pairwise correlation coefficients (r2 ) of the variants. However, haplotypes rather than pairwise r2 , are the true biological representation of linkage disequilibrium (LD) among multiple loci. In this article, we present an empirical iterative method, HAPlotype Regional Association analysis Program (HAPRAP), that enables fine mapping using summary statistics and haplotype information from an individual-level reference panel. Results Simulations with individual-level genotypes show that the results of HAPRAP and multiple regression are highly consistent. In simulation with summary-level data, we demonstrate that HAPRAP is less sensitive to poor LD estimates. In a parametric simulation using Genetic Investigation of ANthropometric Traits height data, HAPRAP performs well with a small training sample size (N < 2000) while other methods become suboptimal. Moreover, HAPRAP’s performance is not affected substantially by single nucleotide polymorphisms (SNPs) with low minor allele frequencies. We applied the method to existing quantitative trait and binary outcome meta-analyses (human height, QTc interval and gallbladder disease); all previous reported association signals were replicated and two additional variants were independently associated with human height. Due to the growing availability of summary level data, the value of HAPRAP is likely to increase markedly for future analyses (e.g. functional prediction and identification of instruments for Mendelian randomization)

Sixty-five common genetic variants and prediction of type 2 diabetes.

We developed a 65 type 2 diabetes (T2D) variant-weighted gene score to examine the impact on T2D risk assessment in a U.K.-based consortium of prospective studies, with subjects initially free from T2D (N = 13,294; 37.3% women; mean age 58.5 [38-99] years). We compared the performance of the gene score with the phenotypically derived Framingham Offspring Study T2D risk model and then the two in combination. Over the median 10 years of follow-up, 804 participants developed T2D. The odds ratio for T2D (top vs. bottom quintiles of gene score) was 2.70 (95% CI 2.12-3.43). With a 10% false-positive rate, the genetic score alone detected 19.9% incident cases, the Framingham risk model 30.7%, and together 37.3%. The respective area under the receiver operator characteristic curves were 0.60 (95% CI 0.58-0.62), 0.75 (95% CI 0.73 to 0.77), and 0.76 (95% CI 0.75 to 0.78). The combined risk score net reclassification improvement (NRI) was 8.1% (5.0 to 11.2; P = 3.31 × 10(-7)). While BMI stratification into tertiles influenced the NRI (BMI ≤24.5 kg/m(2), 27.6% [95% CI 17.7-37.5], P = 4.82 × 10(-8); 24.5-27.5 kg/m(2), 11.6% [95% CI 5.8-17.4], P = 9.88 × 10(-5); >27.5 kg/m(2), 2.6% [95% CI -1.4 to 6.6], P = 0.20), age categories did not. The addition of the gene score to a phenotypic risk model leads to a potentially clinically important improvement in discrimination of incident T2D

A gene-centric association scan for Coagulation Factor VII levels in European and African Americans: the Candidate Gene Association Resource (CARe) Consortium

Polymorphisms in several distinct genomic regions, including the F7 gene, were recently associated with factor VII (FVII) levels in European Americans (EAs). The genetic determinants of FVII in African Americans (AAs) are unknown. We used a 50 000 single nucleotide polymorphism (SNP) gene-centric array having dense coverage of over 2 000 candidate genes for cardiovascular disease (CVD) pathways in a community-based sample of 16 324 EA and 3898 AA participants from the Candidate Gene Association Resource (CARe) consortium. Our aim was the discovery of new genomic loci and more detailed characterization of existing loci associated with FVII levels. In EAs, we identified three new loci associated with FVII, of which APOA5 on chromosome 11q23 and HNF4A on chromosome 20q12–13 were replicated in a sample of 4289 participants from the Whitehall II study. We confirmed four previously reported FVII-associated loci (GCKR, MS4A6A, F7 and PROCR) in CARe EA samples. In AAs, the F7 and PROCR regions were significantly associated with FVII. Several of the FVII-associated regions are known to be associated with lipids and other cardiovascular-related traits. At the F7 locus, there was evidence of at least five independently associated SNPs in EAs and three independent signals in AAs. Though the variance in FVII explained by the existing loci is substantial (20% in EA and 10% in AA), larger sample sizes and investigation of lower frequency variants may be required to identify additional FVII-associated loci in EAs and AAs and further clarify the relationship between FVII and other CVD risk factors

Mendelian Randomisation study of the influence of eGFR on coronary heart disease.

Impaired kidney function, as measured by reduced estimated glomerular filtration rate (eGFR), has been associated with increased risk of coronary heart disease (CHD) in observational studies, but it is unclear whether this association is causal or the result of confounding or reverse causation. In this study we applied Mendelian randomisation analysis using 17 genetic variants previously associated with eGFR to investigate the causal role of kidney function on CHD. We used 13,145 participants from the UCL-LSHTM-Edinburgh-Bristol (UCLEB) Consortium and 194,427 participants from the Coronary ARtery DIsease Genome-wide Replication and Meta-analysis plus Coronary Artery Disease (CARDIoGRAMplusC4D) consortium. We observed significant association of an unweighted gene score with CHD risk (odds ratio = 0.983 per additional eGFR-increasing allele, 95% CI = 0.970-0.996, p = 0.008). However, using weights calculated from UCLEB, the gene score was not associated with disease risk (p = 0.11). These conflicting results could be explained by a single SNP, rs653178, which was not associated with eGFR in the UCLEB sample, but has known pleiotropic effects that prevent us from drawing a causal conclusion. The observational association between low eGFR and increased CHD risk was not explained by potential confounders, and there was no evidence of reverse causation, therefore leaving the remaining unexplained association as an open question

Mendelian randomization of blood lipids for coronary heart disease

AimsTo investigate the causal role of high-density lipoprotein cholesterol (HDL-C) and triglycerides in coronary heart disease (CHD) using multiple instrumental variables for Mendelian randomization.Methods and resultsWe developed weighted allele scores based on single nucleotide polymorphisms (SNPs) with established associations with HDL-C, triglycerides, and low-density lipoprotein cholesterol (LDL-C). For each trait, we constructed two scores. The first was unrestricted, including all independent SNPs associated with the lipid trait identified from a prior meta-analysis (threshold P < 2 × 10−6); and the second a restricted score, filtered to remove any SNPs also associated with either of the other two lipid traits at P ≤ 0.01. Mendelian randomization meta-analyses were conducted in 17 studies including 62,199 participants and 12,099 CHD events. Both the unrestricted and restricted allele scores for LDL-C (42 and 19 SNPs, respectively) associated with CHD. For HDL-C, the unrestricted allele score (48 SNPs) was associated with CHD (OR: 0.53; 95% CI: 0.40, 0.70), per 1 mmol/L higher HDL-C, but neither the restricted allele score (19 SNPs; OR: 0.91; 95% CI: 0.42, 1.98) nor the unrestricted HDL-C allele score adjusted for triglycerides, LDL-C, or statin use (OR: 0.81; 95% CI: 0.44, 1.46) showed a robust association. For triglycerides, the unrestricted allele score (67 SNPs) and the restricted allele score (27 SNPs) were both associated with CHD (OR: 1.62; 95% CI: 1.24, 2.11 and 1.61; 95% CI: 1.00, 2.59, respectively) per 1-log unit increment. However, the unrestricted triglyceride score adjusted for HDL-C, LDL-C, and statin use gave an OR for CHD of 1.01 (95% CI: 0.59, 1.75).ConclusionThe genetic findings support a causal effect of triglycerides on CHD risk, but a causal role for HDL-C, though possible, remains less certain