8 research outputs found
The application and development of methods to combine and infer information from genetic epidemiological studies of cardiovascular and other complex traits
This thesis investigates methods to combine and infer information from genetic
epidemiological studies. Three issues are explored, each in a distinct and self-contained
chapter.
Chapter 1 investigates how best to incorporate treatment information in genetic
analyses of blood pressure. Different approaches to adjusting for treatment are
compared in a number of simulated scenarios, and the approaches that utilise
all the observed data are generally shown to perform best. One particular
condition, however, causes these approaches to suffer bias. This is where a
genetic variant (or some other factor) interacts with treatment. This chapter
therefore urges caution in the interpretation of results from these studies, and
suggests some possible approaches to identifying existing interactions with
treatment.
Chapter 2 concerns participant privacy in genome-wide association studies
(GWAS). Recent methods claim to be able to infer whether an individual
participated in a study, using only aggregate statistics from the study such as
allele frequencies. In the past, these statistics have been freely published
online. This chapter explores the full implications of these methods, by
investigating their true capabilities and limitations. In addition, some
modifications are proposed to one particular method, to demonstrate how it can
be adapted for use in practice. This work finds that participant identification is
possible in ideal conditions, but common characteristics of real studies may
prevent any reliable application of these methods in practice.
Chapter 3 proposes a new approach to synthesising data between studies.
This approach â named âDataSHIELDâ â guarantees identical results to an
individual-level meta-analysis, while offering greater flexibility than the studylevel
meta-analysis. DataSHIELD also potentially circumvents some of the laws
that restrict data use, because it does not involve sharing any individual-level
data between studies. This chapter outlines the principles underpinning
DataSHIELD, and demonstrates its use in a simulated data example
Large-Scale Analysis of Determinants, Stability, and Heritability of High-Density Lipoprotein Cholesterol Efflux Capacity.
OBJECTIVE: Cholesterol efflux capacity (CEC) has emerged as a biomarker of coronary artery disease risk beyond plasma high-density lipoprotein (HDL) cholesterol (HDL-C) level. However, the determinants of CEC are incompletely characterized. We undertook a large-scale family-based population study to identify clinical, biochemical, and HDL particle parameter determinants of CEC, characterize reasons for the discordancy with HDL-C, quantify its heritability, and assess its stability over 10 to 12 years. APPROACHES AND RESULTS: CEC was quantified in 1988 individuals from the GRAPHIC (Genetic Regulation of Arterial Pressure of Humans in the Community) cohort, comprising individuals from 2 generations from 520 white nuclear families. Serum lipid and lipoprotein levels were determined by ultracentrifugation or nuclear magnetic resonance and HDL particle size and number quantified by nuclear magnetic resonance. Ninety unrelated individuals had repeat CEC measurements in samples collected after 10 to 12 years. CEC was positively correlated with HDL-C (R=0.62; P<0.0001). Among clinical and biochemical parameters, age, systolic blood pressure, alcohol consumption, serum albumin, triglycerides, phospholipids, and lipoprotein(a) were independently associated with CEC. Among HDL particle parameters, HDL particle number, particle size, and apolipoprotein A-II level were independently associated with CEC. Serum triglyceride level partially explained discordancy between CEC and HDL-C. CEC measurements in samples collected 10 to 12 years apart were strongly correlated (r=0.73; P<0.0001). Heritability of CEC was 0.31 (P=3.89Ă10(-14)) without adjustment for HDL-C and 0.13 (P=1.44Ă10(-3)) with adjustment. CONCLUSIONS: CEC is a stable trait over time, is influenced by specific clinical, serum, and HDL particle parameters factors beyond HDL-C, can be maintained in persons with a low plasma HDL-C by elevated serum triglyceride level, and is modestly independently heritable
Publisher Correction: Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity (Nature Genetics, (2018), 50, 1, (26-41), 10.1038/s41588-017-0011-x)
An amendment to this paper has been published and can be accessed via a link at the top of the paper.</p
Publisher Correction:Discovery of rare variants associated with blood pressure regulation through meta-analysis of 1.3 million individuals (Nature Genetics, (2020), 52, 12, (1314-1332), 10.1038/s41588-020-00713-x)
Genetic studies of blood pressure (BP) to date have mainly analyzed common variants (minor allele frequencyâ>â0.05). In a meta-analysis of up to ~1.3 million participants, we discovered 106 new BP-associated genomic regions and 87 rare (minor allele frequencyââ€â0.01) variant BP associations (Pâ<â5âĂâ10â8), of which 32 were in new BP-associated loci and 55 were independent BP-associated single-nucleotide variants within known BP-associated regions. Average effects of rare variants (44% coding) were ~8 times larger than common variant effects and indicate potential candidate causal genes at new and known loci (for example, GATA5 and PLCB3). BP-associated variants (including rare and common) were enriched in regions of active chromatin in fetal tissues, potentially linking fetal development with BP regulation in later life. Multivariable Mendelian randomization suggested possible inverse effects of elevated systolic and diastolic BP on large artery stroke. Our study demonstrates the utility of rare-variant analyses for identifying candidate genes and the results highlight potential therapeutic targets
Publisher Correction: Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity
In the HTML version of this article initially published, the author groups âCHD Exome+ Consortiumâ, âEPIC-CVD Consortiumâ, âExomeBP Consortiumâ, âGlobal Lipids Genetic Consortiumâ, âGoT2D Genes Consortiumâ, âEPIC InterAct Consortiumâ, âINTERVAL Studyâ, âReproGen Consortiumâ, âT2D-Genes Consortiumâ, âThe MAGIC Investigatorsâ and âUnderstanding Society Scientific Groupâ appeared at the end of the author list but should have appeared earlier in the list, after author Krina T. Zondervan. The errors have been corrected in the HTML version of the article
Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity
Genome-wide association studies (GWAS) have identified >250 loci for body mass index (BMI), implicating pathways related to neuronal biology. Most GWAS loci represent clusters of common, noncoding variants from which pinpointing causal genes remains challenging. Here we combined data from 718,734 individuals to discover rare and low-frequency (minor allele frequency (MAF) < 5%) coding variants associated with BMI. We identified 14 coding variants in 13 genes, of which 8 variants were in genes (ZBTB7B, ACHE, RAPGEF3, RAB21, ZFHX3, ENTPD6, ZFR2 and ZNF169) newly implicated in human obesity, 2 variants were in genes (MC4R and KSR2) previously observed to be mutated in extreme obesity and 2 variants were in GIPR. The effect sizes of rare variants are ~10 times larger than those of common variants, with the largest effect observed in carriers of an MC4R mutation introducing a stop codon (p.Tyr35Ter, MAF = 0.01%), who weighed ~7 kg more than non-carriers. Pathway analyses based on the variants associated with BMI confirm enrichment of neuronal genes and provide new evidence for adipocyte and energy expenditure biology, widening the potential of genetically supported therapeutic targets in obesity
Rare and low-frequency coding variants alter human adult height
Height is a highly heritable, classic polygenic trait with approximately 700 common associated variants identified through genome-wide association studies so far. Here, we report 83 height-associated coding variants with lower minor-allele frequencies (in the range of 0.1-4.8%) and effects of up to 2 centimetres per allele (such as those in IHH, STC2, AR and CRISPLD2), greater than ten times the average effect of common variants. In functional follow-up studies, rare height-increasing alleles of STC2 (giving an increase of 1-2 centimetres per allele) compromised proteolytic inhibition of PAPP-A and increased cleavage of