Genome-wide analysis of mitochondrial DNA copy number reveals loci implicated in nucleotide metabolism, platelet activation, and megakaryocyte proliferation.

Mitochondrial DNA copy number (mtDNA-CN) measured from blood specimens is a minimally invasive marker of mitochondrial function that exhibits both inter-individual and intercellular variation. To identify genes involved in regulating mitochondrial function, we performed a genome-wide association study (GWAS) in 465,809 White individuals from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank (UKB). We identified 133 SNPs with statistically significant, independent effects associated with mtDNA-CN across 100 loci. A combination of fine-mapping, variant annotation, and co-localization analyses was used to prioritize genes within each of the 133 independent sites. Putative causal genes were enriched for known mitochondrial DNA depletion syndromes (p = 3.09 × 10-15) and the gene ontology (GO) terms for mtDNA metabolism (p = 1.43 × 10-8) and mtDNA replication (p = 1.2 × 10-7). A clustering approach leveraged pleiotropy between mtDNA-CN associated SNPs and 41 mtDNA-CN associated phenotypes to identify functional domains, revealing three distinct groups, including platelet activation, megakaryocyte proliferation, and mtDNA metabolism. Finally, using mitochondrial SNPs, we establish causal relationships between mitochondrial function and a variety of blood cell-related traits, kidney function, liver function and overall (p = 0.044) and non-cancer mortality (p = 6.56 × 10-4).

Partial normalization of components of metabolic syndrome does not influence prevalent echocardiographic abnormalities: the HyperGEN study.

BACKGROUND AND AIMS: Metabolic syndrome (MetS) is a complex condition characterized by different phenotypes, according to the combinations of risk factors and is associated with cardiovascular abnormalities. Whether control of MetS components by treatment produces improvement in the associated cardiovascular abnormalities is unknown. We investigated whether partial control of components of MetS was associated with less echocardiographic abnormalities than the complete presentation of MetS based on measured components. METHODS AND RESULTS: We evaluated markers of echocardiographic preclinical cardiovascular disease in MetS (ATP III) defined by measured components or by history of treatment, in 1421 African-American and 1195 Caucasian non-diabetic HyperGEN participants, without prevalent cardiovascular disease or serum creatinine >2 mg/dL. Of 2616 subjects, 512 subjects had MetS by measured components and 328 by history. Hypertension was found in 16% of participants without MetS, 6% of those with MetS by history and 42% of those with MetS by measured components. Obesity and central fat distribution had similar prevalence in both MetS groups (both p < 0.0001 vs. No-MetS). Blood pressure was similar in MetS by history and No-MetS, and lower than in MetS by measured components (p < 0.0001). LV mass and midwall shortening, left atrial (LA) dimension and LA systolic force were similarly abnormal in both MetS groups (all p < 0.0001 vs. No-MetS) without difference between them. CONCLUSIONS: There is a little impact of control by treatment of single components of MetS (namely hypertension) on echocardiographic abnormalities. Lower blood pressure in participants with MetS by history was not associated with substantially reduced alterations in cardiac geometry and function.

QTL-specific genotype-by-smoking interaction and burden of calcified coronary atherosclerosis: the NHLBI Family Heart Study.

BACKGROUND: Calcified coronary plaque (CCP) is a complex trait influenced by both genes and environment, and plausibly an interaction between the two. Because the familial aggregation of CCP has been demonstrated and smoking is a significant, independent predictor of CCP, we assessed the evidence for genotype-by-smoking interaction and conducted linkage analysis of quantitative Agatston CCP scores in participants of the NHLBI Family Heart Study (FHS). METHODS: During standardized clinical exams smoking habits were ascertained and CCP was quantified with cardiac computed tomography (CT). Among 4387 relationship pairs from 2128 Caucasian examinees variance component analysis was implemented in SOLAR to examine: (1) additive genotype-by-smoking status interaction using a variance component approach; (2) linkage analysis in the full sample and among smoking subsets defined by individual smoking exposure; (3) QTL-specific genotype-by-smoking interaction in the regions that appeared to differentiate between smoking strata. RESULTS: The prevalence of CCP (and median Agatston score) was 75% (184.6) in men and 48% (51.0) in women. We detected four genome-wide significant logarithm of odds (LOD) scores in samples stratified by individual smoking exposure: chromosome 4 at 122cM (nearest marker D4S2297; robust adjusted LOD=3.1; q=0.053), chromosome 6 at 99cM (nearest marker D6S1056; robust adjusted LOD=3.3; q=0.053), chromosome 11 at 19cM (nearest marker D11S199; robust adjusted LOD=4.0; q=0.02) and chromosome 13 at 77cM (nearest marker D13S892; robust adjusted LOD=3.1; q=0.053). Additive and QTL-specific genotype-by-smoking interaction was detected on chromosomes 4, 6, 11 and 13; all P<0.05. Three of the four QTLs identified in this report have been previously linked to atherosclerosis and harbor interesting candidate genes. CONCLUSIONS: These findings demonstrate the importance of considering complex interactions in the search for genes that influence the pathogenesis of CCP.

Genotype-by-sex interaction in the aetiology of type 2 diabetes mellitus: support for sex-specific quantitative trait loci in Hypertension Genetic Epidemiology Network participants.

AIMS/HYPOTHESIS: While there are sex-related differences in both the prevalence of type 2 diabetes mellitus and disease risk factors, there is only limited research on sex-specific influences on type 2 diabetes aetiology within the same study population. Thus, we assessed genotype-by-sex interaction using a liability threshold model in an attempt to localise sex-specific type 2 diabetes quantitative trait loci (QTLs). SUBJECTS, MATERIALS AND METHODS: Hypertensive siblings and their offspring and/or parents in the Hypertension Genetic Epidemiology Network of the Family Blood Pressure Program were recruited from five field centres. The diabetic phenotype was adjusted for race, study centre, age and non-linear age effects. In total, 567 diabetic individuals were identified in 385 families. Variance component linkage analyses in the combined sample and stratified by sex and race were performed (SOLAR program) using race-specific marker allele frequencies derived from a random sample of participants at each centre. RESULTS: We observed a QTL-specific genotype-by-sex interaction (p=0.009) on chromosome 17 at 31 cM, with females displaying a robust adjusted logarithm of odds (LOD) of 3.0 compared with 0.2 in males and 1.3 in the combined sample. Three additional regions demonstrating suggestive evidence for linkage were detected: chromosomes 2 and 5 in the female sample and chromosome 22 (adjusted LOD=1.9) in the combined sample. CONCLUSIONS/INTERPRETATION: These findings suggest that multiple genes may regulate susceptibility to type 2 diabetes, demonstrating the importance of considering the interaction of genes and environment in the aetiology of common complex traits.

Multivariate and multilocus variance components method, based on structural relationships to assess quantitative trait linkage via SEGPATH.

A general-purpose modeling framework for performing path and segregation analysis jointly, called SEGPATH (Province and Rao [1995] Stat. Med. 7:185-198), has been extended to cover "model-free" robust, variance-components linkage analysis, based on identity-by-descent (IBD) sharing. These extended models can be used to analyze linkage to a single marker or to perform multipoint linkage analysis, with a single phenotype or multivariate vector of phenotypes, in pedigrees. Within a single, consistent approach, SEGPATH models can perform segregation analysis, path analysis, linkage analysis, or combinations thereof. SEGPATH models can incorporate environmental or other measured covariate fixed effects (including measured genotypes), genotype-specific covariate effects, population heterogeneity models, repeated-measures models, longitudinal models, autoregressive models, developmental models, gene-by-environment interaction models, etc., with or without linkage components. The data analyzed can have any missing value structure (assumed missing at random), with entire individuals missing, or missing on one or more measurements. Corrections for ascertainment can be made on a vector of phenotypes and/or other measures. Because of the flexibility of the class of models, the SEGPATH approach can also be used in nongenetic applications where there is a hierarchical structure, such as longitudinal, repeated-measures, time series, or nested models. A variety of specific models are provided, as well as some comparisons with other linkage analysis models. Particular applications demonstrate the importance of correctly accounting for the extraneous sources of familial resemblance, as can be done easily with these SEGPATH models, so as to give added power to detect linkage as well as to protect against spuriously inferring linkage.