Constrained multivariate association with longitudinal phenotypes

The incorporation of longitudinal data into genetic epidemiological studies has the potential to provide valuable information regarding the effect of time on complex disease etiology. Yet, the majority of research focuses on variables collected from a single time point. This aim of this study was to test for main effects on a quantitative trait across time points using a constrained maximum-likelihood measured genotype approach. This method simultaneously accounts for all repeat measurements of a phenotype in families. We applied this method to systolic blood pressure (SBP) measurements from three time points using the Genetic Analysis Workshop 19 (GAW19) whole-genome sequence family simulated data set and 200 simulated replicates. Data consisted of 849 individuals from 20 extended Mexican American pedigrees. Comparisons were made among 3 statistical approaches: (a) constrained, where the effect of a variant or gene region on the mean trait value was constrained to be equal across all measurements; (b) unconstrained, where the variant or gene region effect was estimated separately for each time point; and (c) the average SBP measurement from three time points. These approaches were run for nine genetic variants with known effect sizes (\u3e0.001) for SBP variability and a known gene-centric kernel (MAP4)-based test under the GAW19 simulation model across 200 replicates

Large scale mitochondrial sequencing in Mexican Americans suggests a reappraisal of Native American origins

<p>Abstract</p> <p>Background</p> <p>The Asian origin of Native Americans is largely accepted. However uncertainties persist regarding the source population(s) within Asia, the divergence and arrival time(s) of the founder groups, the number of expansion events, and migration routes into the New World. mtDNA data, presented over the past two decades, have been used to suggest a single-migration model for which the Beringian land mass plays an important role.</p> <p>Results</p> <p>In our analysis of 568 mitochondrial genomes, the coalescent age estimates of shared roots between Native American and Siberian-Asian lineages, calculated using two different mutation rates, are A4 (27.5 ± 6.8 kya/22.7 ± 7.4 kya), C1 (21.4 ± 2.7 kya/16.4 ± 1.5 kya), C4 (21.0 ± 4.6 kya/20.0 ± 6.4 kya), and D4e1 (24.1 ± 9.0 kya/17.9 ± 10.0 kya). The coalescent age estimates of pan-American haplogroups calculated using the same two mutation rates (A2:19.5 ± 1.3 kya/16.1 ± 1.5 kya, B2:20.8 ± 2.0 kya/18.1 ± 2.4 kya, C1:21.4 ± 2.7 kya/16.4 ± 1.5 kya and D1:17.2 ± 2.0 kya/14.9 ± 2.2 kya) and estimates of population expansions within America (~21-16 kya), support the pre-Clovis occupation of the New World. The phylogeography of sublineages within American haplogroups A2, B2, D1 and the C1b, C1c andC1d subhaplogroups of C1 are complex and largely specific to geographical North, Central and South America. However some sub-branches (B2b, C1b, C1c, C1d and D1f) already existed in American founder haplogroups before expansion into the America.</p> <p>Conclusions</p> <p>Our results suggest that Native American founders diverged from their Siberian-Asian progenitors sometime during the last glacial maximum (LGM) and expanded into America soon after the LGM peak (~20-16 kya). The phylogeography of haplogroup C1 suggest that this American founder haplogroup differentiated in Siberia-Asia. The situation is less clear for haplogroup B2, however haplogroups A2 and D1 may have differentiated soon after the Native American founders divergence. A moderate population bottle neck in American founder populations just before the expansion most plausibly resulted in few founder types in America. The similar estimates of the diversity indices and Bayesian skyline analysis in North America, Central America and South America suggest almost simultaneous (~ 2.0 ky from South to North America) colonization of these geographical regions with rapid population expansion differentiating into more or less regional branches across the pan-American haplogroups.</p

Epigenetics, heritability and longitudinal analysis

Background Longitudinal data and repeated measurements in epigenome-wide association studies (EWAS) provide a rich resource for understanding epigenetics. We summarize 7 analytical approaches to the GAW20 data sets that addressed challenges and potential applications of phenotypic and epigenetic data. All contributions used the GAW20 real data set and employed either linear mixed effect (LME) models or marginal models through generalized estimating equations (GEE). These contributions were subdivided into 3 categories: (a) quality control (QC) methods for DNA methylation data; (b) heritability estimates pretreatment and posttreatment with fenofibrate; and (c) impact of drug response pretreatment and posttreatment with fenofibrate on DNA methylation and blood lipids. Results Two contributions addressed QC and identified large statistical differences with pretreatment and posttreatment DNA methylation, possibly a result of batch effects. Two contributions compared epigenome-wide heritability estimates pretreatment and posttreatment, with one employing a Bayesian LME and the other using a variance-component LME. Density curves comparing these studies indicated these heritability estimates were similar. Another contribution used a variance-component LME to depict the proportion of heritability resulting from a genetic and shared environment. By including environmental exposures as random effects, the authors found heritability estimates became more stable but not significantly different. Two contributions investigated treatment response. One estimated drug-associated methylation effects on triglyceride levels as the response, and identified 11 significant cytosine-phosphate-guanine (CpG) sites with or without adjusting for high-density lipoprotein. The second contribution performed weighted gene coexpression network analysis and identified 6 significant modules of at least 30 CpG sites, including 3 modules with topological differences pretreatment and posttreatment. Conclusions Four conclusions from this GAW20 working group are: (a) QC measures are an important consideration for EWAS studies that are investigating multiple time points or repeated measurements; (b) application of heritability estimates between time points for individual CpG sites is a useful QC measure for DNA methylation studies; (c) drug intervention demonstrated strong epigenome-wide DNA methylation patterns across the 2 time points; and (d) new statistical methods are required to account for the environmental contributions of DNA methylation across time. These contributions demonstrate numerous opportunities exist for the analysis of longitudinal data in future epigenetic studies

The antihypertensive MTHFR gene polymorphism rs17367504-G is a possible novel protective locus for preeclampsia

Objective: Preeclampsia is a complex heterogeneous disease commonly defined by new-onset hypertension and proteinuria in pregnancy. Women experiencing preeclampsia have increased risk for cardiovascular diseases (CVD) later in life. Preeclampsia and CVD share risk factors and pathophysiologic mechanisms, including dysregulated inflammation and raised blood pressure. Despite commonalities, little is known about the contribution of shared genes (pleiotropy) to these diseases. This study aimed to investigate whether genetic risk factors for hypertension or inflammation are pleiotropic by also being associated with preeclampsia. Methods: We genotyped 122 single nucleotide polymorphisms (SNPs) in women with preeclampsia (n = 1006) and nonpreeclamptic controls (n = 816) from the Norwegian HUNT Study. SNPs were chosen on the basis of previously reported associations with either nongestational hypertension or inflammation in genome-wide association studies. The SNPs were tested for association with preeclampsia in a multiple logistic regression model. Results: The minor (G) allele of the intronic SNP rs17367504 in the gene methylenetetrahydrofolate reductase (MTHFR) was associated with a protective effect on preeclampsia (odds ratio 0.65, 95% confidence interval 0.53–0.80) in the Norwegian cohort. This association did not replicate in an Australian preeclampsia case–control cohort (P = 0.68, odds ratio 1.05, 95% confidence interval 0.83–1.32, minor allele frequency = 0.15). Conclusion: MTHFR is important for regulating transmethylation processes and is involved in regulation of folate metabolism. The G allele of rs17367504 has previously been shown to protect against nongestational hypertension. Our study suggests a novel association between this allele and reduced risk for preeclampsia. This is the first study associating the minor (G) allele of a SNP within the MTHFR gene with a protective effect on preeclampsia, and in doing so identifying a possible pleiotropic protective effect on preeclampsia and hypertension

Whole genome sequencing of 91 multiplex schizophrenia families reveals increased burden of rare, exonic copy number variation in schizophrenia probands and genetic heterogeneity

The importance of genomic copy number variants (CNVs) has long been recognized in the etiology of neurodevelopmental diseases. We report here the results from the CNV analysis of whole-genome sequences from 91 multiplex schizophrenia families. Employing four algorithms (CNVnator, Cn.mops, DELLY and LUMPY) to identify CNVs, we find 1231 rare deletions and 287 rare duplications in 300 individuals (77 with schizophrenia (SZ), 32 with schizoaffective disorder (SAD), 82 with another neuropsychiatric diagnosis and 109 unaffected). The size of the CNVs ranges from a few hundred base-pairs to about 1.3Mb. The total burden of CNVs does not differ significantly between affected (SZ and SAD) and unaffected individuals. Parent-to-child transmission rate for rare CNVs affecting exonic regions is significantly higher for affected (SZ and SAD) probands as compared to their siblings, but rates for all CNVs is not. We observe heterogeneity between families in terms of genes involved in CNVs, and find several CNVs involving genes previously implicated in either schizophrenia or other neuropsychiatric disorders

Childhood sleep health and epigenetic age acceleration in late adolescence: Cross-sectional and longitudinal analyses

Aim: Investigate if childhood measures of sleep health are associated with epigenetic age acceleration in late adolescence. Methods: Parent-reported sleep trajectories from age 5 to 17, self-reported sleep problems at age 17, and six measures of epigenetic age acceleration at age 17 were studied in 1192 young Australians from the Raine Study Gen2. Results: There was no evidence for a relationship between the parent-reported sleep trajectories and epigenetic age acceleration (p ≥ 0.17). There was a positive cross-sectional relationship between self-reported sleep problem score and intrinsic epigenetic age acceleration at age 17 (b = 0.14, p = 0.04), which was attenuated after controlling for depressive symptom score at the same age (b = 0.08, p = 0.34). Follow-up analyses suggested this finding may represent greater overtiredness and intrinsic epigenetic age acceleration in adolescents with higher depressive symptoms. Conclusion: There was no evidence for a relationship between self- or parent-reported sleep health and epigenetic age acceleration in late adolescence after adjusting for depressive symptoms. Mental health should be considered as a potential confounding variable in future research on sleep and epigenetic age acceleration, particularly if subjective measures of sleep are used

Comprehensive genetic analysis of the human lipidome identifies loci associated with lipid homeostasis with links to coronary artery disease

We integrated lipidomics and genomics to unravel the genetic architecture of lipid metabolism and identify genetic variants associated with lipid species putatively in the mechanistic pathway for coronary artery disease (CAD). We quantified 596 lipid species in serum from 4,492 individuals from the Busselton Health Study. The discovery GWAS identified 3,361 independent lipid-loci associations, involving 667 genomic regions (479 previously unreported), with validation in two independent cohorts. A meta-analysis revealed an additional 70 independent genomic regions associated with lipid species. We identified 134 lipid endophenotypes for CAD associated with 186 genomic loci. Associations between independent lipid-loci with coronary atherosclerosis were assessed in ∼456,000 individuals from the UK Biobank. Of the 53 lipid-loci that showed evidence of association (P \u3c 1 × 10−3), 43 loci were associated with at least one lipid endophenotype. These findings illustrate the value of integrative biology to investigate the aetiology of atherosclerosis and CAD, with implications for other complex diseases

Genetic influence on scar height and pliability after burn injury in individuals of European ancestry: A prospective cohort study

After similar extent of injury there is considerable variability in scarring between individuals, in part due to genetic factors. This study aimed to identify genetic variants associated with scar height and pliability after burn injury. An exome-wide array association study and gene pathway analysis were performed on a prospective cohort of 665 patients treated for burn injury. Outcomes were scar height (SH) and scar pliability (SP) sub-scores of the modified Vancouver Scar Scale (mVSS). DNA was genotyped using the Infinium® HumanCoreExome-24 BeadChip. Associations between genetic variants (single nucleotide polymorphisms) and SH and SP were estimated using an additive genetic model adjusting for age, sex, number of surgical procedures and % total body surface area of burn in subjects of European ancestry. No individual genetic variants achieved the cut-off threshold of significance. Gene regions were analysed for spatially correlated single nucleotide polymorphisms and significant regions identified using comb-p software. This gene list was subject to gene pathway analysis to find which biological process terms were over-represented. Using this approach biological processes related to the nervous system and cell adhesion were the predominant gene pathways associated with both SH and SP. This study suggests genes associated with innervation may be important in scar fibrosis. Further studies using similar and larger datasets will be essential to validate these findings

Association of protein function-altering variants with cardiometabolic traits:the strong heart study

Clinical and biomarker phenotypic associations for carriers of protein function-altering variants may help to elucidate gene function and health effects in populations. We genotyped 1127 Strong Heart Family Study participants for protein function-altering single nucleotide variants (SNV) and indels selected from a low coverage whole exome sequencing of American Indians. We tested the association of each SNV/indel with 35 cardiometabolic traits. Among 1206 variants (average minor allele count = 20, range of 1 to 1064), similar to 43% were not present in publicly available repositories. We identified seven SNV-trait significant associations including a missense SNV at ABCA10 (rs779392624, p= 8 x 10(-9)) associated with fasting triglycerides, which gene product is involved in macrophage lipid homeostasis. Among non-diabetic individuals, missense SNVs at four genes were associated with fasting insulin adjusted for BMI (PHIL, chr6:79,650,711, p= 2.1 x 10(-6); TRPM3, rs760461668, p= 5 x10(-8); SPTY2D1, rs756851199, p= 1.6 x 10(-8); and TSPO, rs566547284, p= 2.4 x 10(-6)). PHIL encoded protein is involved in pancreatic beta-cell proliferation and survival, and TRPM3 protein mediates calcium signaling in pancreatic beta-cells in response to glucose. A genetic risk score combining increasing insulin risk alleles of these four genes was associated with 53% (95% confidence interval 1.09, 2.15) increased odds of incident diabetes and 83% (95% confidence interval 1.35, 2.48) increased odds of impaired fasting glucose at follow-up. Our study uncovered novel gene-trait associations through the study of protein-coding variants and demonstrates the advantages of association screenings targeting diverse and high-risk populations to study variants absent in publicly available repositories

Epigenetic effects of metformin: From molecular mechanisms to clinical implications

There is a growing body of evidence that links epigenetic modifications to type 2 diabetes. Researchers have more recently investigated effects of commonly used medications, including those prescribed for diabetes, on epigenetic processes. This work reviews the influence of the widely used antidiabetic drug metformin on epigenomics, microRNA levels and subsequent gene expression, and potential clinical implications. Metformin may influence the activity of numerous epigenetic modifying enzymes, mostly by modulating the activation of AMP-activated protein kinase (AMPK). Activated AMPK can phosphorylate numerous substrates, including epigenetic enzymes such as histone acetyltransferases (HATs), class II histone deacetylases (HDACs) and DNA methyltransferases (DNMTs), usually resulting in their inhibition; however, HAT1 activity may be increased. Metformin has also been reported to decrease expression of multiple histone methyltransferases, to increase the activity of the class III HDAC SIRT1 and to decrease the influence of DNMT inhibitors. There is evidence that these alterations influence the epigenome and gene expression, and may contribute to the antidiabetic properties of metformin and, potentially, may protect against cancer, cardiovascular disease, cognitive decline and aging. The expression levels of numerous microRNAs are also reportedly influenced by metformin treatment and may confer antidiabetic and anticancer activities. However, as the reported effects of metformin on epigenetic enzymes act to both increase and decrease histone acetylation, histone and DNA methylation, and gene expression, a significant degree of uncertainty exists concerning the overall effect of metformin on the epigenome, on gene expression, and on the subsequent effect on the health of metformin users