Heritability and genetic correlations explained by common SNPs for metabolic syndrome traits

Direct Estimates of the Genomic Contributions to Blood Pressure Heritability within a Population-Based Cohort (ARIC)

Blood pressure (BP) is a heritable trait with multiple environmental and genetic contributions, with current heritability estimates from twin and family studies being ~ 40%. Here, we use genome-wide polymorphism data from the Atherosclerosis Risk in Communities (ARIC) study to estimate BP heritability from genomic relatedness among cohort members. We utilized data on 6,365,596 and 9,578,528 genotyped and imputed common single nucleotide polymorphisms (SNPs), in 8,901 European ancestry (EA) and 2,860 African Ancestry (AA) ARIC participants, respectively, and a mixed linear model for analyses, to make four observations. First, for BP measurements, the heritability is ~20%/~50% and ~27%/~39% for systolic (SBP)/diastolic (DBP) blood pressure in European and African ancestry individuals, respectively, consistent with prior studies. Second, common variants with allele frequency >10% recapitulate most of the BP heritability in these data. Third, the vast majority of BP heritability varies by chromosome, depending on its length, and is largely concentrated in noncoding genomic regions annotated as DNaseI hypersensitive sites (DHSs). Fourth, the majority of this heritability arises from loci not harboring currently known cardiovascular and renal genes. Recent meta-analyses of large-scale genome-wide association studies (GWASs) and admixture mapping have identified ~50 loci associated with BP and hypertension (HTN), and yet they account for only a small fraction (~2%) of the heritability.

Transgenerational inheritance of metabolic disease

Metabolic disease encompasses several disorders including obesity, type 2 diabetes, and dyslipidemia. Recently, the incidence of metabolic disease has drastically increased, driven primarily by a worldwide obesity epidemic. Transgenerational inheritance remains controversial, but has been proposed to contribute to human metabolic disease risk based on a growing number of proof-of-principle studies in model organisms ranging from Caenorhabditis elegans to Mus musculus to Sus scrofa. Collectively, these studies demonstrate that heritable risk is epigenetically transmitted from parent to offspring over multiple generations in the absence of a continued exposure to the triggering stimuli. A diverse assortment of initial triggers can induce transgenerational inheritance including high-fat or high-sugar diets, low-protein diets, various toxins, and ancestral genetic variants. Although the mechanistic basis underlying the transgenerational inheritance of disease risk remains largely unknown, putative molecules mediating transmission include small RNAs, histone modifications, and DNA methylation. Due to the considerable impact of metabolic disease on human health, it is critical to better understand the role of transgenerational inheritance of metabolic disease risk to open new avenues for therapeutic intervention and improve upon the current methods for clinical diagnoses and treatment.

Epigenome-wide study identifies novel methylation loci associated with body mass index and waist circumference

OBJECTIVE

To conduct an epigenome-wide analysis of DNA methylation and obesity traits.

METHODS

DNA methylation was quantified in CD4+ T-cells using the Illumina Infinium HumanMethylation450 array in 991 participants of the Genetics of Lipid Lowering Drugs and Diet Network. Methylation at individual cytosine-phosphate-guanine (CpG) sites as a function of body mass index (BMI) and waist circumference (WC), adjusting for age, gender, study site, T-cell purity, smoking, and family structure, was modeled.

RESULTS

Epigenome-wide significant associations between eight CpG sites and BMI and five CpG sites and WC, successfully replicating the top hits in whole blood samples from the Framingham Heart Study (n = 2,377) and the Atherosclerosis Risk in Communities study (n = 2,097), were found. Top findings were in CPT1A (meta-analysis P = 2.7 × 10(-43) for BMI and 9.9 × 10(-23) for WC), PHGDH (meta-analysis P = 2.0 × 10(-15) for BMI and 4.0 × 10(-9) for WC), CD38 (meta-analysis P = 6.3 × 10(-11) for BMI and 1.6 × 10(-12) for WC), and long intergenic non-coding RNA 00263 (meta-analysis P = 2.2 × 10(-16) for BMI and 8.9 × 10(-14) for WC), regions with biologically plausible relationships to adiposity.

CONCLUSIONS

This large-scale epigenome-wide study discovered and replicated robust associations between DNA methylation at CpG loci and obesity indices, laying the groundwork for future diagnostic and/or therapeutic applications.

Implications of pleiotropy: challenges and opportunities for mining Big Data in biomedicine

Pleiotropy arises when a locus influences multiple traits. Rich GWAS findings of various traits in the past decade reveal many examples of this phenomenon, suggesting the wide existence of pleiotropic effects. What underlies this phenomenon is the biological connection among seemingly unrelated traits/diseases. Characterizing the molecular mechanisms of pleiotropy not only helps to explain the relationship between diseases, but may also contribute to novel insights concerning the pathological mechanism of each specific disease, leading to better disease prevention, diagnosis and treatment. However, most pleiotropic effects remain elusive because their functional roles have not been systematically examined. A systematic investigation requires availability of qualified measurements at multilayered biological processes (e.g., transcription and translation). The rise of Big Data in biomedicine, such as high-quality multi-omics data, biomedical imaging data and electronic medical records of patients, offers us an unprecedented opportunity to investigate pleiotropy. There will be a great need of computationally efficient and statistically rigorous methods for integrative analysis of these Big Data in biomedicine. In this review, we outline many opportunities and challenges in methodology developments for systematic analysis of pleiotropy, and highlight its implications on disease prevention, diagnosis and treatment.

Genetic and baseline metabolic factors for incident diabetes and HbA(1c) at follow-up: the healthy twin study

BACKGROUND

We investigated baseline anthropometric/metabolic traits predicting incident diabetes, genetic/environmental relationships between these traits and HbA1c at follow-up and the contribution of genetics, covariates and environments to variance in HbA(1c) at follow-up and incident diabetes.

METHODS

Nondiabetic twins (n = 869) and their family members (n = 949) were followed over 3.7 ± 1.4 years (44.3 ± 12.8 years of age); baseline anthropometric/metabolic traits were measured. Fasting plasma glucose and HbA(1c) were measured at follow-up. Incident diabetes was defined as HbA(1c) ≥6.5% or fasting plasma glucose ≥7 mmol/L.

RESULTS

Age-adjusted incident diabetes was 4.9% in men and 4.1% in women. Odd ratio for incident diabetes was 2.34-2.40, 1.25-1.28, 1.22-1.27 and 1.89 per standard deviation of baseline fasting plasma glucose, white blood cell (WBC), triglycerides and waist circumference, respectively, in multivariate generalized estimating equation models (p < 0.05). Age-adjusted and sex-adjusted heritability was 0.85 for diabetes and 0.72 for HbA(1c). In bivariate analyses adjusted for age, sex and body mass index at baseline, HbA1c at follow-up showed significant genetic and environmental correlations with baseline glucose (0.44, 0.17), significant genetic correlation with baseline waist circumference (0.16) and triglycerides (0.30) and significant environmental correlation with baseline WBC (0.09). Variance in HbA1c at follow-up and incident diabetes was explained by genetics (33% and 28%, respectively), covariates (36% and 48%, respectively), shared environments (7% and 0%, respectively) and errors (24% and 24%, respectively).

CONCLUSIONS

High values for baseline fasting plasma glucose, WBC, triglycerides and waist circumference are independent risk factors for incident diabetes. While genetic influences strongly contribute to variance in HbA1c at follow-up and incident diabetes, these risk factors significantly contribute to the remaining variance.

Genomic prediction of complex human traits: relatedness, trait architecture and predictive meta-models

We explore the prediction of individuals' phenotypes for complex traits using genomic data. We compare several widely used prediction models, including Ridge Regression, LASSO and Elastic Nets estimated from cohort data, and polygenic risk scores constructed using published summary statistics from genome-wide association meta-analyses (GWAMA). We evaluate the interplay between relatedness, trait architecture and optimal marker density, by predicting height, body mass index (BMI) and high-density lipoprotein level (HDL) in two data cohorts, originating from Croatia and Scotland. We empirically demonstrate that dense models are better when all genetic effects are small (height and BMI) and target individuals are related to the training samples, while sparse models predict better in unrelated individuals and when some effects have moderate size (HDL). For HDL sparse models achieved good across-cohort prediction, performing similarly to the GWAMA risk score and to models trained within the same cohort, which indicates that, for predicting traits with moderately sized effects, large sample sizes and familial structure become less important, though still potentially useful. Finally, we propose a novel ensemble of whole-genome predictors with GWAMA risk scores and demonstrate that the resulting meta-model achieves higher prediction accuracy than either model on its own. We conclude that although current genomic predictors are not accurate enough for diagnostic purposes, performance can be improved without requiring access to large-scale individual-level data. Our methodologically simple meta-model is a means of performing predictive meta-analysis for optimizing genomic predictions and can be easily extended to incorporate multiple population-level summary statistics or other domain knowledge.

The uniform-score gene set analysis for identifying common pathways associated with different diabetes traits

Background

Genetic heritability and expression study have shown that different diabetes traits have common genetic components and pathways. A computationally efficient pathway analysis of GWAS results will benefit post-GWAS study of SNP associations and identification of common genetic pathways from diabetes GWAS can help to improve understanding of the disease pathogenesis.

Results

We proposed a uniform-score gene-set analysis (USGSA) with implemented package to unify different gene measures by a uniform score for identifying pathways from GWAS data, and use a pre-generated permutation distribution table to quickly obtain multiple-testing adjusted p-value. Simulation studies of uniform score for four gene measures (minP, 2ndP, simP and fishP) have shown that USGSA has strictly controlled family-wise error rate. The power depends on types of gene measure. USGSA with a two-stage study strategy was applied to identify common pathways associated with diabetes traits based on public dbGaP GWAS results. The study identified 7 gene sets that contain binding motifs at promoter region of component genes for 5 transcription factors (TFs) of FOXO4, TCF3, NFAT, VSX1 and POU2F1, and 1 microRNA of mir-218. These gene sets include 25 common genes that are among top 5% of the gene associations over genome for all GWAS. Previous evidences showed that nearly all of these genes are mainly expressed in the brain.

Conclusions

USGSA is a computationally efficient approach for pathway analysis of GWAS data with promoted interpretability and comparability. The pathway analysis suggested that different diabetes traits share common pathways and component genes are potentially regulated by common TFs and microRNA. The result also indicated that the central nervous system has a critical role in diabetes pathogenesis. The findings will be important in formulating novel hypotheses for guiding follow-up studies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1515-3) contains supplementary material, which is available to authorized users.

Age- and gender-dependent heterogeneous proportion of variation explained by SNPs in quantitative traits reflecting human health

Age-related effects are often included as covariates in the analytical model for genome-wide association analysis of quantitative traits reflecting human health. Nevertheless, previous studies have hardly examined the effects of age on the proportion of variation explained by single nucleotide polymorphisms (PVSNP) in these traits. In this study, the PVSNP estimates of body mass index (BMI), waist-to-hip ratio, pulse pressure, high-density lipoprotein cholesterol level, triglyceride level (TG), low-density lipoprotein cholesterol level, and glucose level were obtained from Korean consortium metadata partitioned by gender or by age. Restricted maximum likelihood estimates of the PVSNP were obtained in a mixed model framework. Previous studies using pedigree data suggested possible differential heritability of certain traits with regard to gender, which we observed in our current study (BMI and TG; P < 0.05). However, the PVSNP analysis based on age revealed that, with respect to every trait tested, individuals aged 40 to 49 exhibited significantly lower PVSNP estimates than individuals aged 50 to 59 or 60 to 69 (P < 0.05). The consistent heterogeneous PVSNP with respect to age may be due to degenerated genetic functions in individuals between the ages of 50 and 69. Our results suggest the genetic mechanism of age- and gender-dependent PVSNP of quantitative traits related to human health should be further examined.

No association between genetic or epigenetic variation in insulin growth factors and antipsychotic-induced metabolic disturbances in a cross-sectional sample

AIM

Second-generation antipsychotics (SGA) are known to induce metabolic disturbances. Genetic pathways, such as the IGF pathway could be associated with increased metabolic syndrome (MetS). Additionally, IGF2 methylation varies as a function of environmental influences and is associated with schizophrenia and MetS. The current study aims to evaluate whether genetic and epigenetic variation in genes of the IGF pathway are associated with metabolic disturbances in patients under treatment with SGAs.

METHODS

Cross-sectional metabolic data from 438 patients with schizophrenia spectrum disorder was analyzed. Using the Sequenom MassARRAY iPLEX(TM) platform, 27 SNPs of the IGF1 and IGF2 genes and the IGF receptors IGF1R and IGF2R were genotyped. Methylation status of seven IGF2 CpG dinucleotides was evaluated using a Sequenom MALDI-TOF spectrometer.

RESULTS & CONCLUSION

There was a significant association between IGF2 methylation and genotype, but no significant association between genetic or epigenetic variability and metabolic parameters in the present study.

Metabolic regulation and energy homeostasis through the primary Cilium

Obesity and diabetes represent a significant healthcare concern. In contrast to genome-wide association studies that, some exceptions notwithstanding, have offered modest clues about pathomechanism, the dissection of rare disorders in which obesity represents a core feature have highlighted key molecules and structures critical to energy regulation. Here we focus on the primary cilium, an organelle whose roles in energy homeostasis have been underscored by the high incidence of obesity and type II diabetes in patients and mouse mutants with compromised ciliary function. We discuss recent evidence linking ciliary dysfunction to metabolic defects and we explore the contribution of neuronal and nonneuronal cilia to these phenotypes.

Marker-based estimation of heritability in immortal populations

Heritability is a central parameter in quantitative genetics, from both an evolutionary and a breeding perspective. For plant traits heritability is traditionally estimated by comparing within- and between-genotype variability. This approach estimates broad-sense heritability and does not account for different genetic relatedness. With the availability of high-density markers there is growing interest in marker-based estimates of narrow-sense heritability, using mixed models in which genetic relatedness is estimated from genetic markers. Such estimates have received much attention in human genetics but are rarely reported for plant traits. A major obstacle is that current methodology and software assume a single phenotypic value per genotype, hence requiring genotypic means. An alternative that we propose here is to use mixed models at the individual plant or plot level. Using statistical arguments, simulations, and real data we investigate the feasibility of both approaches and how these affect genomic prediction with the best linear unbiased predictor and genome-wide association studies. Heritability estimates obtained from genotypic means had very large standard errors and were sometimes biologically unrealistic. Mixed models at the individual plant or plot level produced more realistic estimates, and for simulated traits standard errors were up to 13 times smaller. Genomic prediction was also improved by using these mixed models, with up to a 49% increase in accuracy. For genome-wide association studies on simulated traits, the use of individual plant data gave almost no increase in power. The new methodology is applicable to any complex trait where multiple replicates of individual genotypes can be scored. This includes important agronomic crops, as well as bacteria and fungi.

The Functional Copy Number Variation-67048 in WWOX Contributes to Increased Risk of COPD in Southern and Eastern Chinese

Recent studies have recognized the genetic variants in the WW domain-containing oxidoreductase (WWOX) gene as genetic determinants of lung function, reflecting that the WWOX gene may be a susceptible factor of chronic obstructive pulmonary disease (COPD), which characters as poor lung function. We have previously showed that the copy number variation-67048 (CNV-67048) of WWOX was associated with lung cancer risk. Here, we hypothesized that the CNV-67048 affects COPD susceptibility. Based on a two-stage case-control study with a total of 1791 COPD patients and 1940 controls of southern and eastern Chinese, we found that the loss genotypes (0-copy and 1-copy) of CNV-67048 harbored a significantly increased risk of COPD, with an odds ratio (OR) as 1.29 (1.11-1.49) when compared with the common 2-copy genotype. The pre-forced expiratory volume in one second (pre-FEV1) to pre-forced vital capacity (pre-FVC) of carriers with loss genotypes (0.729 ± 0.130) was significantly lower than carriers with 2-copy genotype (0.747 ± 0.124; p = 7.93 × 10(-5)). However, no significant difference was observed on pre-FEV1, pre-FVC and the annual decline of pre-FEV1 between the loss genotypes and 2-copy genotype carriers. Our data suggest that the loss genotypes of CNV-67048 in WWOX predispose their carriers to COPD, which might be a genetic biomarker to predict risk of COPD in Chinese.

Leveraging population admixture to characterize the heritability of complex traits

Despite recent progress on estimating the heritability explained by genotyped SNPs (h(2)g), a large gap between h(2)g and estimates of total narrow-sense heritability (h(2)) remains. Explanations for this gap include rare variants or upward bias in family-based estimates of h(2) due to shared environment or epistasis. We estimate h(2) from unrelated individuals in admixed populations by first estimating the heritability explained by local ancestry (h(2)γ). We show that h(2)γ = 2FSTCθ(1 - θ)h(2), where FSTC measures frequency differences between populations at causal loci and θ is the genome-wide ancestry proportion. Our approach is not susceptible to biases caused by epistasis or shared environment. We applied this approach to the analysis of 13 phenotypes in 21,497 African-American individuals from 3 cohorts. For height and body mass index (BMI), we obtained h(2) estimates of 0.55 ± 0.09 and 0.23 ± 0.06, respectively, which are larger than estimates of h(2)g in these and other data but smaller than family-based estimates of h(2).

Duplicated copy of CHRNA7 increases risk and worsens prognosis of COPD and lung cancer

Recent genome-wide association studies implicated that the nicotinic acetylcholine receptors (nAChRs) are common susceptible genes of two contextual diseases: chronic obstructive pulmonary disease (COPD) and lung cancer. We aimed to test whether the copy number variations (CNVs) in nAChRs have hereditary contributions to development of the two diseases. In two, two-stage, case-control studies of southern and eastern Chinese, a common CNV-3956 that duplicates the cholinergic receptor, nicotinic, α7 (CHRNA7) gene was genotyped in a total of 7880 subjects and its biological phenotype was assessed. The ≥4-copy of CNV-3956 increased COPD risk (≥4-copy vs 2/3-copy: OR=1.44, 95% CI=1.23-1.68) and caused poor lung function, and it similarly augmented risk (OR=1.49, 95% CI=1.29-1.73) and worsened prognosis (hazard ratio (HR)=1.25, 95% CI=1.07-1.45) of lung cancer. The ≥4-copy was estimated to account for 1.56% of COPD heritability and 1.87% of lung cancer heritability, respectively. Phenotypic analysis further showed that the ≥4-copy of CNV-3956 improved CHRNA7 expression in vivo and increased the carriers' smoking amount. The CNV-3956 of CHRNA7 contributed to increased risks and poor prognoses of both COPD and lung cancer, and this may be a genetic biomarker of the two diseases.

GPA: a statistical approach to prioritizing GWAS results by integrating pleiotropy and annotation

Results from Genome-Wide Association Studies (GWAS) have shown that complex diseases are often affected by many genetic variants with small or moderate effects. Identifications of these risk variants remain a very challenging problem. There is a need to develop more powerful statistical methods to leverage available information to improve upon traditional approaches that focus on a single GWAS dataset without incorporating additional data. In this paper, we propose a novel statistical approach, GPA (Genetic analysis incorporating Pleiotropy and Annotation), to increase statistical power to identify risk variants through joint analysis of multiple GWAS data sets and annotation information because: (1) accumulating evidence suggests that different complex diseases share common risk bases, i.e., pleiotropy; and (2) functionally annotated variants have been consistently demonstrated to be enriched among GWAS hits. GPA can integrate multiple GWAS datasets and functional annotations to seek association signals, and it can also perform hypothesis testing to test the presence of pleiotropy and enrichment of functional annotation. Statistical inference of the model parameters and SNP ranking is achieved through an EM algorithm that can handle genome-wide markers efficiently. When we applied GPA to jointly analyze five psychiatric disorders with annotation information, not only did GPA identify many weak signals missed by the traditional single phenotype analysis, but it also revealed relationships in the genetic architecture of these disorders. Using our hypothesis testing framework, statistically significant pleiotropic effects were detected among these psychiatric disorders, and the markers annotated in the central nervous system genes and eQTLs from the Genotype-Tissue Expression (GTEx) database were significantly enriched. We also applied GPA to a bladder cancer GWAS data set with the ENCODE DNase-seq data from 125 cell lines. GPA was able to detect cell lines that are biologically more relevant to bladder cancer. The R implementation of GPA is currently available at http://dongjunchung.github.io/GPA/.

Cross-tissue and tissue-specific eQTLs: partitioning the heritability of a complex trait

Top signals from genome-wide association studies (GWASs) of type 2 diabetes (T2D) are enriched with expression quantitative trait loci (eQTLs) identified in skeletal muscle and adipose tissue. We therefore hypothesized that such eQTLs might account for a disproportionate share of the heritability estimated from all SNPs interrogated through GWASs. To test this hypothesis, we applied linear mixed models to the Wellcome Trust Case Control Consortium (WTCCC) T2D data set and to data sets representing Mexican Americans from Starr County, TX, and Mexicans from Mexico City. We estimated the proportion of phenotypic variance attributable to the additive effect of all variants interrogated in these GWASs, as well as a much smaller set of variants identified as eQTLs in human adipose tissue, skeletal muscle, and lymphoblastoid cell lines. The narrow-sense heritability explained by all interrogated SNPs in each of these data sets was substantially greater than the heritability accounted for by genome-wide-significant SNPs (∼10%); GWAS SNPs explained over 50% of phenotypic variance in the WTCCC, Starr County, and Mexico City data sets. The estimate of heritability attributable to cross-tissue eQTLs was greater in the WTCCC data set and among lean Hispanics, whereas adipose eQTLs significantly explained heritability among Hispanics with a body mass index ≥ 30. These results support an important role for regulatory variants in the genetic component of T2D susceptibility, particularly for eQTLs that elicit effects across insulin-responsive peripheral tissues.

Genetic association analyses of fast plasma glucose level in pre-menopausal Chinese women: potential interaction between osteocalcin and oestrogen receptor α

BACKGROUND

Fasting plasma glucose (FPG) levels are usually tightly regulated within a narrow physiologic range. Variation of FPG levels is clinically important and is strongly heritable. Several lines of evidence suggest the importance of the oestrogen receptor α (ER-α) and osteocalcin (also known as BGP, for bone Gla protein) in determining FPG; however, whether their polymorphisms are associated with FPG variation is not well understood.

AIM

To investigate whether ER-a PvuII and BGP HindIII genetic polymorphisms and their potential interaction are associated with FPG variation.

SUBJECTS AND METHODS

The study subjects were 328 unrelated pre-menopausal Chinese women aged 21 years and over (mean age ± SD, 33.2 ± 5.9 years), with an average FPG of 4.92 (SD = 0.81). All subjects were genotyped at the ER-α PvuII and BGP HindIII loci using polymerase chain reaction (PCR)-restriction fragment length polymorphism (RFLP).

RESULTS

The ER-α PvuII genotypes were significantly associated with FPG (p = 0.007). In addition, a significant interaction was observed of the ER-α PvuII polymorphism with BGP HindIII polymorphism on FPG variation (p = 0.013), although the BGP HindIII polymorphism was not shown to be individually associated with FPG.

CONCLUSION

The PvuII polymorphism of the ER-α gene and its potential interaction with the HindIII polymorphism of the BGP gene were associated with FPG in pre-menopausal Chinese women.

Assessing multivariate gene-metabolome associations with rare variants using Bayesian reduced rank regression

MOTIVATION

A typical genome-wide association study searches for associations between single nucleotide polymorphisms (SNPs) and a univariate phenotype. However, there is a growing interest to investigate associations between genomics data and multivariate phenotypes, for example, in gene expression or metabolomics studies. A common approach is to perform a univariate test between each genotype-phenotype pair, and then to apply a stringent significance cutoff to account for the large number of tests performed. However, this approach has limited ability to uncover dependencies involving multiple variables. Another trend in the current genetics is the investigation of the impact of rare variants on the phenotype, where the standard methods often fail owing to lack of power when the minor allele is present in only a limited number of individuals.

RESULTS

We propose a new statistical approach based on Bayesian reduced rank regression to assess the impact of multiple SNPs on a high-dimensional phenotype. Because of the method's ability to combine information over multiple SNPs and phenotypes, it is particularly suitable for detecting associations involving rare variants. We demonstrate the potential of our method and compare it with alternatives using the Northern Finland Birth Cohort with 4702 individuals, for whom genome-wide SNP data along with lipoprotein profiles comprising 74 traits are available. We discovered two genes (XRCC4 and MTHFD2L) without previously reported associations, which replicated in a combined analysis of two additional cohorts: 2390 individuals from the Cardiovascular Risk in Young Finns study and 3659 individuals from the FINRISK study.

AVAILABILITY AND IMPLEMENTATION

R-code freely available for download at http://users.ics.aalto.fi/pemartti/gene_metabolome/.

Estimating quantitative genetic parameters in wild populations: a comparison of pedigree and genomic approaches

The estimation of quantitative genetic parameters in wild populations is generally limited by the accuracy and completeness of the available pedigree information. Using relatedness at genomewide markers can potentially remove this limitation and lead to less biased and more precise estimates. We estimated heritability, maternal genetic effects and genetic correlations for body size traits in an unmanaged long-term study population of Soay sheep on St Kilda using three increasingly complete and accurate estimates of relatedness: (i) Pedigree 1, using observation-derived maternal links and microsatellite-derived paternal links; (ii) Pedigree 2, using SNP-derived assignment of both maternity and paternity; and (iii) whole-genome relatedness at 37 037 autosomal SNPs. In initial analyses, heritability estimates were strikingly similar for all three methods, while standard errors were systematically lower in analyses based on Pedigree 2 and genomic relatedness. Genetic correlations were generally strong, differed little between the three estimates of relatedness and the standard errors declined only very slightly with improved relatedness information. When partitioning maternal effects into separate genetic and environmental components, maternal genetic effects found in juvenile traits increased substantially across the three relatedness estimates. Heritability declined compared to parallel models where only a maternal environment effect was fitted, suggesting that maternal genetic effects are confounded with direct genetic effects and that more accurate estimates of relatedness were better able to separate maternal genetic effects from direct genetic effects. We found that the heritability captured by SNP markers asymptoted at about half the SNPs available, suggesting that denser marker panels are not necessarily required for precise and unbiased heritability estimates. Finally, we present guidelines for the use of genomic relatedness in future quantitative genetics studies in natural populations.

Applying compressed sensing to genome-wide association studies

Background

The aim of a genome-wide association study (GWAS) is to isolate DNA markers for variants affecting phenotypes of interest. This is constrained by the fact that the number of markers often far exceeds the number of samples. Compressed sensing (CS) is a body of theory regarding signal recovery when the number of predictor variables (i.e., genotyped markers) exceeds the sample size. Its applicability to GWAS has not been investigated.

Results

Using CS theory, we show that all markers with nonzero coefficients can be identified (selected) using an efficient algorithm, provided that they are sufficiently few in number (sparse) relative to sample size. For heritability equal to one (h² = 1), there is a sharp phase transition from poor performance to complete selection as the sample size is increased. For heritability below one, complete selection still occurs, but the transition is smoothed. We find for h² ∼ 0.5 that a sample size of approximately thirty times the number of markers with nonzero coefficients is sufficient for full selection. This boundary is only weakly dependent on the number of genotyped markers.

Conclusion

Practical measures of signal recovery are robust to linkage disequilibrium between a true causal variant and markers residing in the same genomic region. Given a limited sample size, it is possible to discover a phase transition by increasing the penalization; in this case a subset of the support may be recovered. Applying this approach to the GWAS analysis of height, we show that 70-100% of the selected markers are strongly correlated with height-associated markers identified by the GIANT Consortium.

Genetic architecture of lipid traits changes over time and differs by race: Princeton Lipid Follow-up Study

Dyslipidemia is a major risk factor for CVD. Previous studies on lipid heritability have largely focused on white populations assessed after the obesity epidemic. Given secular trends and racial differences in lipid levels, this study explored whether lipid heritability is consistent across time and between races. African American and white nuclear families had fasting lipids measured in the 1970s and 22-30 years later. Heritability was estimated, and bivariate analyses between visits were conducted by race using variance components analysis. A total of 1,454 individuals (age 14.1/40.6 for offspring/parents at baseline; 39.6/66.5 at follow-up) in 373 families (286 white, 87 African American) were included. Lipid trait heritabilities were typically stronger during the 1970s than the 2000s. At baseline, additive genetic variation for LDL was significantly lower in African Americans than whites (P = 0.015). Shared genetic contribution to lipid variability over time was significant in both whites (all P < 0.0001) and African Americans (P ≤ 0.05 for total, LDL, and HDL cholesterol). African American families demonstrated shared environmental contributions to lipid variation over time (all P ≤ 0.05). Lower heritability, lower LDL genetic variance, and durable environmental effects across the obesity epidemic in African American families suggest race-specific approaches are needed to clarify the genetic etiology of lipids.

Improving genetic risk prediction by leveraging pleiotropy

An important task of human genetics studies is to predict accurately disease risks in individuals based on genetic markers, which allows for identifying individuals at high disease risks, and facilitating their disease treatment and prevention. Although hundreds of genome-wide association studies (GWAS) have been conducted on many complex human traits in recent years, there has been only limited success in translating these GWAS data into clinically useful risk prediction models. The predictive capability of GWAS data is largely bottlenecked by the available training sample size due to the presence of numerous variants carrying only small to modest effects. Recent studies have shown that different human traits may share common genetic bases. Therefore, an attractive strategy to increase the training sample size and hence improve the prediction accuracy is to integrate data from genetically correlated phenotypes. Yet, the utility of genetic correlation in risk prediction has not been explored in the literature. In this paper, we analyzed GWAS data for bipolar and related disorders and schizophrenia with a bivariate ridge regression method, and found that jointly predicting the two phenotypes could substantially increase prediction accuracy as measured by the area under the receiver operating characteristic curve. We also found similar prediction accuracy improvements when we jointly analyzed GWAS data for Crohn's disease and ulcerative colitis. The empirical observations were substantiated through our comprehensive simulation studies, suggesting that a gain in prediction accuracy can be obtained by combining phenotypes with relatively high genetic correlations. Through both real data and simulation studies, we demonstrated pleiotropy can be leveraged as a valuable asset that opens up a new opportunity to improve genetic risk prediction in the future.

Conditions for the validity of SNP-based heritability estimation

The heritability of a trait (h(2)) is the proportion of its population variance caused by genetic differences, and estimates of this parameter are important for interpreting the results of genome-wide association studies (GWAS). In recent years, researchers have adopted a novel method for estimating a lower bound on heritability directly from GWAS data that uses realized genetic similarities between nominally unrelated individuals. The quantity estimated by this method is purported to be the contribution to heritability that could in principle be recovered from association studies employing the given panel of SNPs (h(2)(SNP)). Thus far, the validity of this approach has mostly been tested empirically. Here, we provide a mathematical explication and show that the method should remain a robust means of obtaining h(2)(SNP)) under circumstances wider than those under which it has so far been derived.

Satiety mechanisms in genetic risk of obesity

IMPORTANCE

A better understanding of the cause of obesity is a clinical priority. Obesity is highly heritable, and specific genes are being identified. Discovering the mechanisms through which obesity-related genes influence weight would help pinpoint novel targets for intervention. One potential mechanism is satiety responsiveness. Lack of satiety characterizes many monogenic obesity disorders, and lower satiety responsiveness is linked with weight gain in population samples.

OBJECTIVE

To test the hypothesis that satiety responsiveness is an intermediate behavioral phenotype associated with genetic predisposition to obesity in children.

DESIGN, SETTING, AND PARTICIPANTS

Cross-sectional observational study of a population-based cohort of twins born January 1, 1994, to December 31, 1996 (Twins Early Development Study). Participants included 2258 unrelated children (53.3% female; mean [SD] age, 9.9 [0.8] years), one randomly selected from each twin pair.

EXPOSURE

Genetic predisposition to obesity. We created a polygenic risk score (PRS) comprising 28 common obesity-related single-nucleotide polymorphisms identified in a meta-analysis of obesity-related genome-wide association studies.

MAIN OUTCOMES AND MEASURES

Satiety responsiveness was indexed with a standard psychometric scale (Child Eating Behavior Questionnaire). Using 1990 United Kingdom reference data, body mass index SD scores and waist SD scores were calculated from parent-reported anthropometric data for each child. Information on satiety responsiveness, anthropometrics, and genotype was available for 2258 children. We examined associations among the PRS, adiposity, and satiety responsiveness.

RESULTS

The PRS was negatively related to satiety responsiveness (β coefficient, -0.060; 95% CI, -0.019 to -0.101) and positively related to adiposity (β coefficient, 0.177; 95% CI, 0.136-0.218 for body mass index SD scores and β coefficient, 0.167; 95% CI, 0.126-0.208 for waist SD scores). More children in the top 25% of the PRS were overweight than in the lowest 25% (18.5% vs 7.2%; odds ratio, 2.90; 95% CI, 1.98-4.25). Associations between the PRS and adiposity were significantly mediated by satiety responsiveness (P = .006 for body mass index SD scores and P = .005 for waist SD scores).

CONCLUSIONS AND RELEVANCE

These results support the hypothesis that low satiety responsiveness is one of the mechanisms through which genetic predisposition leads to weight gain in an environment rich with food. Strategies to enhance satiety responsiveness could help prevent weight gain in genetically at-risk children.

Efficient multivariate linear mixed model algorithms for genome-wide association studies

Multivariate linear mixed models (mvLMMs) are powerful tools for testing associations between single-nucleotide polymorphisms and multiple correlated phenotypes while controlling for population stratification in genome-wide association studies. We present efficient algorithms in the genome-wide efficient mixed model association (GEMMA) software for fitting mvLMMs and computing likelihood ratio tests. These algorithms offer improved computation speed, power and P-value calibration over existing methods, and can deal with more than two phenotypes.

Explaining additional genetic variation in complex traits

Genome-wide association studies (GWAS) have provided valuable insights into the genetic basis of complex traits, discovering >6000 variants associated with >500 quantitative traits and common complex diseases in humans. The associations identified so far represent only a fraction of those that influence phenotype, because there are likely to be many variants across the entire frequency spectrum, each of which influences multiple traits, with only a small average contribution to the phenotypic variance. This presents a considerable challenge to further dissection of the remaining unexplained genetic variance within populations, which limits our ability to predict disease risk, identify new drug targets, improve and maintain food sources, and understand natural diversity. This challenge will be met within the current framework through larger sample size, better phenotyping, including recording of nongenetic risk factors, focused study designs, and an integration of multiple sources of phenotypic and genetic information. The current evidence supports the application of quantitative genetic approaches, and we argue that one should retain simpler theories until simplicity can be traded for greater explanatory power.

Whole-genome pathway analysis on 132,497 individuals identifies novel gene-sets associated with body mass index

Whole genome pathway analysis is a powerful tool for the exploration of the combined effects of gene-sets within biological pathways. This study applied Interval Based Enrichment Analysis (INRICH) to perform whole-genome pathway analysis of body-mass index (BMI). We used a discovery set composed of summary statistics from a meta-analysis of 123,865 subjects performed by the GIANT Consortium, and an independent sample of 8,632 subjects to assess replication of significant pathways. We examined SNPs within nominally significant pathways using linear mixed models to estimate their contribution to overall BMI heritability. Six pathways replicated as having significant enrichment for association after correcting for multiple testing, including the previously unknown relationships between BMI and the Reactome regulation of ornithine decarboxylase pathway, the KEGG lysosome pathway, and the Reactome stabilization of P53 pathway. Two non-overlapping sets of genes emerged from the six significant pathways. The clustering of shared genes based on previously identified protein-protein interactions listed in PubMed and OMIM supported the relatively independent biological effects of these two gene-sets. We estimate that the SNPs located in examined pathways explain ∼20% of the heritability for BMI that is tagged by common SNPs (3.35% of the 16.93% total).

Polygenic heritability estimates in pharmacogenetics: focus on asthma and related phenotypes

Although accurate measures of heritability are required to understand the pharmacogenetic basis of drug treatment response, these are generally not available, as it is unfeasible to give medications to individuals for which treatment is not indicated. Using a polygenic linear mixed modeling approach, we estimated lower bounds on the heritability of asthma and the heritability of two related drug-response phenotypes, bronchodilator response and airway hyperreactivity, using genome-wide single nucleotide polymorphism (SNP) data from existing asthma cohorts. Our estimate of the heritability for bronchodilator response is 28.5% (SE 16%, P=0.043) and airway hyperresponsiveness is 51.1% (SE 34%, P=0.064), whereas we estimate asthma genetic liability at 61.5% (SE 16%, P<0.001). Our results agree with the previously published estimates of the heritability of these traits, suggesting that the linear mixed modeling method is useful for computing the heritability of other pharmacogenetic traits. Furthermore, our results indicate that multiple SNP main effects, including SNPs as yet unidentified by genome-wide association study methods, together explain a sizable portion of the heritability of these traits.

Quantifying missing heritability at known GWAS loci

Recent work has shown that much of the missing heritability of complex traits can be resolved by estimates of heritability explained by all genotyped SNPs. However, it is currently unknown how much heritability is missing due to poor tagging or additional causal variants at known GWAS loci. Here, we use variance components to quantify the heritability explained by all SNPs at known GWAS loci in nine diseases from WTCCC1 and WTCCC2. After accounting for expectation, we observed all SNPs at known GWAS loci to explain more heritability than GWAS-associated SNPs on average (). For some diseases, this increase was individually significant: for Multiple Sclerosis (MS) () and for Crohn's Disease (CD) (); all analyses of autoimmune diseases excluded the well-studied MHC region. Additionally, we found that GWAS loci from other related traits also explained significant heritability. The union of all autoimmune disease loci explained more MS heritability than known MS SNPs () and more CD heritability than known CD SNPs (), with an analogous increase for all autoimmune diseases analyzed. We also observed significant increases in an analysis of Rheumatoid Arthritis (RA) samples typed on ImmunoChip, with more heritability from all SNPs at GWAS loci () and more heritability from all autoimmune disease loci () compared to known RA SNPs (including those identified in this cohort). Our methods adjust for LD between SNPs, which can bias standard estimates of heritability from SNPs even if all causal variants are typed. By comparing adjusted estimates, we hypothesize that the genome-wide distribution of causal variants is enriched for low-frequency alleles, but that causal variants at known GWAS loci are skewed towards common alleles. These findings have important ramifications for fine-mapping study design and our understanding of complex disease architecture.

Heritable diseases have an unknown underlying “genetic architecture” that defines the distribution of effect-sizes for disease-causing mutations. Understanding this genetic architecture is an important first step in designing disease-mapping studies, and many theories have been developed on the nature of this distribution. Here, we evaluate the hypothesis that additional heritable variation lies at previously known associated loci but is not fully explained by the single most associated marker. We develop methods based on variance-components analysis to quantify this type of “local” heritability, demonstrating that standard strategies can be falsely inflated or deflated due to correlation between neighboring markers and propose a robust adjustment. In analysis of nine common diseases we find a significant average increase of local heritability, consistent with multiple common causal variants at an average locus. Intriguingly, for autoimmune diseases we also observe significant local heritability in loci not associated with the specific disease but with other autoimmune diseases, implying a highly correlated underlying disease architecture. These findings have important implications to the design of future studies and our general understanding of common disease.

Whole-exome sequencing of 2,000 Danish individuals and the role of rare coding variants in type 2 diabetes

It has been hypothesized that, in aggregate, rare variants in coding regions of genes explain a substantial fraction of the heritability of common diseases. We sequenced the exomes of 1,000 Danish cases with common forms of type 2 diabetes (including body mass index > 27.5 kg/m(2) and hypertension) and 1,000 healthy controls to an average depth of 56×. Our simulations suggest that our study had the statistical power to detect at least one causal gene (a gene containing causal mutations) if the heritability of these common diseases was explained by rare variants in the coding regions of a limited number of genes. We applied a series of gene-based tests to detect such susceptibility genes. However, no gene showed a significant association with disease risk after we corrected for the number of genes analyzed. Thus, we could reject a model for the genetic architecture of type 2 diabetes where rare nonsynonymous variants clustered in a modest number of genes (fewer than 20) are responsible for the majority of disease risk.

Genotype by energy expenditure interaction with metabolic syndrome traits: the Portuguese healthy family study

Moderate-to-high levels of physical activity are established as preventive factors in metabolic syndrome development. However, there is variability in the phenotypic expression of metabolic syndrome under distinct physical activity conditions. In the present study we applied a Genotype X Environment interaction method to examine the presence of GxEE interaction in the phenotypic expression of metabolic syndrome. A total of 958 subjects, from 294 families of The Portuguese Healthy Family study, were included in the analysis. Total daily energy expenditure was assessed using a 3 day physical activity diary. Six metabolic syndrome related traits, including waist circumference, systolic blood pressure, glucose, HDL cholesterol, total cholesterol and triglycerides, were measured and adjusted for age and sex. GxEE examination was performed on SOLAR 4.3.1. All metabolic syndrome indicators were significantly heritable. The GxEE interaction model fitted the data better than the polygenic model (p<0.001) for waist circumference, systolic blood pressure, glucose, total cholesterol and triglycerides. For waist circumference, glucose, total cholesterol and triglycerides, the significant GxEE interaction was due to rejection of the variance homogeneity hypothesis. For waist circumference and glucose, GxEE was also significant by the rejection of the genetic correlation hypothesis. The results showed that metabolic syndrome traits expression is significantly influenced by the interaction established between total daily energy expenditure and genotypes. Physical activity may be considered an environmental variable that promotes metabolic differences between individuals that are distinctively active.

Pivotal role of microRNA-33 in metabolic syndrome: A systematic review

Metabolic syndrome (MetS) is a major public health concerns and increase in the incidence of MetS caused a rise in the rates of global morbidity, and mortality due to cardiovascular disease and diabetes. Lifestyle modification, a healthy diet, and pharmacological treatment and bariatric surgery are recommended in order to control this syndrome. Molecular mechanisms of metabolic disorders are essential in order to develop novel, valid therapeutic strategies. MicroRNA-33 plays imperative regulatory roles in a variety of biological processes including collaboration with sterol regulatory element-binding protein (SREBP) to maintain cholesterol homeostasis, high-density lipoprotein formation, fatty acid oxidation, and insulin signaling. Investigation of these molecules and their genetic targets may potentially identify new pathways involved in complex metabolic disease processes, improve our understanding of metabolic disorders, and influence future approaches to the treatment of obesity. This article reviews the role of miRNA-33 in metabolic syndrome, and highlights the potential of using miRNA-33 as a novel biomarker and therapeutic target for this syndrome.

Germline genetic contributions to risk for esophageal adenocarcinoma, Barrett's esophagus, and gastroesophageal reflux

BACKGROUND

Esophageal adenocarcinoma (EA) is an increasingly common cancer with poor survival. Barrett's esophagus (BE) is the main precursor to EA, and every year 0.12% to 0.5% of BE patients progress to EA. BE typically arises on a background of chronic gastroesophageal reflux (GERD), one of the risk factors for EA.

METHODS

We used genome-wide association data to investigate the genetic architecture underlying GERD, BE, and EA. We applied a method to estimate the variance explained (array heritability, h(2)g) and the genetic correlation (rg) between GERD, BE, and EA by considering all single nucleotide polymorphisms (SNPs) simultaneously. We also estimated the polygenic overlap between GERD, BE, and EA using a prediction approach. All tests were two-sided, except in the case of variance-explained estimation where one-sided tests were used.

RESULTS

We estimated a statistically significant genetic variance explained for BE (h(2)g = 35%; standard error [SE] = 6%; one-sided P = 1 × 10(-9)) and for EA (h(2)g = 25 %; SE = 5%; one-sided P = 2 × 10(-7)). The genetic correlation between BE and EA was found to be high (rg = 1.0; SE = 0.37). We also estimated a statistically significant polygenic overlap between BE and EA (one-sided P = 1 × 10(-6)), which suggests, together with the high genetic correlation, that shared genes underlie the development of BE and EA. Conversely, no statistically significant results were obtained for GERD.

CONCLUSIONS

We have demonstrated that risk to BE and EA is influenced by many germline genetic variants of small effect and that shared polygenic effects contribute to risk of these two diseases.

Familial aggregation of metabolic syndrome indicators in Portuguese families

Background and Aims. Family studies are well suited to investigate the genetic architecture underlying the metabolic syndrome (MetS). The purposes of this paper were (i) to estimate heritabilities for each of the MetS indicators, and (ii) to test the significance of familial intratrait and cross-trait correlations in MetS markers. Methods and Results. This study included 1,363 individuals from 515 Portuguese families in which five MetS components, including waist circumference (WC), blood pressure (BP), HDL-cholesterol, triglycerides (TG), and glucose (GLU), were measured. Intratrait and cross-trait familial correlations of these five components were estimated using Generalized Estimating Equations. Each MetS component was significantly heritable (h² ranged from 0.12 to 0.60) and exhibited strong familial resemblance with correlations between biological relatives of similar magnitude to those observed between spouses. With respect to cross-trait correlations, familial resemblance was very weak except for the HDL-TG pair. Conclusions. The present findings confirm the idea of familial aggregation in MetS traits. Spousal correlations were, in general, of the same magnitude as the biological relatives' correlations suggesting that most of the phenotypic variance in MetS traits could be explained by shared environment.

Using extended genealogy to estimate components of heritability for 23 quantitative and dichotomous traits

Important knowledge about the determinants of complex human phenotypes can be obtained from the estimation of heritability, the fraction of phenotypic variation in a population that is determined by genetic factors. Here, we make use of extensive phenotype data in Iceland, long-range phased genotypes, and a population-wide genealogical database to examine the heritability of 11 quantitative and 12 dichotomous phenotypes in a sample of 38,167 individuals. Most previous estimates of heritability are derived from family-based approaches such as twin studies, which may be biased upwards by epistatic interactions or shared environment. Our estimates of heritability, based on both closely and distantly related pairs of individuals, are significantly lower than those from previous studies. We examine phenotypic correlations across a range of relationships, from siblings to first cousins, and find that the excess phenotypic correlation in these related individuals is predominantly due to shared environment as opposed to dominance or epistasis. We also develop a new method to jointly estimate narrow-sense heritability and the heritability explained by genotyped SNPs. Unlike existing methods, this approach permits the use of information from both closely and distantly related pairs of individuals, thereby reducing the variance of estimates of heritability explained by genotyped SNPs while preventing upward bias. Our results show that common SNPs explain a larger proportion of the heritability than previously thought, with SNPs present on Illumina 300K genotyping arrays explaining more than half of the heritability for the 23 phenotypes examined in this study. Much of the remaining heritability is likely to be due to rare alleles that are not captured by standard genotyping arrays.

Interactions of several single nucleotide polymorphisms and high body mass index on serum lipid traits

The interactions between single nucleotide polymorphisms (SNPs) and high body mass index (BMI) on serum lipid profiles are limited. This study was undertaken to detect the interactions of 10 SNPs and high BMI on serum lipid traits in an isolated population. A total of 978 normal BMI (< 24 kg/m2) and 751 high BMI (≥ 24 kg/m2) subjects of Bai Ku Yao were randomly selected from our previous stratified randomized cluster samples. Genotypes of rs2066715, rs1044925, low density lipoprotein receptor (LDL-R) Ava||, rs2070895, rs2000813, rs1801133, rs3757354, rs505151, rs2016520, and rs5888 SNPs were determined by polymerase chain reaction and restriction fragment length polymorphism combined with gel electrophoresis, and then confirmed by direct sequencing. The interactions were detected by factorial design covariance analysis. The genotypic and allelic frequencies of rs2070895 and rs505151 were different between normal and high BMI subjects, the genotypic frequency of rs2000813 and allelic frequency of rs3757354 were also different between normal and high BMI subjects (P < 0.01). The levels of total cholesterol (TC), apolipoprotein (Apo) A1 (rs2066715); TC, low-density lipoprotein cholesterol (LDL-C), ApoA1, ApoB, and ApoA1/ApoB (rs2070895); triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), and ApoA1 (rs2000813); TC, HDL-C, LDL-C, ApoA1, and ApoB (rs1801133); HDL-C and ApoA1 (rs3757354) in normal BMI subjects were different among the genotypes (P < 0.01). The levels of LDL-C, ApoB, and ApoA1/ApoB (rs2066715); HDL-C, ApoA1, ApoB, and ApoA1/ApoB (rs2070895); TC, HDL-C, ApoA1, and ApoB (rs2000813); TC, TG, HDL-C, LDL-C, ApoA1, and ApoB (rs1801133); TC, TG, and ApoB (rs3757354); TG (rs505151); TG and ApoA1 and ApoB (rs2016520); and TC, HDL-C, LDL-C, ApoA1, and ApoB (rs5888) in high BMI subjects were also different among the genotypes (P < 0.01). The SNPs of rs2066715 (LDL-C and ApoA1/ApoB); rs2070895 (TC, LDL-C, ApoA1, and ApoB); rs2000813 (ApoB); rs1801133 (TC, TG, and LDL-C); rs3757354 (TC and TG); rs505151 (TG, HDL-C, ApoB, and ApoA1/ApoB); rs2016520 (TG and ApoA1/ApoB); and rs5888 (TG, ApoA1, and ApoB) interacted with high BMI to influence serum lipid levels (P < 0.01). The differences in serum lipid levels between normal and high BMI subjects might partly result from different interactions of several SNPs and high BMI.

Complex genetic architecture of cardiac disease in a wild type inbred strain of Drosophila melanogaster

Natural populations of the fruit fly, Drosophila melanogaster, segregate genetic variation that leads to cardiac disease phenotypes. One nearly isogenic line from a North Carolina peach orchard, WE70, is shown to harbor two genetically distinct heart phenotypes: elevated incidence of arrhythmias, and a dramatically constricted heart diameter in both diastole and systole, with resemblance to restrictive cardiomyopathy in humans. Assuming the source to be rare variants of large effect, we performed Bulked Segregant Analysis using genomic DNA hybridization to Affymetrix chips to detect single feature polymorphisms, but found that the mutant phenotypes are more likely to have a polygenic basis. Further mapping efforts revealed a complex architecture wherein the constricted cardiomyopathy phenotype was observed in individual whole chromosome substitution lines, implying that variants on both major autosomes are sufficient to produce the phenotype. A panel of 170 Recombinant Inbred Lines (RIL) was generated, and a small subset of mutant lines selected, but these each complemented both whole chromosome substitutions, implying a non-additive (epistatic) contribution to the “disease” phenotype. Low coverage whole genome sequencing was also used to attempt to map chromosomal regions contributing to both the cardiomyopathy and arrhythmia, but a polygenic architecture had to be again inferred to be most likely. These results show that an apparently simple rare phenotype can have a complex genetic basis that would be refractory to mapping by deep sequencing in pedigrees. We present this as a cautionary tale regarding assumptions related to attempts to map new disease mutations on the assumption that probands carry a single causal mutation.

Progress in genetic association studies of plasma lipids

PURPOSE OF REVIEW

This review summarizes recently published large-scale efforts elucidating the genetic architecture of lipid levels. A supplemental file with all genetic loci is provided for research purposes and we performed bioinformatic analyses of the genetic variants to give an oversight of involved pathways.

RECENT FINDINGS

In total, 52 genes for HDL cholesterol, 42 genes for LDL cholesterol, 59 genes for total cholesterol, and 39 genes for triglycerides have been identified. Genetic overlap is present between the different traits and similar pathways are involved. Most of the SNPs that were detected in the European studies could be replicated in other ethnicities and these SNPs show the same direction of effect suggesting that the underlying genetic architecture of blood lipids is similar between ethnicities.

SUMMARY

Genetic studies have identified many loci associated with plasma lipids and have provided insight into the underlying mechanisms of lipid homeostasis. Future research is needed to determine whether these loci may be novel targets for lipid-lowering therapy and for reducing cardiovascular disease risk. In addition, the proportion of genetic variance explained by these lipid loci is still limited and new large-scale genetic studies are ongoing to identify additional common and rare variants associated with lipid levels.

Finding the missing heritability in pediatric obesity: the contribution of genome-wide complex trait analysis

Known single-nucleotide polymorphisms (SNPs) explain <2% of the variation in body mass index (BMI) despite the evidence of >50% heritability from twin and family studies, a phenomenon termed ‘missing heritability'. Using DNA alone for unrelated individuals, a novel method (in a software package called Genome-wide Complex Trait Analysis, GCTA) estimates the total additive genetic influence due to common SNPs on whole-genome arrays. GCTA has made major inroads into explaining the ‘missing heritability' of BMI in adults. This study provides the first GCTA estimate of genetic influence on adiposity in children. Participants were from the Twins Early Development Study (TEDS), a British twin birth cohort. BMI s.d. scores (BMI-SDS) were obtained from validated parent-reported anthropometric measures when children were about 10 years old (mean=9.9; s.d.=0.84). Selecting one child per family (n=2269), GCTA results from 1.7 million DNA markers were used to quantify the additive genetic influence of common SNPs. For direct comparison, a standard twin analysis in the same families estimated the additive genetic influence as 82% (95% CI: 0.74–0.88, P<0.001). GCTA explained 30% of the variance in BMI-SDS (95% CI: 0.02–0.59; P=0.02). These results indicate that 37% of the twin-estimated heritability (30/82%) can be explained by additive effects of multiple common SNPs, and provide compelling evidence for strong genetic influence on adiposity in childhood.

Rare genomic structural variants in complex disease: lessons from the replication of associations with obesity

The limited ability of common variants to account for the genetic contribution to complex disease has prompted searches for rare variants of large effect, to partly explain the 'missing heritability'. Analyses of genome-wide genotyping data have identified genomic structural variants (GSVs) as a source of such rare causal variants. Recent studies have reported multiple GSV loci associated with risk of obesity. We attempted to replicate these associations by similar analysis of two familial-obesity case-control cohorts and a population cohort, and detected GSVs at 11 out of 18 loci, at frequencies similar to those previously reported. Based on their reported frequencies and effect sizes (OR≥25), we had sufficient statistical power to detect the large majority (80%) of genuine associations at these loci. However, only one obesity association was replicated. Deletion of a 220 kb region on chromosome 16p11.2 has a carrier population frequency of 2×10(-4) (95% confidence interval [9.6×10(-5)-3.1×10(-4)]); accounts overall for 0.5% [0.19%-0.82%] of severe childhood obesity cases (P = 3.8×10(-10); odds ratio = 25.0 [9.9-60.6]); and results in a mean body mass index (BMI) increase of 5.8 kg.m(-2) [1.8-10.3] in adults from the general population. We also attempted replication using BMI as a quantitative trait in our population cohort; associations with BMI at or near nominal significance were detected at two further loci near KIF2B and within FOXP2, but these did not survive correction for multiple testing. These findings emphasise several issues of importance when conducting rare GSV association, including the need for careful cohort selection and replication strategy, accurate GSV identification, and appropriate correction for multiple testing and/or control of false discovery rate. Moreover, they highlight the potential difficulty in replicating rare CNV associations across different populations. Nevertheless, we show that such studies are potentially valuable for the identification of variants making an appreciable contribution to complex disease.

Coordinated genetic scaling of the human eye: shared determination of axial eye length and corneal curvature

PURPOSE

To examine the extent to which the two major determinants of refractive error, corneal curvature and axial length, are scaled relative to one another by shared genetic variants, along with their relationship to the genetic scaling of height.

METHODS

Corneal curvature, axial length, and height were measured in unrelated 14- to 17-year-old white European participants of the Avon Longitudinal Study of Parents and Children (ALSPAC; n = 1915) and in unrelated 40- to 80-year-old participants of the Singapore Chinese Eye Study (SCES; n = 1642). Univariate and bivariate heritability analyses were performed with methods that avoid confounding by common family environment, using information solely from genome-wide high-density genotypes.

RESULTS

IN ALSPAC SUBJECTS, AXIAL LENGTH, CORNEAL CURVATURE, AND HEIGHT HAD SIMILAR LOWER-BOUND HERITABILITY ESTIMATES: axial length, h(2) = 0.46 (SE = 0.16, P = 0.002); corneal curvature, h(2) = 0.42 (SE = 0.16, P = 0.004); height, h(2) = 0.48 (SE = 0.17, P = 0.002). The corresponding estimates in the SCES were 0.79 (SE = 0.18, P < 0.001), 0.35 (SE = 0.20, P = 0.036), and 0.31 (SE = 0.20, P = 0.061), respectively. The genetic correlation between corneal curvature and axial length was 0.69 (SE = 0.17, P = 0.019) for ALSPAC participants and 0.64 (SE = 0.22, P = 0.003) for SCES participants. In the subset of 1478 emmetropic ALSPAC individuals, the genetic correlation was 0.85 (SE = 0.12, P = 0.008).

CONCLUSIONS

These results imply that coordinated scaling of ocular component dimensions is largely achieved by hundreds to thousands of common genetic variants, each with a small pleiotropic effect. Furthermore, genome-wide association studies (GWAS) for either axial length or corneal curvature are likely to identify variants controlling overall eye size when using discovery cohorts dominated by emmetropes, but trait-specific variants in discovery cohorts dominated by ametropes.

Projecting the performance of risk prediction based on polygenic analyses of genome-wide association studies

We report a new model to project the predictive performance of polygenic models based on the number and distribution of effect sizes for the underlying susceptibility alleles and the size of the training dataset. Using estimates of effect-size distribution and heritability derived from current studies, we project that while 45% of the variance of height has been attributed to common tagging Single Nucleotide Polymorphisms (SNP), a model trained on one million people may only explain 33.4% of variance of the trait. Current studies can identify 3.0%, 1.1%, and 7.0%, of the populations who are at two-fold or higher than average risk for Type 2 diabetes, coronary artery disease and prostate cancer, respectively. Tripling of sample sizes could elevate the percentages to 18.8%, 6.1%, and 12.2%, respectively. The utility of future polygenic models will depend on achievable sample sizes, underlying genetic architecture and information on other risk-factors, including family history.

An overview of the genomics of metabolic syndrome

PURPOSE

This article provides a brief overview of the diagnostic criteria and genomic risk factors for the components of metabolic syndrome (MetS).

ORGANIZING CONSTRUCTS

Contributions of cardiovascular, obesity, and diabetes genomic risk factors to the development of MetS as reported in the literature have been reviewed.

FINDINGS

The genomic risk factors for the development of MetS are strongly linked to the genomic risk factors that make up the components of the disease. Many of the cardiovascular and renal genomic risk factors for MetS development are similar to those found in the development of hypertension and dyslipidemia. Obesity may act as a master trigger to turn on the gene expression changes necessary for the other components of the disease. Studies in the genomics of type 2 diabetes show a number of overlapping genes and polymorphisms that influence both the development of diabetes and MetS.

CONCLUSIONS

Although health practitioners now have some insights into the genomics of risk factors associated with MetS, the overall understanding of MetS remains inadequate. Clinical applications based on some of the discussed genomic risk factors are being developed but are not yet available for the diagnosis and treatment of MetS.

CLINICAL RELEVANCE

A broad knowledge of the genomic contributions to disease processes will enable the clinician to better utilize genomics to assess and tailor management of patients.

Heritability of the metabolic syndrome and its components in the Tehran Lipid and Glucose Study (TLGS)

Growing evidence suggests that metabolic syndrome (MetS) has both genetic and environmental bases. We estimated the heritability of the MetS and its components in the families from the Tehran Lipid and Glucose Study (TLGS). We investigated 904 nuclear families in TLGS with two biological parents and at least one offspring (1565 parents and 2448 children), aged 3–90 years, for whom MetS information was available and had at least two members of family with MetS. Variance component methods were used to estimate age and sex adjusted heritability of metabolic syndrome score (MSS) and MetS components using SOLAR software. The heritability of waist circumference (WC), HDL-cholesterol (HDL-C), triglycerides (TGs), fasting blood sugar (FBS), systolic blood pressure (SBP) and diastolic blood pressure (DBP) as continuous traits after adjusting for age and gender were 27, 46, 36, 29, 25, 26 and 15%, respectively, and MSS had a heritability of 15%. When MetS components were analysed as discrete traits, the estimates of age and gender adjusted heritability for MetS, abdominal obesity, low HDL-C, high TG, high FBS and high blood pressure (BP) were 22, 40, 34, 38 and 23%, respectively (P < 0·05). Three factors were extracted from the six continuous traits of the MetS including factor I (BP), factor II (lipids) and factor III (obesity and FBS). Heritability estimation for these three factors were 7, 13 (P < 0·05) and 2%, respectively. The highest heritability was for HDL-C and TG. The results strongly encourage efforts to identify the underlying susceptibility genes.

Accuracy of across-environment genome-wide prediction in maize nested association mapping populations

Most of previous empirical studies with genome-wide prediction were focused on within-environment prediction based on a single-environment (SE) model. In this study, we evaluated accuracy improvements of across-environment prediction by using genetic and residual covariance across correlated environments. Predictions with a multienvironment (ME) model were evaluated for two corn polygenic leaf structure traits, leaf length and leaf width, based on within-population (WP) and across-population (AP) experiments using a large maize nested association mapping data set consisting of 25 populations of recombinant inbred-lines. To make our study more applicable to plant breeding, two cross-validation schemes were used by evaluating accuracies of (CV1) predicting unobserved phenotypes of untested lines and (CV2) predicting unobserved phenotypes of lines that have been evaluated in some environments but not others. We concluded that (1) genome-wide prediction provided greater prediction accuracies than traditional quantitative trait loci-based prediction in both WP and AP and provided more advantages over quantitative trait loci -based prediction for WP than for AP. (2) Prediction accuracy with ME was significantly greater than that attained by SE in CV1 and CV2, and gains with ME over SE were greater in CV2 than in CV1. These gains were also greater in WP than in AP in both CV1 and CV2. (3) Gains with ME over SE attributed to genetic correlation between environments, with little effect from residual correlation. Impacts of marker density on predictions also were investigated in this study.

A comprehensive genetic approach for improving prediction of skin cancer risk in humans

Prediction of genetic risk for disease is needed for preventive and personalized medicine. Genome-wide association studies have found unprecedented numbers of variants associated with complex human traits and diseases. However, these variants explain only a small proportion of genetic risk. Mounting evidence suggests that many traits, relevant to public health, are affected by large numbers of small-effect genes and that prediction of genetic risk to those traits and diseases could be improved by incorporating large numbers of markers into whole-genome prediction (WGP) models. We developed a WGP model incorporating thousands of markers for prediction of skin cancer risk in humans. We also considered other ways of incorporating genetic information into prediction models, such as family history or ancestry (using principal components, PCs, of informative markers). Prediction accuracy was evaluated using the area under the receiver operating characteristic curve (AUC) estimated in a cross-validation. Incorporation of genetic information (i.e., familial relationships, PCs, or WGP) yielded a significant increase in prediction accuracy: from an AUC of 0.53 for a baseline model that accounted for nongenetic covariates to AUCs of 0.58 (pedigree), 0.62 (PCs), and 0.64 (WGP). In summary, prediction of skin cancer risk could be improved by considering genetic information and using a large number of single-nucleotide polymorphisms (SNPs) in a WGP model, which allows for the detection of patterns of genetic risk that are above and beyond those that can be captured using family history. We discuss avenues for improving prediction accuracy and speculate on the possible use of WGP to prospectively identify individuals at high risk.

Genetic heritability of ischemic stroke and the contribution of previously reported candidate gene and genomewide associations

BACKGROUND AND PURPOSE

The contribution of genetics to stroke risk, and whether this differs for different stroke subtypes, remainsuncertain. Genomewide complex trait analysis allows heritability to be assessed from genomewide association study (GWAS) data. Previous candidate gene studies have identified many associations with stoke but whether these are important requires replication in large independent data sets. GWAS data sets provide a powerful resource to perform replication studies.

METHODS

We applied genomewide complex trait analysis to a GWAS data set of 3752 ischemic strokes and 5972 controls and determined heritability for all ischemic stroke and the most common subtypes: large-vessel disease, small-vessel disease, and cardioembolic stroke. By systematic review we identified previous candidate gene and GWAS associations with stroke and previous GWAS associations with related cardiovascular phenotypes (myocardial infarction, atrial fibrillation, and carotid intima-media thickness). Fifty associations were identified.

RESULTS

For all ischemic stroke, heritability was 37.9%. Heritability varied markedly by stroke subtype being 40.3% for large-vessel disease and 32.6% for cardioembolic but lower for small-vessel disease (16.1%). No previously reported candidate gene was significant after rigorous correction for multiple testing. In contrast, 3 loci from related cardiovascular GWAS studies were significant: PHACTR1 in large-vessel disease (P=2.63e(-6)), PITX2 in cardioembolic stroke (P=4.78e(-8)), and ZFHX3 in cardioembolic stroke (P=5.50e(-7)).

CONCLUSIONS

There is substantial heritability for ischemic stroke, but this varies for different stroke subtypes. Previous candidate gene associations contribute little to this heritability, but GWAS studies in related cardiovascular phenotypes are identifying robust associations. The heritability data, and data from GWAS, suggest detecting additional associations will depend on careful stroke subtyping.