Estimation and partition of heritability in human populations using whole-genome analysis methods

Interleukin 6 polymorphisms are associated with cardiovascular risk factors in premature coronary artery disease patients and healthy controls of the GEA Mexican study

BACKGROUND AND AIMS

Interleukin-6 (IL-6) is an acute-phase protein that plays an important role in the inflammatory response, vascular inflammation, and atherosclerosis process. The study aimed to establish whether IL-6 gene polymorphisms and IL-6 concentrations are associated with premature coronary artery disease (pCAD) and cardiovascular risk factors.

METHODS

The IL-6 concentrations and the rs2069827, rs1800796, and rs1800795 IL-6 polymorphisms were determined in 1150 pCAD patients and 1083 healthy controls (coronary artery calcium equal to zero determined by tomography).

RESULTS

The IL-6 polymorphisms studied were not associated with pCAD, but they were associated with cardiovascular risk factors in patients and controls. In controls, under the dominant model, the rs1800795 C allele and the rs2069827 T allele were associated with a low risk of central obesity (OR = 0.401, p = 0.017 and OR = 0.577, p = 0.031, respectively), hypoalphalipoproteinemia (OR = 0.581, p = 0.027 and OR = 0.700, p = 0.014, respectively) and hypertriglyceridemia (OR = 0.575, p = 0.030 and OR = 0.728, p = 0.033, respectively). In pCAD, the rs1800795 C allele was associated with an increased risk of hypoalphalipoproteinemia (OR = 1.370, padditive = 0.025) and increased C-reactive protein (CRP) concentrations (OR = 1.491, padditive = 0.007). pCAD patients had significantly higher serum IL-6 concentrations compared to controls (p = 0.002). In the total population, individuals carrying the rs1800795 GC + CC genotypes had higher levels of IL-6 than carriers of the GG genotype (p = 0.025). In control individuals carrying the C allele (CG + CC), an inverse correlation was observed between IL-6 and HDL-cholesterol levels (p = 0.003).

CONCLUSIONS

In summary, the IL-6 polymorphisms were not associated with pCAD, however, they were associated with cardiovascular risk factors in pCAD patients and healthy controls. Individuals carrying the rs1800795 GC + CC genotypes had higher levels of IL-6 than carriers of the GG genotype.

Molecular diagnosis is an important indicator for response to growth hormone therapy in children with short stature

BACKGROUND

Significant differences have been observed in the efficacy of recombinant human growth hormone (rhGH) treatment for short children. The present study aimed to identify the genetic etiology of short stature and to assess the role of molecular diagnosis in predicting responses to rhGH treatment.

METHODS

A total of 407 short children were included in the present study, 226 of whom received rhGH treatment. Whole-exome sequencing (WES) was conducted on short children to identify the underlying genetic etiology. Correlations between molecular diagnosis and the efficacy of rhGH treatment were examined.

RESULTS

Pathogenic or likely pathogenic mutations were identified in 86 of the 407 patients (21.1%), including 36 (41.9%) novel variants. Among the multiple pathways affecting short stature, genes involved in fundamental cellular processes (38.7%) play a larger role, especially the RAS-MAPK pathway. In general, patients without pathogenic mutations responded better to rhGH than those with mutations. Furthermore, patients with hormone signaling pathway mutations had a better response to rhGH, while those with paracrine factor mutations had a worse response to rhGH.

CONCLUSIONS

This study highlights the utility of WES in identifying genetic etiology in children with short stature. Identifying likely causal mutations is an important factor in predicting rhGH response.

Association of air pollution exposure and increased coronary artery disease risk: the modifying effect of genetic susceptibility

BACKGROUND

Both genetic factors and air pollution are risk factors for coronary artery disease (CAD), but their combined effects on CAD are uncertain. The study aimed to comprehensively investigate their separate, combined and interaction effects on the onset of CAD.

METHODS

We utilized data from the UK Biobank with a recruitment of 487,507 participants who were free of CAD at baseline from 2006 to 2010. We explored the separate, combined effect or interaction association among genetic factors, air pollution and CAD with the polygenic risk score (PRS) and Cox proportional hazard models.

RESULTS

The hazard ratios (HRs) [95% confidence interval (CI)] of CAD for 10-µg/m3 increases in PM2.5, NO2 and NOx concentrations were 1.25 (1.09, 1.44), 1.03 (1.01, 1.05) and 1.01 (1.00, 1.02), respectively. Participants with high PRS and air pollution exposure had a higher risk of CAD than those with the low genetic risk and low air pollution exposure, and the HRs (95% CI) of CAD in the PM2.5, PM10, NO2 and NOx high joint exposure groups were 1.56 (1.48, 1.64), 1.55(1.48, 1.63), 1.57 (1.49, 1.65), and 1.57 (1.49, 1.65), respectively. Air pollution and genetic factors exerted significant additive effects on the development of CAD (relative excess risk due to the interaction [RERI]: 0.12 (0.05, 0.19) for PM2.5, 0.17 (0.10, 0.24) for PM10, 0.14 (0.07, 0.21) for NO2, and 0.17 (0.10, 0.24) for NOx; attributable proportion due to the interaction [AP]: 0.09 (0.04, 0.14) for PM2.5, 0.12 (0.07, 0.18) for PM10, 0.11 (0.06, 0.16) for NO2, and 0.13 (0.08, 0.18) for NOx).

CONCLUSION

Exposure to air pollution was significantly related to an increased CAD risk, which could be further strengthened by CAD gene susceptibility. Additionally, there were positive additive interactions between genetic factors and air pollution on the onset of CAD. This can provide a more comprehensive, precise and individualized scientific basis for the risk assessment, prevention and control of CAD.

Clinical and genetic evaluation of children with short stature of unknown origin

BACKGROUND

Short stature is a common human trait. More severe and/or associated short stature is usually part of the presentation of a syndrome and may be a monogenic disease. The present study aimed to identify the genetic etiology of children with short stature of unknown origin.

METHODS

A total of 232 children with short stature of unknown origin from March 2013 to May 2020 were enrolled in this study. Whole exome sequencing (WES) was performed for the enrolled patients to determine the underlying genetic etiology.

RESULTS

We identified pathogenic or likely pathogenic genetic variants in 18 (7.8%) patients. All of these variants were located in genes known to be associated with growth disorders. Five of the genes are associated with paracrine signaling or cartilage extracellular matrix in the growth plate, including NPR2 (N = 1), ACAN (N = 1), CASR (N = 1), COMP (N = 1) and FBN1 (N = 1). Two of the genes are involved in the RAS/MAPK pathway, namely, PTPN11 (N = 6) and NF1 (N = 1). Two genes are associated with the abnormal growth hormone-insulin-like growth factor 1 (GH-IGF1) axis, including GH1 (N = 1) and IGF1R (N = 1). Two mutations are located in PROKR2, which is associated with gonadotropin-releasing hormone deficiency. Mutations were found in the remaining two patients in genes with miscellaneous mechanisms: ANKRD11 (N = 1) and ARID1A (N = 1).

CONCLUSIONS

The present study identified pathogenic or likely pathogenic genetic variants in eighteen of the 232 patients (7.8%) with short stature of unknown origin. Our findings suggest that in the absence of prominent malformation, genetic defects in hormones, paracrine factors, and matrix molecules may be the causal factors for this group of patients. Early genetic testing is necessary for accurate diagnosis and precision treatment.

On asymptotic distributions of several test statistics for familial relatedness in linear mixed models

In this study, the asymptotic distributions of the likelihood ratio test (LRT), the restricted likelihood ratio test (RLRT), the F and the sequence kernel association test (SKAT) statistics for testing an additive effect of the expected familial relatedness (FR) in a linear mixed model are examined based on an eigenvalue approach. First, the covariance structure for modeling the FR effect in a LMM is presented. Then, the multiplicity of eigenvalues for the log-likelihood and restricted log-likelihood is established under a replicate family setting and extended to a more general replicate family setting (GRFS) as well. After that, the asymptotic null distributions of LRT, RLRT, F and SKAT statistics under GRFS are derived. The asymptotic null distribution of SKAT for testing genetic rare variants is also constructed. In addition, a simple formula for sample size calculation is provided based on the restricted maximum likelihood estimate of the effect size for the expected FR. Finally, a power comparison of these test statistics on hypothesis test of the expected FR effect is made via simulation. The four test statistics are also applied to a data set from the UK Biobank.

Chromosome Y Haplogroup R Was Associated with the Risk of Premature Myocardial Infarction with ST-Elevation: Data from the CholeSTEMI Registry

Cardiovascular disease (CVD) is the leading cause of death worldwide, with coronary artery disease (CAD) being one of its main manifestations. Both environmental and genetic factors are widely known to be related to CAD, such as smoking, diabetes mellitus, dyslipidemia, and a family history of CAD. However, there is still a lack of information about other risk factors, especially those related to genetic mutations. Sex represents a classic CAD risk factor, as men are more likely to suffer CAD, but there is lack of evidence with regard to sex-specific genetic factors. We evaluated the Y chromosome haplogroups in a cohort of young Spanish male patients who suffered from STEMI. In this cohort, haplogroup R was significantly more frequent in STEMI patients.

Periostin in Osteoporosis and Cardiovascular Disease

Context

Osteoporosis (OP) and cardiovascular disease (CVD), prevalent disorders worldwide, often coexist and share common risk factors. The identification of common biomarkers could significantly improve patients' preventive care.

Objectives

The objectives are 1, to review periostin (Postn) involvement in osteoporosis and in CVD, and 2, identify if Postn could be a common biomarker.

Design

This is a scoping review on Postn in OP and CVD.

Methods

Databases were searched, in vitro and in vivo, for publications in English on Postn, bone, and the cardiovascular system, with no limit regarding publication date.

Results

Postn appears as a key factor in OP and CVD. Its role as a potential biomarker in both pathologies is described in recent studies, but a number of limitations have been identified.

Conclusions

Current evidence provides fragmented views on Postn in OP and CVD and does not encapsulate Postn as a common pivotal thread linking these comorbidities. A number of gaps impede highlighting Postn as a common biomarker. There is room for future basic and clinical research with Postn as a marker and a target to provide new therapeutic options for aging patients with concomitant OP and CVD.

Role of non-coding variants in cardiovascular disease

Cardiovascular diseases (CVDs) constitute one of the significant causes of death worldwide. Different pathological states are linked to CVDs, which despite interventions and treatments, still have poor prognoses. The genetic component, as a beneficial tool in the risk stratification of CVD development, plays a role in the pathogenesis of this group of diseases. The emergence of genome-wide association studies (GWAS) have led to the identification of non-coding parts associated with cardiovascular traits and disorders. Variants located in functional non-coding regions, including promoters/enhancers, introns, miRNAs and 5'/3' UTRs, account for 90% of all identified single-nucleotide polymorphisms associated with CVDs. Here, for the first time, we conducted a comprehensive review on the reported non-coding variants for different CVDs, including hypercholesterolemia, cardiomyopathies, congenital heart diseases, thoracic aortic aneurysms/dissections and coronary artery diseases. Additionally, we present the most commonly reported genes involved in each CVD. In total, 1469 non-coding variants constitute most reports on familial hypercholesterolemia, hypertrophic cardiomyopathy and dilated cardiomyopathy. The application and identification of non-coding variants are beneficial for the genetic diagnosis and better therapeutic management of CVDs.

Which mouse multiparental population is right for your study? The Collaborative Cross inbred strains, their F1 hybrids, or the Diversity Outbred population

Multiparental populations (MPPs) encompass greater genetic diversity than traditional experimental crosses of two inbred strains, enabling broader surveys of genetic variation underlying complex traits. Two such mouse MPPs are the Collaborative Cross (CC) inbred panel and the Diversity Outbred (DO) population, which are descended from the same eight inbred strains. Additionally, the F1 intercrosses of CC strains (CC-RIX) have been used and enable study designs with replicate outbred mice. Genetic analyses commonly used by researchers to investigate complex traits in these populations include characterizing how heritable a trait is, i.e. its heritability, and mapping its underlying genetic loci, i.e. its quantitative trait loci (QTLs). Here we evaluate the relative merits of these populations for these tasks through simulation, as well as provide recommendations for performing the quantitative genetic analyses. We find that sample populations that include replicate animals, as possible with the CC and CC-RIX, provide more efficient and precise estimates of heritability. We report QTL mapping power curves for the CC, CC-RIX, and DO across a range of QTL effect sizes and polygenic backgrounds for samples of 174 and 500 mice. The utility of replicate animals in the CC and CC-RIX for mapping QTLs rapidly decreased as traits became more polygenic. Only large sample populations of 500 DO mice were well-powered to detect smaller effect loci (7.5-10%) for highly complex traits (80% polygenic background). All results were generated with our R package musppr, which we developed to simulate data from these MPPs and evaluate genetic analyses from user-provided genotypes.

Polygenic risk scores for the prediction of cardiometabolic disease

Cardiometabolic diseases contribute more to global morbidity and mortality than any other group of disorders. Polygenic risk scores (PRSs), the weighted summation of individually small-effect genetic variants, represent an advance in our ability to predict the development and complications of cardiometabolic diseases. This article reviews the evidence supporting the use of PRS in seven common cardiometabolic diseases: coronary artery disease (CAD), stroke, hypertension, heart failure and cardiomyopathies, obesity, atrial fibrillation (AF), and type 2 diabetes mellitus (T2DM). Data suggest that PRS for CAD, AF, and T2DM consistently improves prediction when incorporated into existing clinical risk tools. In other areas such as ischaemic stroke and hypertension, clinical application appears premature but emerging evidence suggests that the study of larger and more diverse populations coupled with more granular phenotyping will propel the translation of PRS into practical clinical prediction tools.

Associations of genetic and infectious risk factors with coronary heart disease

Coronary heart disease (CHD) is one of the most pressing health problems of our time and a major cause of preventable death. CHD results from complex interactions between genetic and environmental factors. Using multiplex serological testing for persistent or frequently recurring infections and genome-wide analysis in a prospective population study, we delineate the respective and combined influences of genetic variation, infections, and low-grade inflammation on the risk of incident CHD. Study participants are enrolled in the CoLaus|PsyCoLaus study, a longitudinal, population-based cohort with baseline assessments from 2003 through 2008 and follow-up visits every 5 years. We analyzed a subgroup of 3459 individuals with available genome-wide genotyping data and immunoglobulin G levels for 22 persistent or frequently recurring pathogens. All reported CHD events were evaluated by a panel of specialists. We identified independent associations with incident CHD using univariable and multivariable stepwise Cox proportional hazards regression analyses. Of the 3459 study participants, 210 (6.07%) had at least one CHD event during the 12 years of follow-up. Multivariable stepwise Cox regression analysis, adjusted for known cardiovascular risk factors, socioeconomic status, and statin intake, revealed that high polygenic risk (hazard ratio [HR] 1.31, 95% CI 1.10-1.56, p=2.64 × 10^-3) and infection with Fusobacterium nucleatum (HR 1.63, 95% CI 1.08-2.45, p=1.99 × 10^-2) were independently associated with incident CHD. In a prospective, population-based cohort, high polygenic risk and infection with F. nucleatum have a small, yet independent impact on CHD risk.

Taking quantitative genomics into the wild

We organized this special issue to highlight new work and review recent advances at the cutting edge of 'wild quantitative genomics'. In this editorial, we will present some history of wild quantitative genetic and genomic studies, before discussing the main themes in the papers published in this special issue and highlighting the future outlook of this dynamic field.

Machine learning of large-scale multimodal brain imaging data reveals neural correlates of hand preference

Lateralization is a fundamental characteristic of many behaviors and the organization of the brain, and atypical lateralization has been suggested to be linked to various brain-related disorders such as autism and schizophrenia. Right-handedness is one of the most prominent markers of human behavioural lateralization, yet its neurobiological basis remains to be determined. Here, we present a large-scale analysis of handedness, as measured by self-reported direction of hand preference, and its variability related to brain structural and functional organization in the UK Biobank (N = 36,024). A multivariate machine learning approach with multi-modalities of brain imaging data was adopted, to reveal how well brain imaging features could predict individual's handedness (i.e., right-handedness vs. non-right-handedness) and further identify the top brain signatures that contributed to the prediction. Overall, the results showed a good prediction performance, with an area under the receiver operating characteristic curve (AUROC) score of up to 0.72, driven largely by resting-state functional measures. Virtual lesion analysis and large-scale decoding analysis suggested that the brain networks with the highest importance in the prediction showed functional relevance to hand movement and several higher-level cognitive functions including language, arithmetic, and social interaction. Genetic analyses of contributions of common DNA polymorphisms to the imaging-derived handedness prediction score showed a significant heritability (h²=7.55%, p <0.001) that was similar to and slightly higher than that for the behavioural measure itself (h²=6.74%, p <0.001). The genetic correlation between the two was high (r_g=0.71), suggesting that the imaging-derived score could be used as a surrogate in genetic studies where the behavioural measure is not available. This large-scale study using multimodal brain imaging and multivariate machine learning has shed new light on the neural correlates of human handedness.

Coronary artery disease and cancer: a significant resemblance

Cancer and coronary artery disease (CAD) are two of the most common causes of death, and they frequently coexist, especially as the world's population ages. CAD can develop prior to or following cancer diagnosis, as well as a side effect of cancer treatment. CAD develops as complex interactions of lifestyle and hereditary variables, just like the development of the most complex and non-communicable diseases. Cancer is caused by both external/acquired factors (tobacco, food, physical activity, alcohol consumption, epigenetic alterations) and internal/inherited factors (genetic mutations, hormones, and immunological diseases). The Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-associated protein 9 (Cas9) system has recently emerged as a strong tool for gene therapy for both cancer as well as CAD treatment due to its great accuracy and efficiency. A deeper understanding of the complex link between CAD and cancer should lead to better prevention, faster detection, and safer treatment strategies.

A cognitive neurogenetic approach to uncovering the structure of executive functions

One central mission of cognitive neuroscience is to understand the ontology of complex cognitive functions. We addressed this question with a cognitive neurogenetic approach using a large-scale dataset of executive functions (EFs), whole-brain resting-state functional connectivity, and genetic polymorphisms. We found that the bifactor model with common and shifting-specific components not only was parsimonious but also showed maximal dissociations among the EF components at behavioral, neural, and genetic levels. In particular, the genes with enhanced expression in the middle frontal gyrus (MFG) and the subcallosal cingulate gyrus (SCG) showed enrichment for the common and shifting-specific component, respectively. Finally, High-dimensional mediation models further revealed that the functional connectivity patterns significantly mediated the genetic effect on the common EF component. Our study not only reveals insights into the ontology of EFs and their neurogenetic basis, but also provides useful tools to uncover the structure of complex constructs of human cognition.

Preliminary genome wide screening identifies new variants associated with coronary artery disease in Indian population

AIM

Coronary artery disease (CAD) is a major health problem in developed and developing nations. Development of CAD involves a complex interaction between genetics and lifestyle factors. Individuals with high-risk genetic predisposition along with poor lifestyle are more inclined to the development of CAD. Advancement in genotyping technologies and increase in genome wide studies has provided a platform to identify new risk factors associated with CAD and associated complexities.

METHODOLOGY

In this study we performed genome wide screening in 76 well-defined CAD cases and 77 control samples in Indian population. Interestingly, new variants are identified in three genes viz, VLDLR, IFITM2 and C2CD4C.

RESULTS

The odds ratios observed for variant rs1869592 (VLDLR), rs1059091 (IFITMI) and rs7247159 (C2CD4C) were 2.6 (1.4-4.8 95% CI), 1.9 (95% CI 1.2-3.1) and 2.1 (1.2-3.7 95% CI), respectively with significant P value <0.01. These variants that are associated with pathogenesis of CAD were not previously reported in literature. Moreover, we anticipate that these variants will be further validated using a larger sample size.

Genetic Specificity of Hippocampal Subfield Volumes, Relative to Hippocampal Formation, Identified in 2148 Young Adult Twins and Siblings

The hippocampus is a complex brain structure with key roles in cognitive and emotional processing and with subregion abnormalities associated with a range of disorders and psychopathologies. Here we combine data from two large independent young adult twin/sibling cohorts to obtain the most accurate estimates to date of genetic covariation between hippocampal subfield volumes and the hippocampus as a single volume. The combined sample included 2148 individuals, comprising 1073 individuals from 627 families (mean age = 22.3 years) from the Queensland Twin IMaging (QTIM) Study, and 1075 individuals from 454 families (mean age = 28.8 years) from the Human Connectome Project (HCP). Hippocampal subfields were segmented using FreeSurfer version 6.0 (CA4 and dentate gyrus were phenotypically and genetically indistinguishable and were summed to a single volume). Multivariate twin modeling was conducted in OpenMx to decompose variance into genetic and environmental sources. Bivariate analyses of hippocampal formation and each subfield volume showed that 10%-72% of subfield genetic variance was independent of the hippocampal formation, with greatest specificity found for the smaller volumes; for example, CA2/3 with 42% of genetic variance being independent of the hippocampus; fissure (63%); fimbria (72%); hippocampus-amygdala transition area (41%); parasubiculum (62%). In terms of genetic influence, whole hippocampal volume is a good proxy for the largest hippocampal subfields, but a poor substitute for the smaller subfields. Additive genetic sources accounted for 49%-77% of total variance for each of the subfields in the combined sample multivariate analysis. In addition, the multivariate analyses were sufficiently powered to identify common environmental influences (replicated in QTIM and HCP for the molecular layer and CA4/dentate gyrus, and accounting for 7%-16% of total variance for 8 of 10 subfields in the combined sample). This provides the clearest indication yet from a twin study that factors such as home environment may influence hippocampal volumes (albeit, with caveats).

Unfolding the genotype-to-phenotype black box of cardiovascular diseases through cross-scale modeling

Complex traits such as cardiovascular diseases (CVD) are the results of complicated processes jointly affected by genetic and environmental factors. Genome-wide association studies (GWAS) identified genetic variants associated with diseases but usually did not reveal the underlying mechanisms. There could be many intermediate steps at epigenetic, transcriptomic, and cellular scales inside the black box of genotype-phenotype associations. In this article, we present a machine-learning-based cross-scale framework GRPath to decipher putative causal paths (pcPaths) from genetic variants to disease phenotypes by integrating multiple omics data. Applying GRPath on CVD, we identified 646 and 549 pcPaths linking putative causal regions, variants, and gene expressions in specific cell types for two types of heart failure, respectively. The findings suggest new understandings of coronary heart disease. Our work promoted the modeling of tissue- and cell type-specific cross-scale regulation to uncover mechanisms behind disease-associated variants, and provided new findings on the molecular mechanisms of CVD.

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We defined one type of cross-scale genotype-to-phenotype regulation path

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We designed a framework GRPath to uncover putative regulation paths for diseases

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GRPath helped uncover molecular mechanisms for two major types of heart failure

A review of SNP heritability estimation methods

Over the past decade, statistical methods have been developed to estimate single nucleotide polymorphism (SNP) heritability, which measures the proportion of phenotypic variance explained by all measured SNPs in the data. Estimates of SNP heritability measure the degree to which the available genetic variants influence phenotypes and improve our understanding of the genetic architecture of complex phenotypes. In this article, we review the recently developed and commonly used SNP heritability estimation methods for continuous and binary phenotypes from the perspective of model assumptions and parameter optimization. We primarily focus on their capacity to handle multiple phenotypes and longitudinal measurements, their ability for SNP heritability partition and their use of individual-level data versus summary statistics. State-of-the-art statistical methods that are scalable to the UK Biobank dataset are also elucidated in detail.

Investigating how the accuracy of teacher expectations of pupil performance relate to socioeconomic and genetic factors

Teacher expectations of pupil ability can influence educational progression, impacting subsequent streaming and exam level. Systematic discrepancies between teacher expectations of pupil achievement may therefore have a detrimental effect on children's education. Associations between socioeconomic and demographic factors with teacher expectation accuracy have been demonstrated, but it is not known how teacher expectations of achievement may relate to genetic factors. We investigated these relationships using nationally standardized exam results at ages 11 and 14 from a UK longitudinal cohort study. We found that teacher expectation of achievement was strongly correlated with educational test scores. Furthermore, the accuracy of teacher expectation was patterned by pupil socioeconomic background but not teacher characteristics. The accuracy of teacher expectation related to pupil's genetic liability to education as captured by a polygenic score for educational attainment. Despite correlation with the polygenic score, we found no strong evidence for genomewide SNP heritability in teacher reporting accuracy.

A Novel Gene CDC27 Causes SLE and Is Associated With the Disease Activity

Background

As genetic genetic factors are important in SLE, so screening causative genes is of great significance for the prediction and early prevention in people who may develop SLE. At present, it is very difficult to screen causative genes through pedigrees. The analytical method described herein can be used to screen causative genes for SLE and other complex diseases through pedigrees.

Methods

For the first time, 24 lupus pedigrees were analyzed by combining whole exon sequencing and a variety of biological information tools including common-specific analysis, pVAAST (pedigree variant annotation, analysis and search tool), Exomiser (Combining phenotype and PPI associated analysis), and FARVAT (family based gene burden), and the causative genes of these families with lupus identified. Selected causative genes in peripheral-blood mononuclear cells (PBMCs) were evaluated by quantitative polymerase chain reaction (qPCR).

Results

Cell division cycle 27 (CDC27) was screened out by common-specific analysis and Exomiser causative gene screening. FARVAT analysis on these families detected only CDC27 at the extremely significant level (false discovery rate <0.05) by three family-based burden analyses (BURDEN, CALPHA, and SKATO). QPCR was performed to detect for CDC27 in the PBMCs of the SLE family patients, sporadic lupus patients, and healthy people. Compared with the healthy control group, CDC27 expression was low in lupus patients (familial and sporadic patients) (P<0.05) and correlated with lupus activity indicators: negatively with C-reactive protein (CRP) (P<0.05) and erythrocyte sedimentation rate (P<0.05) and positively with complement C3 and C4 (P<0.05). The CDC27 expression was upregulated in PBMCs from SLE patients with reduced lupus activity after immunotherapy (P<0.05). Based on Receiver operating characteristic (ROC) curve analysis, the sensitivity and specificity of CDC27 in diagnosing SLE were 82.30% and 94.40%.

Conclusion

The CDC27 gene, as found through WES combined with multiple analytical method may be a causative gene of lupus. CDC27 may serve as a marker for the diagnosis of SLE and is closely related to the lupus activity. We hope that the analytical method in this study will be used to screen causative genes for other diseases through small pedigrees, especially among non-close relatives.

Study protocol to quantify the genetic architecture of sonographic cervical length and its relationship to spontaneous preterm birth

INTRODUCTION

A short cervix (cervical length <25 mm) in the midtrimester (18-24 weeks) of pregnancy is a powerful predictor of spontaneous preterm delivery. Although the biological mechanisms of cervical change during pregnancy have been the subject of extensive investigation, little is known about whether genes influence the length of the cervix, or the extent to which genetic factors contribute to premature cervical shortening. Defining the genetic architecture of cervical length is foundational to understanding the aetiology of a short cervix and its contribution to an increased risk of spontaneous preterm delivery.

METHODS/ANALYSIS

The proposed study is designed to characterise the genetic architecture of cervical length and its genetic relationship to gestational age at delivery in a large cohort of Black/African American women, who are at an increased risk of developing a short cervix and delivering preterm. Repeated measurements of cervical length will be modelled as a longitudinal growth curve, with parameters estimating the initial length of the cervix at the beginning of pregnancy, and its rate of change over time. Genome-wide complex trait analysis methods will be used to estimate the heritability of cervical length growth parameters and their bivariate genetic correlation with gestational age at delivery. Polygenic risk profiling will assess maternal genetic risk for developing a short cervix and subsequently delivering preterm and evaluate the role of cervical length in mediating the relationship between maternal genetic variation and gestational age at delivery.

ETHICS/DISSEMINATION

The proposed analyses will be conducted using deidentified data from participants in an IRB-approved study of longitudinal cervical length who provided blood samples and written informed consent for their use in future genetic research. These analyses are preregistered with the Center for Open Science using the AsPredicted format and the results and genomic summary statistics will be published in a peer-reviewed journal.

PSD3 downregulation confers protection against fatty liver disease

Fatty liver disease (FLD) is a growing health issue with burdening unmet clinical needs. FLD has a genetic component but, despite the common variants already identified, there is still a missing heritability component. Using a candidate gene approach, we identify a locus (rs71519934) at the Pleckstrin and Sec7 domain-containing 3 (PSD3) gene resulting in a leucine to threonine substitution at position 186 of the protein (L186T) that reduces susceptibility to the entire spectrum of FLD in individuals at risk. PSD3 downregulation by short interfering RNA reduces intracellular lipid content in primary human hepatocytes cultured in two and three dimensions, and in human and rodent hepatoma cells. Consistent with this, Psd3 downregulation by antisense oligonucleotides in vivo protects against FLD in mice fed a non-alcoholic steatohepatitis-inducing diet. Thus, translating these results to humans, PSD3 downregulation might be a future therapeutic option for treating FLD.

Association of body mass index and its long-term changes with cardiometabolic diseases: A nationwide twin study

BACKGROUND & AIMS

The association between higher body mass index (BMI) and cardiometabolic diseases (CMDs, including type 2 diabetes and cardiovascular diseases) is not well understood. We aimed to examine the association of BMI and its long-term changes with cardiometabolic diseases (CMDs) and explore the role of familial background and healthy lifestyle in this association.

METHODS

Within the Swedish Twin Registry, 36 622 CMD-free individuals aged ≥40 were followed for up to 16 years. BMI data was collected at baseline and 25-35 years prior to baseline. Healthy lifestyle (non-smoking, no/mild alcohol consumption, and regular physical activity) was assessed as unfavourable (none or only one of these factors) vs. favourable (two or three). Incident CMDs were identified by linkage with the Swedish National Patient Registry. Two strategies were followed: 1) Cox models in all twin individuals; 2) stratified Cox models in CMD-discordant twin pairs.

RESULTS

At baseline, 16 195 (44.2%) study participants had overweight/obesity (BMI ≥ 25 kg/m²) and 11 202 (30.6%) developed CMDs over follow-up. Among all participants, the hazard ratio (HR) and 95% confidence interval (CI) of developing any CMD was 1.52 (1.45-1.58) for people with overweight/obesity compared to normal BMI (20-25 kg/m²). Compared to stable normal BMI, HRs (95% CIs) of CMDs were 1.28 (1.02-1.59) and 1.33 (1.24-1.43) for only earlier life or only later life overweight/obesity, respectively, and 1.69 (1.55-1.85) for overweight/obesity both in earlier and later life. In stratified Cox analyses conducted among all CMD-discordant twin pairs, overweight/obesity was associated with greater risk of CMDs (1.37, 95% CI 1.18-1.61). In joint effect analysis, the risk of CMDs related to overweight/obesity was diminished 32% among people with a favourable lifestyle (1.51, 95% CI 1.44-1.58) compared to those with overweight/obesity and an unfavourable lifestyle (2.20, 95% CI 2.03-2.38).

CONCLUSIONS

Overweight/obesity is associated with an increased risk of CMDs, and shared genetic and early-life environmental factors might not account for this association. However, a favourable lifestyle could attenuate the risk of high BMI-related CMDs.

Gene-Environment Interactions for Cardiovascular Disease

PURPOSE OF REVIEW

We provide an overview of recent findings with respect to gene-environment (GxE) interactions for cardiovascular disease (CVD) risk and discuss future opportunities for advancing the field.

RECENT FINDINGS

Over the last several years, GxE interactions for CVD have mostly been identified for smoking and coronary artery disease (CAD) or related risk factors. By comparison, there is more limited evidence for GxE interactions between CVD outcomes and other exposures, such as physical activity, air pollution, diet, and sex. The establishment of large consortia and population-based cohorts, in combination with new computational tools and mouse genetics platforms, can potentially overcome some of the limitations that have hindered human GxE interaction studies and reveal additional association signals for CVD-related traits. The identification of novel GxE interactions is likely to provide a better understanding of the pathogenesis and genetic liability of CVD, with significant implications for healthy lifestyles and therapeutic strategies.

Large-Scale Phenomic and Genomic Analysis of Brain Asymmetrical Skew

The human cerebral hemispheres show a left-right asymmetrical torque pattern, which has been claimed to be absent in chimpanzees. The functional significance and developmental mechanisms are unknown. Here, we carried out the largest-ever analysis of global brain shape asymmetry in magnetic resonance imaging data. Three population datasets were used, UK Biobank (N = 39 678), Human Connectome Project (N = 1113), and BIL&GIN (N = 453). At the population level, there was an anterior and dorsal skew of the right hemisphere, relative to the left. Both skews were associated independently with handedness, and various regional gray and white matter metrics oppositely in the two hemispheres, as well as other variables related to cognitive functions, sociodemographic factors, and physical and mental health. The two skews showed single nucleotide polymorphisms-based heritabilities of 4-13%, but also substantial polygenicity in causal mixture model analysis, and no individually significant loci were found in genome-wide association studies for either skew. There was evidence for a significant genetic correlation between horizontal brain skew and autism, which requires future replication. These results provide the first large-scale description of population-average brain skews and their inter-individual variations, their replicable associations with handedness, and insights into biological and other factors which associate with human brain asymmetry.

The Analytic Identification of Variance Component Models Common to Behavior Genetics

Many behavior genetics models follow the same general structure. We describe this general structure and analytically derive simple criteria for its identification. In particular, we find that variance components can be uniquely estimated whenever the relatedness matrices that define the components are linearly independent (i.e., not confounded). Thus, we emphasize determining which variance components can be identified given a set of genetic and environmental relationships, rather than the estimation procedures. We validate the identification criteria with several well-known models, and further apply them to several less common models. The first model distinguishes child-rearing environment from extended family environment. The second model adds a gene-by-common-environment interaction term in sets of twins reared apart and together. The third model separates measured-genomic relatedness from the scanner site variation in a hypothetical functional magnetic resonance imaging study. The computationally easy analytic identification criteria allow researchers to quickly address model identification issues and define novel variance components, facilitating the development of new research questions.

The Risk Factors of Coronary Heart Disease and its Relationship with Endothelial Nitric Oxide Synthase

BACKGROUND: Coronary heart disease (CHD) is the leading cause of death and start with injury to the endothelium of a coronary artery. The common feature of endothelial dysfunction is a decrease of nitric oxide (NO) bioavailability that regulated by endothelial NO synthase (eNOS) activity. AIM: The aim of our study was to study the relationship between risk factors of CHD patients with the level of eNOS. METHODS: Thirty-seven outpatients in cardiology department of the regional public hospital diagnosed as CHD were included in our study. Thirty healthy individuals were included as the control group. Risk factors of CHD were identified according to anamnesis and laboratory finding. eNOS was measured by ELISA methods. RESULTS: Endothelial NOS levels were significantly higher in the CHD when compared to the controls (p < 0.05). The most dominant risk factor for CHD is overweight, and followed by dyslipidemia, smoking, hypertension, history of CHD, and diabetes mellitus. eNOS in CHD patients who had one risk factor was 37.598 ± 0.1541 ng/ml, two risk factors 42.154 ± 22.329 ng/ml, three risk factors 25.329 ± 6.083 ng/ml, four risk factors 22.483 ± 4.022 ng/ml, and five risk factors 15.994 ± 4.774 ng/ml. There were significant differences in the average eNOS levels based on the number of risk factors (p < 0.05), and a tendency that more risk factors in CHD patients, the lower the average level of eNOS. CONCLUSION: In our study, eNOS levels showed highly significant relation with CHD and related to the number of risk factors those the CHD patients had.

On the Transformation of Genetic Effect Size from Logit to Liability Scale

Genetic effects on the liability scale are informative for describing the genetic architecture of binary traits, typically diseases. However, most genetic association analyses on binary traits are performed by logistic regression, and there is no straightforward method that transforms both effect size estimate and standard error from the logit scale to the liability scale. Here, we derive a simple linear transformation of the log odds ratio and its standard error for a single nucleotide polymorphism (SNP) to an effect size and standard error on the liability scale. We show by analytic calculations and simulations that this approximation is accurate when the disease is common and the SNP effect is small. We also apply this method to estimate the contribution of a SNP near the RET gene to the variance of Hirschsprung disease liability, and the age-specific contributions of APOE4 on the variance of Alzheimer's disease liability. We discuss the approximate linear inter-relationships between genotype and effect sizes on the observed binary, logit, and liability scales, and the potential applications of the linear approximation to statistical power calculation for binary traits.

The evolution of group differences in changing environments

The selection pressures that have shaped the evolution of complex traits in humans remain largely unknown, and in some contexts highly contentious, perhaps above all where they concern mean trait differences among groups. To date, the discussion has focused on whether such group differences have any genetic basis, and if so, whether they are without fitness consequences and arose via random genetic drift, or whether they were driven by selection for different trait optima in different environments. Here, we highlight a plausible alternative: that many complex traits evolve under stabilizing selection in the face of shifting environmental effects. Under this scenario, there will be rapid evolution at the loci that contribute to trait variation, even when the trait optimum remains the same. These considerations underscore the strong assumptions about environmental effects that are required in ascribing trait differences among groups to genetic differences.

How does contemporary selection shape oak phenotypes?

Most existing forests are subjected to natural and human-mediated selection pressures, which have increased due to climate change and the increasing needs of human societies for wood, fibre and fuel resources. It remains largely unknown how these pressures trigger evolutionary changes. We address this issue here for temperate European oaks (Quercus petraea and Q. robur), which grow in mixed stands, under even-aged management regimes. We screened numerous functional traits for univariate selection gradients and for expected and observed genetic changes over two successive generations. In both species, growth, leaf morphology and physiology, and defence-related traits displayed significant selection gradients and predicted shifts, whereas phenology, water metabolism, structure and resilience-related traits did not. However, the direction of the selection response and the potential for adaptive evolution differed between the two species. Quercus petraea had a much larger phenotypic and genetic variance of fitness than Q. robur. This difference raises concerns about the adaptive response of Q. robur to contemporary selection pressures. Our investigations suggest that Q. robur will probably decline steadily, particularly in mixed stands with Q. petraea, consistent with the contrasting demographic dynamics of the two species.

Identification of 31 loci for mammographic density phenotypes and their associations with breast cancer risk

Mammographic density (MD) phenotypes are strongly associated with breast cancer risk and highly heritable. In this GWAS meta-analysis of 24,192 women, we identify 31 MD loci at P < 5 × 10-8, tripling the number known to 46. Seventeen identified MD loci also are associated with breast cancer risk in an independent meta-analysis (P < 0.05). Mendelian randomization analyses show that genetic estimates of dense area (DA), nondense area (NDA), and percent density (PD) are all significantly associated with breast cancer risk (P < 0.05). Pathway analyses reveal distinct biological processes involving DA, NDA and PD loci. These findings provide additional insights into the genetic basis of MD phenotypes and their associations with breast cancer risk.

The IL-6 rs1800795 and rs1800796 polymorphisms are associated with coronary artery disease risk

Studies examining the associations between the interleukin‐6 (IL‐6) rs1800795 and rs1800796 gene polymorphisms and risk of coronary artery disease (CAD) remain controversial. Our aim was to evaluate the accurately determine role of these two polymorphisms in CAD risk. PubMed, Embase, VIP, Wan fang and China National Knowledge Infrastructure databases were searched. The odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. The trial sequential analysis (TSA) was conducted, and bioinformatics tools were employed. A total of thirty‐seven articles were obtained. For the IL‐6 rs1800795 polymorphism, 9411 CAD patients and 3161 controls were included, 4720 patients with CAD, and 5000 controls were included for the IL‐6 rs1800796 polymorphism. In the pooled analysis, significant associations were only observed for the rs1800796 polymorphism (allelic: OR [95%CI] = 1.28 [1.13, 1.44], dominant: OR [95%CI] = 1.35 [1.17, 1.57], recessive: OR [95%CI] = 1.35 [1.18, 1.55], heterozygote: OR [95%CI] = 1.26 [1.15, 1.37], homozygote: OR [95%CI] = 1.62 [1.23, 2.13]). Significant associations were detected in the Asian and Mongoloid populations and ‘more than 500’ subgroup for the rs1800795 polymorphism. TSA confirmed the true‐positive results for the rs1800796 polymorphism. The bioinformatics analysis showed that the two polymorphisms played important roles in the gene transcription. The IL‐6 rs1800796 polymorphism is associated with an increased susceptibility to CAD and is a risk factor for CAD. The IL‐6 rs1800795 polymorphism is associated with an increased risk of CAD in Asians, particularly in Chinese, and a decreased risk of CAD in an African population is remarkably observed.

Genetics of type 2 diabetes and coronary artery disease and their associations with twelve cardiometabolic traits in the United Arab Emirates population

BACKGROUND

The United Arab Emirates (UAE) population has a high rate of type 2 diabetes mellitus (T2DM) and other metabolic risk factors for coronary artery disease (CAD). Previous studies have indicated strong genetic associations between T2DM and CAD. The objective of this study was to replicate previously reported significant genetic associations for T2DM and CAD which were in a genome-wide significance level in a cohort from the Arab population of the UAE, and to investigate the associations of these loci with twelve cardiometabolic traits that may influence the development of T2DM and CAD.

METHODS

A total of nine hundreds and fourteen Emiratis were recruited to this study to investigate associations of 101 loci for T2DM (422 patients and 455 controls), and 53 loci for CAD (160 patients and 245 controls), using logistic regression models which incorporating possible confounding factors. Results are presented using odds ratios with their corresponding 95% confidence intervals and p-values. Linear regression models, which included possible covariates were applied to determine any associations between the T2DM and CAD reported loci with the twelve cardiometabolic traits and results were presented as effect sizes (beta), standard errors, and p-values. Furthermore, the overall risks for all the loci found to be associated with T2DM and CAD were determined using the cumulative effects of the risk alleles. For those found to be associated with the twelve cardiometabolic traits, risks were determined using calculations of their polygenic risk scores.

RESULTS

The mean age of the T2DM group was 61.5 ± 11.3 and of the CAD group was 66.2 ± 9.3 years. The prevalence of most of the cardiovascular disease risk factors in this cohort were high: mean body mass index (BMI) = 29.4, T2DM (51.9%), hypertension (60.9%), dyslipidemia (68.8%), and smoking (47.9%). All individuals who were tested for CAD (n = 405) also had a diagnosis of T2DM. The highest association variant for T2DM was in SNP rs1977833 in HHEX (p = 0.0016, OR = 0.56 for allele A), which is a multi-ethnic locus for T2DM. The strongest association with CAD was detected with SNP rs264 in LPL, which encodes lipoprotein lipase (p = 0.009, OR = 1.96 for allele A). For the cardiometabolic traits analyses, most notable associations were those of FTO with BMI and waist circumference; ABO with height; KCNK16 with diastolic blood pressure; PROX1-AS1, GCKR, and MIR129-LEP with fasting blood glucose; random blood glucose with ZEB2 and THADA; HbA1c levels with TLE1 and FAM99B loci; HDL-cholesterol levels with BRAF; and triglyceride levels with ZEB2. Furthermore, accumulation of risk alleles and polygenic scores of the associated loci was clearly associated with increased risks for all tested diseases and traits in this cohort.

CONCLUSIONS

The present study highlighted many known genetic loci, which are linked to T2DM and CAD and their associations with major cardiometabolic traits in Arab descendants. We confirmed that some loci are associated with T2DM, CAD, and metabolic traits independently of the ethnic background, with a novel association also detected between height and ABO.

Genome-wide analysis highlights contribution of immune system pathways to the genetic architecture of asthma

Asthma is a chronic and genetically complex respiratory disease that affects over 300 million people worldwide. Here, we report a genome-wide analysis for asthma using data from the UK Biobank and the Trans-National Asthma Genetic Consortium. We identify 66 previously unknown asthma loci and demonstrate that the susceptibility alleles in these regions are, either individually or as a function of cumulative genetic burden, associated with risk to a greater extent in men than women. Bioinformatics analyses prioritize candidate causal genes at 52 loci, including CD52, and demonstrate that asthma-associated variants are enriched in regions of open chromatin in immune cells. Lastly, we show that a murine anti-CD52 antibody mimics the immune cell-depleting effects of a clinically used human anti-CD52 antibody and reduces allergen-induced airway hyperreactivity in mice. These results further elucidate the genetic architecture of asthma and provide important insight into the immunological and sex-specific relevance of asthma-associated risk variants.

Asthma is a common disease of the airways for which numerous genetic loci have been identified. Here, Han et al. carry out a genome-wide analysis for asthma to identify additional loci, report sex-stratified and genetic risk score analyses, and functionally follow-up one locus using a murine model of airway hyperreactivity.

Characterization of DSM-IV Opioid Dependence Among Individuals of European Ancestry

OBJECTIVE

The opioid epidemic in the United States has led to unprecedented increases in morbidity and mortality, posing a serious public health crisis. Although twin and family studies, as well as genome-wide association studies (GWAS), all identify significant genetic factors contributing to opioid dependence, no studies to date have estimated marker-based heritability estimates of opioid dependence. The goal of the current study was to use a large, genetically imputed, case/control sample of 4,064 participants (after quality control and imputation) with genome-wide data to estimate the unbiased heritability tagged by single nucleotide polymorphisms (SNPs).

METHOD

Study data were part of the Genome-wide Study of Heroin Dependence obtained via the Database for Genotypes and Phenotypes (dbGaP). Genomic-Relatedness-Matrix Restricted Maximum Likelihood with adjustment for minor allele frequency (MAF) and linkage disequilibrium (LD; GREML-LDMS) was used to determine the variation in opioid dependence attributable to common SNPs from imputed data. Mixed linear models were used in an exploratory GWAS to assess effects of single SNPs.

RESULTS

At least 45% of the variance in opioid dependence according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, was attributable to common SNPs, after stratifying to account for differences in MAF and LD across the genome. Most of the genetic variance was tagged by SNPs in the 1%-9% MAF range and in low LD with other SNPs in the region. Two markers in LOC101927293 survived multiple-testing correction (i.e., q value < .05).

CONCLUSIONS

Nearly half of the variation in opioid dependence can be attributed to common SNPs. Most of this variation is due to rare variants in low LD with other markers in the region.

In situ estimation of genetic variation of functional and ecological traits in Quercus petraea and Q.robur

BACKGROUND

Predicting the evolutionary potential of natural tree populations requires the estimation of heritability and genetic correlations among traits on which selection acts, as differences in evolutionary success between species may rely on differences for these genetic parameters. In situ estimates are expected to be more accurate than measures done under controlled conditions which do not reflect the natural environmental variance.

AIMS

The aim of the current study was to estimate three genetic parameters (i.e. heritability, evolvability and genetic correlations) in a natural mixed oak stand composed of Quercus petraea and Quercus robur about 100 years old, for 58 traits of ecological and functional relevance (growth, reproduction, phenology, physiology, resilience, structure, morphology and defence).

METHODS

First we estimated genetic parameters directly in situ using realized genomic relatedness of adult trees and parentage relationships over two generations to estimate the traits additive variance. Secondly, we benefited from existing ex situ experiments (progeny tests and conservation collection) installed with the same populations, thus allowing comparisons of in situ heritability estimates with more traditional methods.

RESULTS

Heritability and evolvability estimates obtained with different methods varied substantially and showed large confidence intervals, however we found that in situ were less precise than ex situ estimates, and assessments over two generations (with deeper relatedness) improved estimates of heritability while large sampling sizes are needed for accurate estimations. At the biological level, heritability values varied moderately across different ecological and functional categories of traits, and genetic correlations among traits were conserved over the two species.

CONCLUSION

We identified limits for using realized genomic relatedness in natural stands to estimate the genetic variance, given the overall low variance of genetic relatedness and the rather low sampling sizes of currently used long term genetic plots in forestry. These limits can be overcome if larger sample sizes are considered, or if the approach is extended over the next generation.

The genetics of human ageing

The past two centuries have witnessed an unprecedented rise in human life expectancy. Sustaining longer lives with reduced periods of disability will require an understanding of the underlying mechanisms of ageing, and genetics is a powerful tool for identifying these mechanisms. Large-scale genome-wide association studies have recently identified many loci that influence key human ageing traits, including lifespan. Multi-trait loci have been linked with several age-related diseases, suggesting shared ageing influences. Mutations that drive accelerated ageing in prototypical progeria syndromes in humans point to an important role for genome maintenance and stability. Together, these different strands of genetic research are highlighting pathways for the discovery of anti-ageing interventions that may be applicable in humans.

Pedigree-free quantitative genetic approach provides evidence for heritability of movement tactics in wild roe deer

Assessing the evolutionary potential of animal populations in the wild is crucial to understanding how they may respond to selection mediated by rapid environmental change (e.g. habitat loss and fragmentation). A growing number of studies have investigated the adaptive role of behaviour, but assessments of its genetic basis in a natural setting remain scarce. We combined intensive biologging technology with genome-wide data and a pedigree-free quantitative genetic approach to quantify repeatability, heritability and evolvability for a suite of behaviours related to the risk avoidance-resource acquisition trade-off in a wild roe deer (Capreolus capreolus) population inhabiting a heterogeneous, human-dominated landscape. These traits, linked to the stress response, movement and space-use behaviour, were all moderately to highly repeatable. Furthermore, the repeatable among-individual component of variation in these traits was partly due to additive genetic variance, with heritability estimates ranging from 0.21 ± 0.08 to 0.70 ± 0.11 and evolvability ranging from 1.1% to 4.3%. Changes in the trait mean can therefore occur under hypothetical directional selection over just a few generations. To the best of our knowledge, this is the first empirical demonstration of additive genetic variation in space-use behaviour in a free-ranging population based on genomic relatedness data. We conclude that wild animal populations may have the potential to adjust their spatial behaviour to human-driven environmental modifications through microevolutionary change.

Natural Variation Reveals a Key Role for Rhamnogalacturonan I in Seed Outer Mucilage and Underlying Genes

On imbibition, Arabidopsis (Arabidopsis thaliana) seeds release polysaccharides from their epidermal cells that form a two-layered hydrogel, termed mucilage. Analysis of a publicly available data set of outer seed mucilage traits of over 300 accessions showed little natural variation in composition. This mucilage is almost exclusively made up of rhamnogalacturonan I (RGI), highlighting the importance of this pectin for outer mucilage function. In a genome-wide association study, observed variations in polymer amount and macromolecular characteristics were linked to several genome polymorphisms, indicating the complexity of their genetic regulation. Natural variants with high molar mass were associated with a gene encoding a putative glycosyltransferase called MUCILAGE-RELATED70 (MUCI70). muci70 insertion mutants produced many short RGI polymers that were highly substituted with xylan, confirming that polymorphism in this gene can affect RGI polymer size. A second gene encoding a putative copper amine oxidase of clade 1a (CuAOα1) was associated with natural variation in the amount of RGI present in the outer mucilage layer; cuaoα1 mutants validated its role in pectin production. As the mutant phenotype is unique, with RGI production only impaired for outer mucilage, this indicates that CuAOα1 contributes to a further mechanism controlling mucilage synthesis.

Genome-wide association meta-analysis identifies five novel loci for age-related hearing impairment

Previous research has shown that genes play a substantial role in determining a person's susceptibility to age-related hearing impairment. The existing studies on this subject have different results, which may be caused by difficulties in determining the phenotype or the limited number of participants involved. Here, we have gathered the largest sample to date (discovery n = 9,675; replication n = 10,963; validation n = 356,141), and examined phenotypes that represented low/mid and high frequency hearing loss on the pure tone audiogram. We identified 7 loci that were either replicated and/or validated, of which 5 loci are novel in hearing. Especially the ILDR1 gene is a high profile candidate, as it contains our top SNP, is a known hearing loss gene, has been linked to age-related hearing impairment before, and in addition is preferentially expressed within hair cells of the inner ear. By verifying all previously published SNPs, we can present a paper that combines all new and existing findings to date, giving a complete overview of the genetic architecture of age-related hearing impairment. This is of importance as age-related hearing impairment is highly prevalent in our ageing society and represents a large socio-economic burden.

Best Prediction of the Additive Genomic Variance in Random-Effects Models

The additive genomic variance in linear models with random marker effects can be defined as a random variable that is in accordance with classical quantitative genetics theory. Common approaches to estimate the genomic variance in random-effects linear models based on genomic marker data can be regarded as estimating the unconditional (or prior) expectation of this random additive genomic variance, and result in a negligence of the contribution of linkage disequilibrium (LD). We introduce a novel best prediction (BP) approach for the additive genomic variance in both the current and the base population in the framework of genomic prediction using the genomic best linear unbiased prediction (gBLUP) method. The resulting best predictor is the conditional (or posterior) expectation of the additive genomic variance when using the additional information given by the phenotypic data, and is structurally in accordance with the genomic equivalent of the classical additive genetic variance in random-effects models. In particular, the best predictor includes the contribution of (marker) LD to the additive genomic variance and possibly fully eliminates the missing contribution of LD that is caused by the assumptions of statistical frameworks such as the random-effects model. We derive an empirical best predictor (eBP) and compare its performance with common approaches to estimate the additive genomic variance in random-effects models on commonly used genomic datasets.

Joint Estimation of Pedigrees and Effective Population Size Using Markov Chain Monte Carlo

Pedigrees provide the genealogical relationships among individuals at a fine resolution and serve an important function in many areas of genetic studies. One such use of pedigree information is in the estimation of the short-term effective population size [Formula: see text], which is of great relevance in fields such as conservation genetics. Despite the usefulness of pedigrees, however, they are often an unknown parameter and must be inferred from genetic data. In this study, we present a Bayesian method to jointly estimate pedigrees and [Formula: see text] from genetic markers using Markov Chain Monte Carlo. Our method supports analysis of a large number of markers and individuals within a single generation with the use of a composite likelihood, which significantly increases computational efficiency. We show, on simulated data, that our method is able to jointly estimate relationships up to first cousins and [Formula: see text] with high accuracy. We also apply the method on a real dataset of house sparrows to reconstruct their previously unreported pedigree.

RAD-sequencing for estimating genomic relatedness matrix-based heritability in the wild: A case study in roe deer

Estimating the evolutionary potential of quantitative traits and reliably predicting responses to selection in wild populations are important challenges in evolutionary biology. The genomic revolution has opened up opportunities for measuring relatedness among individuals with precision, enabling pedigree-free estimation of trait heritabilities in wild populations. However, until now, most quantitative genetic studies based on a genomic relatedness matrix (GRM) have focused on long-term monitored populations for which traditional pedigrees were also available, and have often had access to knowledge of genome sequence and variability. Here, we investigated the potential of RAD-sequencing for estimating heritability in a free-ranging roe deer (Capreolous capreolus) population for which no prior genomic resources were available. We propose a step-by-step analytical framework to optimize the quality and quantity of the genomic data and explore the impact of the single nucleotide polymorphism (SNP) calling and filtering processes on the GRM structure and GRM-based heritability estimates. As expected, our results show that sequence coverage strongly affects the number of recovered loci, the genotyping error rate and the amount of missing data. Ultimately, this had little effect on heritability estimates and their standard errors, provided that the GRM was built from a minimum number of loci (above 7,000). Genomic relatedness matrix-based heritability estimates thus appear robust to a moderate level of genotyping errors in the SNP data set. We also showed that quality filters, such as the removal of low-frequency variants, affect the relatedness structure of the GRM, generating lower h² estimates. Our work illustrates the huge potential of RAD-sequencing for estimating GRM-based heritability in virtually any natural population.

A Case-Control Study of the Association of the Polymorphisms of MTHFR and APOE with Risk Factors and the Severity of Coronary Artery Disease

OBJECTIVES

To explore the association between single-nucleotide polymorphisms (SNPs) in MTHFR and APOE and the risk of CAD and, more importantly, the severity of CAD and the profile of serum lipids, we performed a case-control study in a Chinese Han population.

METHODS

A total of 1,207 cases of consecutive CAD-suspected inpatients were recruited, and 406 CAD cases and 231 non-CAD controls were enrolled for the final analysis after screening for exclusion criteria. All subjects had undergone coronary angiography, and the severity of CAD was evaluated by 2 cardiologists according to the Gensini scores. The genotypes of MTHFR and APOEwere detected using real-time PCR, and then verified by Sanger sequencing. Environmental risk factors, such as age, sex, smoking, alcohol consumption, hypertension, diabetes, dyslipidemia, and BMI were collected. Statistical analyses (the χ2 test, binary logistic regression analysis, and ordinal polytomous logistic regression analysis) were performed with SPSS v16.0.

RESULTS

The genotypes ofall the subjects included in the CAD and non-CAD groups in this study were successfully detected, with an agreement of 100% with Sanger sequencing. The distributions of genotypes CT and TT at MTHFR C667T were higher in CAD cases than in non-CAD controls (OR 1.99, 95% CI 1.34-2.95; OR 1.77, 95% CI 1.18-2.67; p < 0.05), whereas genotype AC at MTHFR A1298Cwas lower in CAD cases (OR 0.71, 95% CI 0.50-1.02; p < 0.05). A significant association was observed in genotypes CT and TT at MTHFR C667T and the risk of CAD (OR 1.44, 95% CI 1.27-3.67; OR 1.56, 95% CI 0.88-2.78; p < 0.05). Both genotypes and alleles of APOE were comparable in the CAD cases and non-CAD controls (p > 0.05). The genotype TT at MTHFR C667T and ε4+ at APOE were more likely to be found in the CAD subgroup with a Gensini score ≥72 (p = 0.040 and p = 0.028, respectively). Meanwhile, in the patients with genotype TT,a higher level of serum Hcy was detected, while genotype ε4+ patients possessed higher levels of serum apolipoprotein E (ApoE) and low-density lipoprotein cholesterol (LDL-C) than other genotypes.

CONCLUSION

This study revealed that the SNP site of MTHFR C667Tis associatedwith the risk of CAD in this Chinese Han population. In addition, the genotypes of TT in MTHFR C667T and ε4+in APOE may increase the severity of CAD, and higher Hcy, LDL-C, and ApoE levels may be involved in this pathogenic process.

Estimation of metabolic syndrome heritability in three large populations including full pedigree and genomic information

Metabolic syndrome is a complex human disorder characterized by a cluster of conditions (increased blood pressure, hyperglycemia, excessive body fat around the waist, and abnormal cholesterol or triglyceride levels). Any of these conditions increases the risk of serious disorders such as diabetes or cardiovascular disease. Currently, the degree of genetic regulation of this syndrome is under debate and partially unknown. The principal aim of this study was to estimate the genetic component and the common environmental effects in different populations using full pedigree and genomic information. We used three large populations (Gubbio, ARIC, and Ogliastra cohorts) to estimate the heritability of metabolic syndrome. Due to both pedigree and genotyped data, different approaches were applied to summarize relatedness conditions. Linear mixed models (LLM) using average information restricted maximum likelihood (AIREML) algorithm were applied to partition the variances and estimate heritability (h²) and common sib-household effect (c²). Globally, results obtained from pedigree information showed a significant heritability (h²: 0.286 and 0.271 in Gubbio and Ogliastra, respectively), whereas a lower, but still significant heritability was found using SNPs data ([Formula: see text]: 0.167 and 0.254 in ARIC and Ogliastra). The remaining heritability between h² and [Formula: see text] ranged between 0.031 and 0.237. Finally, the common environmental c² in Gubbio and Ogliastra were also significant accounting for about 11% of the phenotypic variance. Availability of different kinds of populations and data helped us to better understand what happened when heritability of metabolic syndrome is estimated and account for different possible confounding. Furthermore, the opportunity of comparing different results provided more precise and less biased estimation of heritability.