Multiple Rare Alleles Contribute to Low Plasma Levels of HDL Cholesterol

A generalized model for combining dependent SNP-level summary statistics and its extensions to statistics of other levels

Here I propose a fundamentally new flexible model to reveal the association between a trait and a set of genetic variants in a genomic region/gene. This model was developed for the situation when original individual-level phenotype and genotype data are not available, but the researcher possesses the results of statistical analyses conducted on these data (namely, SNP-level summary Z score statistics and SNP-by-SNP correlations). The new model was analytically derived from the classical multiple linear regression model applied for the region-based association analysis of individual-level phenotype and genotype data by using the linear compression of data, where the SNP-by-SNP correlations are among the explanatory variables, and the summary Z score statistics are categorized as the response variables. I analytically show that the regional association analysis methods developed within the framework of the classical multiple linear regression model with additive effects of genetic variants can be reformulated in terms of the new model without the loss of information. The results obtained from the regional association analysis utilizing the classical model and those derived using the proposed model are identical when SNP-by-SNP correlations and SNP-level statistics are estimated from the same genetic data.

Identifying Rare Variant Associations in Admixed Populations

An admixed population and its ancestral populations bear different burdens of a complex disease. The ancestral populations may have different haplotypes of deleterious alleles and thus ancestry-gene interaction can influence disease risk in the admixed population. Among admixed individuals, deleterious haplotypes and their ancestries are dependent and can provide non-redundant association information. Herein we propose a local ancestry boosted sum test (LABST) for identifying chromosomal blocks that harbor rare variants but have no ancestry switches. For such a stable ancestral block, our LABST exploits ancestry-gene interaction and the number of rare alleles therein. Under the null of no genetic association, the test statistic asymptotically follows a chi-square distribution with one degree of freedom (1-df). Our LABST properly controlled type I error rates under extensive simulations, suggesting that the asymptotic approximation was accurate for the null distribution of the test statistic. In terms of power for identifying rare variant associations, our LABST uniformly outperformed several famed methods under four important modes of disease genetics over a large range of relative risks. In conclusion, exploiting ancestry-gene interaction can boost statistical power for rare variant association mapping in admixed populations.

Apolipoprotein E-C1-C4-C2 gene cluster region and inter-individual variation in plasma lipoprotein levels: a comprehensive genetic association study in two ethnic groups

The apolipoprotein E-C1-C4-C2 gene cluster at 19q13.32 encodes four amphipathic apolipoproteins. The influence of APOE common polymorphisms on plasma lipid/lipoprotein profile, especially on LDL-related traits, is well recognized; however, little is known about the role of other genes/variants in this gene cluster. In this study, we evaluated the role of common and uncommon/rare genetic variation in this gene region on inter-individual variation in plasma lipoprotein levels in non-Hispanic Whites (NHWs) and African blacks (ABs). In the variant discovery step, the APOE, APOC1, APOC4, APOC2 genes were sequenced along with their flanking and hepatic control regions (HCR1 and HCR2) in 190 subjects with extreme HDL-C/TG levels. The next step involved the genotyping of 623 NHWs and 788 ABs for the identified uncommon/rare variants and common tagSNPs along with additional relevant SNPs selected from public resources, followed by association analyses with lipid traits. A total of 230 sequence variants, including 15 indels were identified, of which 65 were novel. A total of 70 QC-passed variants in NHWs and 108 QC-passed variants in ABs were included in the final association analyses. Single-site association analysis of SNPs with MAF>1% revealed 20 variants in NHWs and 24 variants in ABs showing evidence of association with at least one lipid trait, including several variants exhibiting independent associations from the established APOE polymorphism even after multiple-testing correction. Overall, our study has confirmed known associations and also identified novel associations in this genomic region with various lipid traits. Our data also support the contribution of both common and uncommon/rare variation in this gene region in affecting plasma lipid profile in the general population.

Rare Pathogenic Variants Predispose to Hepatocellular Carcinoma in Nonalcoholic Fatty Liver Disease

Nonalcoholic fatty liver disease (NAFLD) is a rising cause of hepatocellular carcinoma (HCC). We examined whether inherited pathogenic variants in candidate genes (n = 181) were enriched in patients with NAFLD-HCC. To this end, we resequenced peripheral blood DNA of 142 NAFLD-HCC, 59 NAFLD with advanced fibrosis, and 50 controls, and considered 404 healthy individuals from 1000 G. Pathogenic variants were defined according to ClinVar, likely pathogenic as rare variants predicted to alter protein activity. In NAFLD-HCC patients, we detected an enrichment in pathogenic (p = 0.024), and likely pathogenic variants (p = 1.9*10-6), particularly in APOB (p = 0.047). APOB variants were associated with lower circulating triglycerides and higher HDL cholesterol (p < 0.01). A genetic risk score predicted NAFLD-HCC (OR 4.96, 3.29-7.55; p = 5.1*10-16), outperforming the diagnostic accuracy of common genetic risk variants, and of clinical risk factors (p < 0.05). In conclusion, rare pathogenic variants in genes involved in liver disease and cancer predisposition are associated with NAFLD-HCC development.

A gene-based recessive diplotype exome scan discovers FGF6, a novel hepcidin-regulating iron-metabolism gene

Standard analyses applied to genome-wide association data are well designed to detect additive effects of moderate strength. However, the power for standard genome-wide association study (GWAS) analyses to identify effects from recessive diplotypes is not typically high. We proposed and conducted a gene-based compound heterozygosity test to reveal additional genes underlying complex diseases. With this approach applied to iron overload, a strong association signal was identified between the fibroblast growth factor-encoding gene, FGF6, and hemochromatosis in the central Wisconsin population. Functional validation showed that fibroblast growth factor 6 protein (FGF-6) regulates iron homeostasis and induces transcriptional regulation of hepcidin. Moreover, specific identified FGF6 variants differentially impact iron metabolism. In addition, FGF6 downregulation correlated with iron-metabolism dysfunction in systemic sclerosis and cancer cells. Using the recessive diplotype approach revealed a novel susceptibility hemochromatosis gene and has extended our understanding of the mechanisms involved in iron metabolism.

Identifying Multi-Omics Causers and Causal Pathways for Complex Traits

The central dogma of molecular biology delineates a unidirectional causal flow, i.e., DNA → RNA → protein → trait. Genome-wide association studies, next-generation sequencing association studies, and their meta-analyses have successfully identified ~12,000 susceptibility genetic variants that are associated with a broad array of human physiological traits. However, such conventional association studies ignore the mediate causers (i.e., RNA, protein) and the unidirectional causal pathway. Such studies may not be ideally powerful; and the genetic variants identified may not necessarily be genuine causal variants. In this article, we model the central dogma by a mediate causal model and analytically prove that the more remote an omics level is from a physiological trait, the smaller the magnitude of their correlation is. Under both random and extreme sampling schemes, we numerically demonstrate that the proteome-trait correlation test is more powerful than the transcriptome-trait correlation test, which in turn is more powerful than the genotype-trait association test. In conclusion, integrating RNA and protein expressions with DNA data and causal inference are necessary to gain a full understanding of how genetic causal variants contribute to phenotype variations.

Genetic control of the mouse HDL proteome defines HDL traits, function, and heterogeneity

HDLs are nanoparticles with more than 80 associated proteins, phospholipids, cholesterol, and cholesteryl esters. The potential inverse relation of HDL to coronary artery disease (CAD) and the effects of HDL on myriad other inflammatory conditions warrant a better understanding of the genetic basis of the HDL proteome. We conducted a comprehensive genetic analysis of the regulation of the proteome of HDL isolated from a panel of 100 diverse inbred strains of mice (the hybrid mouse diversity panel) and examined protein composition and efflux capacity to identify novel factors that affect the HDL proteome. Genetic analysis revealed widely varied HDL protein levels across the strains. Some of this variation was explained by local cis-acting regulation, termed cis-protein quantitative trait loci (QTLs). Variations in apoA-II and apoC-3 affected the abundance of multiple HDL proteins, indicating a coordinated regulation. We identified modules of covarying proteins and defined a protein-protein interaction network that describes the protein composition of the naturally occurring subspecies of HDL in mice. Sterol efflux capacity varied up to 3-fold across the strains, and HDL proteins displayed distinct correlation patterns with macrophage and ABCA1-specific cholesterol efflux capacity and cholesterol exchange, suggesting that subspecies of HDL participate in discrete functions. The baseline and stimulated sterol efflux capacity phenotypes were associated with distinct QTLs with smaller effect size, suggesting a multigenetic regulation. Our results highlight the complexity of HDL particles by revealing the high degree of heterogeneity and intercorrelation, some of which is associated with functional variation, and support the concept that HDL-cholesterol alone is not an accurate measure of HDL’s properties, such as protection against CAD.

Role for Nucleotide Excision Repair Gene Variants in Oxaliplatin-Induced Peripheral Neuropathy

PURPOSE

Oxaliplatin forms part of routine treatment of advanced colorectal cancer; however, it often causes severe peripheral neuropathy, resulting in treatment discontinuation. We sought to determine the molecular and cellular mechanism underlying this toxicity.

PATIENTS AND METHODS

We exome resequenced blood DNA samples from nine patients with advanced colorectal cancer who had severe peripheral neuropathy associated with oxaliplatin (PNAO) within 12 weeks of treatment. We Sanger sequenced the ERCC4 and ERCC6 open reading frames in 63 patients with PNAO and carried out targeted genotyping in 1,763 patients without PNAO. We tested the functionality of ERCC4 variants using viability and DNA repair assays in Schizosaccharomyces pombe and human cell lines after exposure to oxaliplatin and ultraviolet light.

RESULTS

Exome resequencing identified one patient carrying a novel germline truncating mutation in the nucleotide excision repair (NER) gene ERCC4. This mutation was functionally associated with sensitivity to oxaliplatin (P = 3.5 × 10-2). We subsequently found that multiple rare ERCC4 nonsynonymous variants were over-represented in affected individuals (P = 7.7 × 10-3) and three of these were defective in the repair of ultraviolet light-induced DNA damage (P < 1 × 10-3). We validated a role for NER genes in PNAO by finding that multiple rare ERCC6 nonsynonymous variants were similarly over-represented in affected individuals (P = 2.4 × 10-8). Excluding private variants, 22.2% of patients (14 of 63 patients) with PNAO carried Pro379Ser or Glu875Gly in ERCC4 or Asp425Ala, Gly446Asp, or Ser797Cys in ERCC6, compared with 8.7% of unaffected patients (152 of 1,750 patients; odds ratio, 3.0; 95% CI, 1.6 to 5.6; P = 2.5 × 10-4).

CONCLUSION

Our study provides evidence for a role of NER genes in PNAO, together with mechanistic insights.

Identifying genetic markers associated with susceptibility to cardiovascular diseases

The development of cardiovascular diseases (CVDs) is due to a complex interaction between the genome and the environment. Understanding how genetic differences in individuals contribute to their susceptibility to CVDs can help guide practitioners to give the best advice to achieve a favorable outcome for the patient. As genome technologies evolve, genotyping of individuals could be available to all patients using a simple saliva test. Large-scale genome-wide association studies and meta analyses have provided powerful insights into polymorphisms that may be predictive of disease and an individual's response to certain nutrients, but moving forward it is imperative that these insights can be applied in the medical setting to reduce the incidence and mortality of CVDs.

Cardiovascular diseases (CVDs) are the leading cause of death worldwide, and while most CVDs can be prevented by adopting a healthy lifestyle, this is only half the story. Evidence suggests changes in an individual's genes or DNA can cause some form of CVDs, highlighting a complex relationship between genes and the environment. Genotyping, a process used to determine genetic differences within an individual's DNA, can provide doctors with relevant information to identify individuals who are at high risk of developing CVDs. This would allow treatment to begin early and encourage individuals to adopt a healthy lifestyle to reduce their risk.

Genetic and secondary causes of severe HDL deficiency and cardiovascular disease

We assessed secondary and genetic causes of severe HDL deficiency in 258,252 subjects, of whom 370 men (0.33%) and 144 women (0.099%) had HDL cholesterol levels <20 mg/dl. We excluded 206 subjects (40.1%) with significant elevations of triglycerides, C-reactive protein, glycosylated hemoglobin, myeloperoxidase, or liver enzymes and men receiving testosterone. We sequenced 23 lipid-related genes in 201 (65.3%) of 308 eligible subjects. Mutations (23 novel) and selected variants were found at the following gene loci: 1) ABCA1 (26.9%): 2 homozygotes, 7 compound or double heterozygotes, 30 heterozygotes, and 2 homozygotes and 13 heterozygotes with variants rs9282541/p.R230C or rs111292742/c.-279C>G; 2) LCAT (12.4%): 1 homozygote, 3 compound heterozygotes, 13 heterozygotes, and 8 heterozygotes with variant rs4986970/p.S232T; 3) APOA1 (5.0%): 1 homozygote and 9 heterozygotes; and 4) LPL (4.5%): 1 heterozygote and 8 heterozygotes with variant rs268/p.N318S. In addition, 4.5% had other mutations, and 46.8% had no mutations. Atherosclerotic cardiovascular disease (ASCVD) prevalence rates in the ABCA1, LCAT, APOA1, LPL, and mutation-negative groups were 37.0%, 4.0%, 40.0%, 11.1%, and 6.4%, respectively. Severe HDL deficiency is uncommon, with 40.1% having secondary causes and 48.8% of the subjects sequenced having ABCA1, LCAT, APOA1, or LPL mutations or variants, with the highest ASCVD prevalence rates being observed in the ABCA1 and APOA1 groups.

Whole Exome Sequencing: Applications in Prenatal Genetics

Prenatal whole exome sequencing (WES) has the potential to increase the ability to provide more diagnostic capabilities in fetuses with sonographic abnormalities, which would then improve the ability to counsel families. It is also often the first step in improving the path toward informed diagnosis and treatment, which is especially important in the era of advancing in utero fetal therapy. This article discusses the current literature regarding prenatal WES, clinical indications for WES, challenges with interpretation/counseling (variants of unknown significance), research priorities, ethical issues, and potential future advances.

Large-scale deletions of the ABCA1 gene in patients with hypoalphalipoproteinemia

Copy-number variations (CNVs) have been studied in the context of familial hypercholesterolemia but have not yet been evaluated in patients with extreme levels of HDL cholesterol. We evaluated targeted, next-generation sequencing data from patients with very low levels of HDL cholesterol (i.e., hypoalphalipoproteinemia) with the VarSeq-CNV^® caller algorithm to screen for CNVs that disrupted the ABCA1, LCAT, or APOA1 genes. In four individuals, we found three unique deletions in ABCA1: a heterozygous deletion of exon 4, a heterozygous deletion that spanned exons 8 to 31, and a heterozygous deletion of the entire ABCA1 gene. Breakpoints were identified with Sanger sequencing, and the full-gene deletion was confirmed by using exome sequencing and the Affymetrix CytoScan HD array. Previously, large-scale deletions in candidate HDL genes had not been associated with hypoalphalipoproteinemia; our findings indicate that CNVs in ABCA1 may be a previously unappreciated genetic determinant of low levels of HDL cholesterol. By coupling bioinformatic analyses with next-generation sequencing data, we can successfully assess the spectrum of genetic determinants of many dyslipidemias, including hypoalphalipoproteinemia.

Roles of the Chr.9p21.3 ANRIL Locus in Regulating Inflammation and Implications for Anti-Inflammatory Drug Target Identification

Periodontitis (PD) is a common gingival infectious disease caused by an over-aggressive inflammatory reaction to dysbiosis of the oral microbiome. The disease induces a profound systemic inflammatory host response, that triggers endothelial dysfunction and pro-thrombosis and thus may aggravate atherosclerotic vascular disease and its clinical complications. Recently, a risk haplotype at the ANRIL/CDKN2B-AS1 locus on chromosome 9p21.3, that is not only associated with coronary artery disease / myocardial infarction (CAD/MI) but also with PD, could be identified by genome-wide association studies. The locus encodes ANRIL - a long non-coding RNA (lncRNA) which, like other lncRNAs, regulates genome methylation via interacting with specific DNA sequences and proteins, such as DNA methyltranferases and polycomb proteins, thereby affecting expression of multiple genes by cis and trans mechanisms. Here, we describe ANRIL regulated genes and metabolic pathways and discuss implications of the findings for target identification of drugs with potentially anti-inflammatory activity in general.

Polygenic influences on dyslipidemias

PURPOSE OF REVIEW

Rare large-effect genetic variants underlie monogenic dyslipidemias, whereas common small-effect genetic variants - single nucleotide polymorphisms (SNPs) - have modest influences on lipid traits. Over the past decade, these small-effect SNPs have been shown to cumulatively exert consistent effects on lipid phenotypes under a polygenic framework, which is the focus of this review.

RECENT FINDINGS

Several groups have reported polygenic risk scores assembled from lipid-associated SNPs, and have applied them to their respective phenotypes. For lipid traits in the normal population distribution, polygenic effects quantified by a score that integrates several common polymorphisms account for about 20-30% of genetic variation. Among individuals at the extremes of the distribution, that is, those with clinical dyslipidemia, the polygenic component includes both rare variants with large effects and common polymorphisms: depending on the trait, 20-50% of susceptibility can be accounted for by this assortment of genetic variants.

SUMMARY

Accounting for polygenic effects increases the numbers of dyslipidemic individuals who can be explained genetically, but a substantial proportion of susceptibility remains unexplained. Whether documenting the polygenic basis of dyslipidemia will affect outcomes in clinical trials or prospective observational studies remains to be determined.

A novel LPL intronic variant: g.18704C>A identified by re-sequencing Kuwaiti Arab samples is associated with high-density lipoprotein, very low-density lipoprotein and triglyceride lipid levels

The role interethnic genetic differences play in plasma lipid level variation across populations is a global health concern. Several genes involved in lipid metabolism and transport are strong candidates for the genetic association with lipid level variation especially lipoprotein lipase (LPL). The objective of this study was to re-sequence the full LPL gene in Kuwaiti Arabs, analyse the sequence variation and identify variants that could attribute to variation in plasma lipid levels for further genetic association. Samples (n = 100) of an Arab ethnic group from Kuwait were analysed for sequence variation by Sanger sequencing across the 30 Kb LPL gene and its flanking sequences. A total of 293 variants including 252 single nucleotide polymorphisms (SNPs) and 39 insertions/deletions (InDels) were identified among which 47 variants (32 SNPs and 15 InDels) were novel to Kuwaiti Arabs. This study is the first to report sequence data and analysis of frequencies of variants at the LPL gene locus in an Arab ethnic group with a novel “rare” variant (LPL:g.18704C>A) significantly associated to HDL (B = -0.181; 95% CI (-0.357, -0.006); p = 0.043), TG (B = 0.134; 95% CI (0.004–0.263); p = 0.044) and VLDL (B = 0.131; 95% CI (-0.001–0.263); p = 0.043) levels. Sequence variation in Kuwaiti Arabs was compared to other populations and was found to be similar with regards to the number of SNPs, InDels and distribution of the number of variants across the LPL gene locus and minor allele frequency (MAF). Moreover, comparison of the identified variants and their MAF with other reports provided a list of 46 potential variants across the LPL gene to be considered for future genetic association studies. The findings warrant further investigation into the association of g.18704C>A with lipid levels in other ethnic groups and with clinical manifestations of dyslipidemia.

Rare non-coding variants are associated with plasma lipid traits in a founder population

Founder populations are ideally suited for studies on the clinical effects of alleles that are rare in general populations but occur at higher frequencies in these isolated populations. Whole genome sequencing in 98 Hutterites, a founder population of European descent, and subsequent imputation revealed 660,238 single nucleotide polymorphisms that are rare (<1%) or absent in European populations, but occur at frequencies >1% in the Hutterites. We examined the effects of these rare in European variants on plasma lipid levels in 828 Hutterites and applied a Bayesian hierarchical framework to prioritize potentially causal variants based on functional annotations. We identified two novel non-coding rare variants associated with LDL cholesterol (rs17242388 in LDLR) and HDL cholesterol (rs189679427 between GOT2 and APOOP5), and replicated previous associations of a splice variant in APOC3 (rs138326449) with triglycerides and HDL-C. All three variants are at well-replicated loci in GWAS but are independent from and have larger effect sizes than the known common variation in these regions. Candidate eQTL analyses in in LCLs in the Hutterites suggest that these rare non-coding variants are likely to mediate their effects on lipid traits by regulating gene expression.

Increased prevalence of clinical and subclinical atherosclerosis in patients with damaging mutations in ABCA1 or APOA1

BACKGROUND

A low level of high-density lipoprotein cholesterol (HDL-C) is a common clinical scenario and poses challenges for management. Many patients with low HDL-C harbor a damaging mutation in ABCA1 or APOA1, but the clinical implications of genetic testing for these mutations are unclear.

OBJECTIVE

The purpose of this study was to investigate the prevalence of clinical or subclinical atherosclerosis among patients with low HDL-C due to a mutation in ABCA1 or APOA1, compared with patients with low HDL-C without such a mutation.

METHODS

We performed targeted next-generation sequencing to identify mutations in ABCA1 and APOA1 in 72 patients with HDL-C levels below the 10^th percentile. We examined the prevalence of clinical atherosclerosis and subclinical atherosclerosis in these patients. We also measured cholesterol efflux capacity (CEC) in plasma.

RESULTS

We identified a known disease-causing or likely pathogenic variant in the ABCA1 or APOA1 genes in 22% of patients with low HDL-C. Eighty-three percent of patients with a damaging mutation in ABCA1 or APOA1 had evidence of atherosclerosis compared with 38.6% with low HDL-C without such a mutation (P = .04). Patients with damaging mutations in ABCA1 or APOA1 had lower CEC compared with patients without a mutation (25.9% vs 30.1%).

CONCLUSION

The presence of a damaging mutation in ABCA1 or APOA1 confers an increased risk of atherosclerosis relative to patients without such a mutation at a comparable level of HDL cholesterol, possibly because of a reduction in CEC.

Association detection between ordinal trait and rare variants based on adaptive combination of P values

Next-generation sequencing technology not only presents a new method for the detection of human genomic structural variation, but also provides a large number of genetic data of rare variants for us. Currently, how to detect association between human complex diseases and rare variants using genetical data has attracted extensive attention. In the field of medicine, many people's health and disease conditions are measured by ordinal response variables, namely, the trait value reflects the development stage or severity of a certain condition. However, most existing methods to test for association between rare variants and complex diseases are designed to deal with dichotomous or quantitative traits. Association analysis methods of ordinal traits are relatively fewer, and considering ordinal traits as dichotomous and quantitative traits will inevitably lose some valuable information in the original data. Therefore, in this paper, we extend an existing method of adaptive combination of P values (ADA) and propose a new method of association analysis for ordinal trait based on it (called OR-ADA) to test for possible association between ordinal trait and rare variants. In our method, we establish a cumulative logistic regression model, in which the regression coefficients are estimated by the Newton-Raphson algorithm and the likelihood ratio test is used to test the association. Through a large number of simulation studies and an example, we demonstrate the performance of the new method and compare it with several methods. The analysis results show that the OR-ADA strategy is robust to the signs of effects of causal variants and more powerful under many scenarios.

Polygenic determinants in extremes of high-density lipoprotein cholesterol

HDL cholesterol (HDL-C) remains a superior biochemical predictor of CVD risk, but its genetic basis is incompletely defined. In patients with extreme HDL-C concentrations, we concurrently evaluated the contributions of multiple large- and small-effect genetic variants. In a discovery cohort of 255 unrelated lipid clinic patients with extreme HDL-C levels, we used a targeted next-generation sequencing panel to evaluate rare variants in known HDL metabolism genes, simultaneously with common variants bundled into a polygenic trait score. Two additional cohorts were used for validation and included 1,746 individuals from the Montréal Heart Institute Biobank and 1,048 individuals from the University of Pennsylvania. Findings were consistent between cohorts: we found rare heterozygous large-effect variants in 18.7% and 10.9% of low- and high-HDL-C patients, respectively. We also found common variant accumulation, indicated by extreme polygenic trait scores, in an additional 12.8% and 19.3% of overall cases of low- and high-HDL-C extremes, respectively. Thus, the genetic basis of extreme HDL-C concentrations encountered clinically is frequently polygenic, with contributions from both rare large-effect and common small-effect variants. Multiple types of genetic variants should be considered as contributing factors in patients with extreme dyslipidemia.

Gene Discovery for Complex Traits: Lessons from Africa

The genetics of African populations reveals an otherwise "missing layer" of human variation that arose between 100,000 and 5 million years ago. Both the vast number of these ancient variants and the selective pressures they survived yield insights into genes responsible for complex traits in all populations.

Lumbosacral stenosis in Labrador retriever military working dogs - an exomic exploratory study

Background

Canine lumbosacral stenosis is defined as narrowing of the caudal lumbar and/or sacral vertebral canal. A risk factor for neurologic problems in many large sized breeds, lumbosacral stenosis can also cause early retirement in Labrador retriever military working dogs. Though vital for conservative management of the condition, early detection is complicated by the ambiguous nature of clinical signs of lumbosacral stenosis in stoic and high-drive Labrador retriever military working dogs. Though clinical diagnoses of lumbosacral stenosis using CT imaging are standard, they are usually not performed unless dogs present with clinical symptoms. Understanding the underlying genomic mechanisms would be beneficial in developing early detection methods for lumbosacral stenosis, which could prevent premature retirement in working dogs. The exomes of 8 young Labrador retriever military working dogs (4 affected and 4 unaffected by lumbosacral stenosis, phenotypically selected by CT image analyses from 40 dogs with no reported clinical signs of the condition) were sequenced to identify and annotate exonic variants between dogs negative and positive for lumbosacral stenosis.

Results

Two-hundred and fifty-two variants were detected to be homozygous for the wild allele and either homozygous or heterozygous for the variant allele. Seventeen non-disruptive variants were detected that could affect protein effectiveness in 7 annotated (SCN1B, RGS9BP, ASXL3, TTR, LRRC16B, PTPRO, ZBBX) and 3 predicted genes (EEF1A1, DNAJA1, ZFX). No exonic variants were detected in any of the canine orthologues for human lumbar spinal stenosis candidate genes.

Conclusions

TTR (transthyretin) gene could be a possible candidate for lumbosacral stenosis in Labrador retrievers based on previous human studies that have reported an association between human lumbar spinal stenosis and transthyretin protein amyloidosis. Other genes identified with exonic variants in this study but with no known published association with lumbosacral stenosis and/or lumbar spinal stenosis could also be candidate genes for future canine lumbosacral stenosis studies but their roles remain currently unknown. Human lumbar spinal stenosis candidate genes also cannot be ruled out as lumbosacral stenosis candidate genes. More definitive genetic investigations of this condition are needed before any genetic test for lumbosacral stenosis in Labrador retriever can be developed.

Electronic supplementary material

The online version of this article (10.1186/s40575-017-0052-6) contains supplementary material, which is available to authorized users.

Target sequencing of 307 deafness genes identifies candidate genes implicated in microtia

Microtia is a congenital malformation of the external ear caused by genetic and/or environmental factors. However, no causal genetic mutations have been identified in isolated microtia patients. In this study, we utilized targeted genomic capturing combined with next-generation sequencing to screen for mutations in 307 deafness genes in 32 microtia patients. Forty-two rare heterozygous mutations in 25 genes, including 22 novel mutations in 24 isolated unilateral microtia cases were identified. Pathway analysis found five pathways especially focal adhesion pathway and ECM-receptor interaction pathway were significantly associated with microtia. The low-frequency variants association study was used and highlighted several strong candidate genes MUC4, MUC6, COL4A4, MYO7A, AKAP12, COL11A1, DSPP, ESPN, GPR98, PCDH15, BSN, CACNA1D, TPRN, and USH1C for microtia (P = 2.51 × 10^-4). Among these genes, COL4A4 and COL11A1 may lead to microtia through focal adhesion pathway and ECM-receptor interaction pathway which are connected to the downstream Wnt signaling pathway. The present results indicate that certain genes may affect both external/middle and inner ear development, and demonstrate the benefits of using a capture array in microtia patients.

TEX15: A DNA repair gene associated with prostate cancer risk in Han Chinese

BACKGROUND

Both common and rare genetic variants may contribute to risk of developing prostate cancer. Genome-wide association studies (GWASs) have identified ∼100 independent, common variants associated with prostate cancer risk. However, little is known about the association of rare variants (minor allele frequency [MAF] <1%) in the genome with prostate cancer risk.

METHODS

A two-stage study was used to test the association of rare, deleterious coding variants, annotated using predictive algorithms, with prostate cancer risk in Chinese men. Predicted rare, deleterious coding variants in the Illumina HumanExome-12 v1.1 beadchip were first evaluated in 1343 prostate cancer patients and 1008 controls. Significant variants were then validated in an additional 1816 prostate cancer patients and 1549 controls.

RESULTS

In the discovery stage, 14 predicted rare, deleterious coding variants were significantly associated with prostate cancer risk (P < 0.01). In the confirmation stage, Q1631H in TEX15 (rs142485241), a DNA repair gene, was significantly associated with prostate cancer risk (P = 0.0069). The estimated odds ratio (OR) of the variant in the combined analysis was 3.24 (95% Confidence Interval 1.85-6.06), P = 8.81 × 10^-5 . Additionally, rs28756990 (V741F) at MLH3 (P = 0.06) and rs2961144 (I126V) at OR2A5 (P = 0.065) were marginally associated with prostate cancer risk in the replication stage.

CONCLUSIONS

Our study provided preliminary evidence that the rare variant Q1631H in DNA repair gene TEX15 is associated with prostate cancer risk. This finding complements known common prostate cancer risk-associated variants and suggests the possible role of DNA repair genes in prostate cancer development.

Rare coding variants pinpoint genes that control human hematological traits

The identification of rare coding or splice site variants remains the most straightforward strategy to link genes with human phenotypes. Here, we analyzed the association between 137,086 rare (minor allele frequency (MAF) <1%) coding or splice site variants and 15 hematological traits in up to 308,572 participants. We found 56 such rare coding or splice site variants at P<5x10-8, including 31 that are associated with a blood-cell phenotype for the first time. All but one of these 31 new independent variants map to loci previously implicated in hematopoiesis by genome-wide association studies (GWAS). This includes a rare splice acceptor variant (rs146597587, MAF = 0.5%) in interleukin 33 (IL33) associated with reduced eosinophil count (P = 2.4x10-23), and lower risk of asthma (P = 2.6x10-7, odds ratio [95% confidence interval] = 0.56 [0.45-0.70]) and allergic rhinitis (P = 4.2x10-4, odds ratio = 0.55 [0.39-0.76]). The single new locus identified in our study is defined by a rare p.Arg172Gly missense variant (rs145535174, MAF = 0.05%) in plasminogen (PLG) associated with increased platelet count (P = 6.8x10-9), and decreased D-dimer concentration (P = 0.018) and platelet reactivity (P<0.03). Finally, our results indicate that searching for rare coding or splice site variants in very large sample sizes can help prioritize causal genes at many GWAS loci associated with complex human diseases and traits.

Low-Frequency Synonymous Coding Variation in CYP2R1 Has Large Effects on Vitamin D Levels and Risk of Multiple Sclerosis

Vitamin D insufficiency is common, correctable, and influenced by genetic factors, and it has been associated with risk of several diseases. We sought to identify low-frequency genetic variants that strongly increase the risk of vitamin D insufficiency and tested their effect on risk of multiple sclerosis, a disease influenced by low vitamin D concentrations. We used whole-genome sequencing data from 2,619 individuals through the UK10K program and deep-imputation data from 39,655 individuals genotyped genome-wide. Meta-analysis of the summary statistics from 19 cohorts identified in CYP2R1 the low-frequency (minor allele frequency = 2.5%) synonymous coding variant g.14900931G>A (p.Asp120Asp) (rs117913124[A]), which conferred a large effect on 25-hydroxyvitamin D (25OHD) levels (-0.43 SD of standardized natural log-transformed 25OHD per A allele; p value = 1.5 × 10-88). The effect on 25OHD was four times larger and independent of the effect of a previously described common variant near CYP2R1. By analyzing 8,711 individuals, we showed that heterozygote carriers of this low-frequency variant have an increased risk of vitamin D insufficiency (odds ratio [OR] = 2.2, 95% confidence interval [CI] = 1.78-2.78, p = 1.26 × 10-12). Individuals carrying one copy of this variant also had increased odds of multiple sclerosis (OR = 1.4, 95% CI = 1.19-1.64, p = 2.63 × 10-5) in a sample of 5,927 case and 5,599 control subjects. In conclusion, we describe a low-frequency CYP2R1 coding variant that exerts the largest effect upon 25OHD levels identified to date in the general European population and implicates vitamin D in the etiology of multiple sclerosis.

Evolutionary public health: introducing the concept

The emerging discipline of evolutionary medicine is breaking new ground in understanding why people become ill. However, the value of evolutionary analyses of human physiology and behaviour is only beginning to be recognised in the field of public health. Core principles come from life history theory, which analyses the allocation of finite amounts of energy between four competing functions-maintenance, growth, reproduction, and defence. A central tenet of evolutionary theory is that organisms are selected to allocate energy and time to maximise reproductive success, rather than health or longevity. Ecological interactions that influence mortality risk, nutrient availability, and pathogen burden shape energy allocation strategies throughout the life course, thereby affecting diverse health outcomes. Public health interventions could improve their own effectiveness by incorporating an evolutionary perspective. In particular, evolutionary approaches offer new opportunities to address the complex challenges of global health, in which populations are differentially exposed to the metabolic consequences of poverty, high fertility, infectious diseases, and rapid changes in nutrition and lifestyle. The effect of specific interventions is predicted to depend on broader factors shaping life expectancy. Among the important tools in this approach are mathematical models, which can explore probable benefits and limitations of interventions in silico, before their implementation in human populations.

Genetic determinants of inherited susceptibility to hypercholesterolemia - a comprehensive literature review

Hypercholesterolemia is a strong determinant of mortality and morbidity associated with cardiovascular diseases and a major contributor to the global disease burden. Mutations in four genes (LDLR, APOB, PCSK9 and LDLRAP1) account for the majority of cases with familial hypercholesterolemia. However, a substantial proportion of adults with hypercholesterolemia do not have a mutation in any of these four genes. This indicates the probability of having other genes with a causative or contributory role in the pathogenesis of hypercholesterolemia and suggests a polygenic inheritance of this condition. Here in, we review the recent evidence of association of the genetic variants with hypercholesterolemia and the three lipid traits; total cholesterol (TC), HDL-cholesterol (HDL-C) and LDL-cholesterol (LDL-C), their biological pathways and the associated pathogenetic mechanisms. Nearly 80 genes involved in lipid metabolism (encoding structural components of lipoproteins, lipoprotein receptors and related proteins, enzymes, lipid transporters, lipid transfer proteins, and activators or inhibitors of protein function and gene transcription) with single nucleotide variants (SNVs) that are recognized to be associated with hypercholesterolemia and serum lipid traits in genome-wide association studies and candidate gene studies were identified. In addition, genome-wide association studies in different populations have identified SNVs associated with TC, HDL-C and LDL-C in nearly 120 genes within or in the vicinity of the genes that are not known to be involved in lipid metabolism. Over 90% of the SNVs in both these groups are located outside the coding regions of the genes. These findings indicates that there might be a considerable number of unrecognized processes and mechanisms of lipid homeostasis, which when disrupted, would lead to hypercholesterolemia. Knowledge of these molecular pathways will enable the discovery of novel treatment and preventive methods as well as identify the biochemical and molecular markers for the risk prediction and early detection of this common, yet potentially debilitating condition.

The impact of rare and low-frequency genetic variants in common disease

Despite thousands of genetic loci identified to date, a large proportion of genetic variation predisposing to complex disease and traits remains unaccounted for. Advances in sequencing technology enable focused explorations on the contribution of low-frequency and rare variants to human traits. Here we review experimental approaches and current knowledge on the contribution of these genetic variants in complex disease and discuss challenges and opportunities for personalised medicine.

Adiposity amplifies the genetic risk of fatty liver disease conferred by multiple loci

Complex traits arise from the interplay between genetic and environmental factors. The actions of these factors usually appear to be additive, and few compelling examples of gene-environment synergy have been documented. Here we show that adiposity significantly amplifies the effect of three sequence variants (PNPLA3-I148M, TM6SF2-E167K and GCKR-P446L) associated with nonalcoholic fatty liver disease (NAFLD). Synergy between adiposity and genotype promoted the full spectrum of NAFLD, from steatosis to hepatic inflammation to cirrhosis. We found no evidence of strong interactions between adiposity and sequence variants influencing other adiposity-associated traits. These results indicate that adiposity may augment genetic risk of NAFLD at multiple loci through at least three different metabolic mechanisms.

Identification and characterization of Aurora kinase B and C variants associated with maternal aneuploidy

STUDY QUESTION

Are single nucleotide variants (SNVs) in Aurora kinases B and C (AURKB, AURKC) associated with risk of aneuploid conception?

SUMMARY ANSWER

Two SNVs were found in patients with extreme aneuploid concepti rates with respect to their age; one variant, AURKC p.I79V, is benign, while another, AURKB p.L39P, is a potential gain-of-function mutant with increased efficiency in promoting chromosome alignment.

WHAT IS KNOWN ALREADY

Maternal age does not always predict aneuploidy risk, and rare gene variants can be drivers of disease. The AURKB and AURKC regulate chromosome segregation, and are associated with reproductive impairments in mouse and human.

STUDY DESIGN, SIZE, DURATION

An extreme phenotype sample selection scheme was performed for variant discovery. Ninety-six DNA samples were from young patients with higher than average embryonic aneuploidy rates and an additional 96 DNA samples were from older patients with lower than average aneuploidy rates.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Using the192 DNA samples, the coding regions of AURKB and AURKC were sequenced using next generation sequencing. To assess biological significance, we expressed complementary RNA encoding the human variants in mouse oocytes. Assays such as determining subcellular localization and assessing catalytic activity were performed to determine alterations in protein function during meiosis.

MAIN RESULTS AND THE ROLE OF CHANCE

Ten SNVs were identified using three independent variant-calling methods. Two of the SNVs (AURKB p.L39P and AURKC p.I79V) were non-synonymous and identified by at least two variant-identification methods. The variant encoding AURKC p.I79V, identified in a young woman with a higher than average rate of aneuploid embryos, showed wild-type localization pattern and catalytic activity. On the other hand, the variant encoding AURKB p.L39P, identified in an older woman with lower than average rates of aneuploid embryos, increased the protein's ability to regulate alignment of chromosomes at the metaphase plate. These experiments were repeated three independent times using 2-3 mice for each trial.

LARGE SCALE DATA

N/A.

LIMITATIONS, REASONS FOR CAUTION

Biological significance of the human variants was assessed in an in vitro mouse oocyte model where the variants are over-expressed. Therefore, the human protein may not function identically to the mouse homolog, or the same in mouse oocytes as in human oocytes. Furthermore, supraphysiological expression levels may not accurately reflect endogenous activity. Moreover, the evaluated variants were identified in one patient each, and no trial linking the SNV to pregnancy outcomes was conducted. Finally, the patient aneuploidy rates were established by performing comprehensive chromosome screening in blastocysts, and because of the link between female gamete aneuploidy giving rise to aneuploid embryos, we evaluate the role of the variants in Meiosis I. However, it is possible that the chromosome segregation mistake arose during Meiosis II or in mitosis in the preimplantation embryo. Their implications in human female meiosis and aneuploidy risk remain to be determined.

WIDER IMPLICATIONS OF THE FINDINGS

The data provide evidence that gene variants exist in reproductively younger or advanced aged women that are predictive of the risk of producing aneuploid concepti in humans. Furthermore, a single amino acid in the N-terminus of AURKB is a gain-of-function mutant that could be protective of euploidy.

STUDY FUNDING/COMPETING INTERESTS

This work was supported by a Research Grant from the American Society of Reproductive Medicine and support from the Charles and Johanna Busch Memorial Fund at Rutgers, the State University of NJ to K.S. and the Foundation for Embryonic Competence, Inc to N.T. The authors declare no conflicts of interest.

An efficient and flexible test for rare variant effects

Since it has been claimed that rare variants with extremely small allele frequency play a crucial role in complex traits, there is great demand for the development of a powerful test for detecting these variants. However, due to the extremely low frequencies of rare variants, common statistical testing methods do not work well, which has motivated recent extensive research on developing an efficient testing procedure for rare variant effects. Many studies have suggested effective testing procedures with reasonably high power under some presumed assumptions of parametric statistical models. However, if the parametric assumptions are violated, these tests are possibly under-powered. In this paper, we develop an optimal, powerful statistical test called the aggregated conditional score test (ACST) for simultaneously testing M rare variant effects without restrictive parametric assumptions. The proposed test uses a test statistic aggregating the conditional score statistics of effect sizes of M rare variants. In simulation studies, ACST generally performed well compared with the two most commonly used tests, the optimal sequence kernel association test (SKAT-O) and Kullback-Leibler distance test. Finally, we demonstrate the performance and practical utility of ACST using the Dallas Heart Study data.

ABC Transport Proteins in Cardiovascular Disease-A Brief Summary

Adenosine triphosphate (ATP)-binding cassette (ABC) transporters may play an important role in the pathogenesis of atherosclerotic vascular diseases due to their involvement in cholesterol homeostasis, blood pressure regulation, endothelial function, vascular inflammation, as well as platelet production and aggregation. In this regard, ABC transporters, such as ABCA1, ABCG5 and ABCG8, were initially found to be responsible for genetically-inherited syndromes like Tangier diseases and sitosterolemia. These findings led to the understanding of those transporter’s function in cellular cholesterol efflux and thereby also linked them to atherosclerosis and cardiovascular diseases (CVD). Subsequently, further ABC transporters, i.e., ABCG1, ABCG4, ABCB6, ABCC1, ABCC6 or ABCC9, have been shown to directly or indirectly affect cellular cholesterol efflux, the inflammatory response in macrophages, megakaryocyte proliferation and thrombus formation, as well as vascular function and blood pressure, and may thereby contribute to the pathogenesis of CVD and its complications. Furthermore, ABC transporters, such as ABCB1, ABCC2 or ABCG2, may affect the safety and efficacy of several drug classes currently in use for CVD treatment. This review will give a brief overview of ABC transporters involved in the process of atherogenesis and CVD pathology. It also aims to briefly summarize the role of ABC transporters in the pharmacokinetics and disposition of drugs frequently used to treat CVD and CVD-related complications.

Whole-exome sequencing in individuals with multiple cardiovascular risk factors and normal coronary arteries

OBJECTIVES

Most studies on the genes involved in coronary artery disease (CAD) targeted individuals with angiographically or clinically proven CAD. Focusing on high-risk individuals with normal coronary arteries (NCA) may offer novel insights into the pathogenesis of CAD. We aimed to identify genes putatively protective for development of CAD.

METHODS

Pooled whole-exome sequencing (WES) was performed on 17 patients with multiple cardiovascular risk factors and NCA and on 17 controls with multivessel CAD. Rare NCA-unique sequence variants were subsequently individually validated using the Fluidigm platform in 100 additional CAD controls and 100 general population controls.

RESULTS

In total, 555 100 variants were detected in at least one WES pool in the study group and in none of the control WES pools. For second phase validation, we focused on rare, nonsynonymous variants, resulting in a total of 144 variants in 40 genes, of which 96 were selected for subsequent genotyping. Validation phase genotyping resulted in 19 variants in 16 genes that were found in the NCA group and in none of the CAD controls. The SPTBN5, NID2, and ADAMTSL4 genes harbored sequence variants in more than one CAD-protected patient and none of the 117 CAD controls.

CONCLUSION

Applying WES technology and focusing on individuals seemingly protected from developing CAD successfully identified 19 variants that may offer protection from CAD by undetermined mechanisms. Studying the genetics of high-risk individuals apparently protected from CAD may provide novel insights into the pathogenesis of CAD.

Detecting association of rare and common variants based on cross-validation prediction error

Despite the extensive discovery of disease-associated common variants, much of the genetic contribution to complex traits remains unexplained. Rare variants may explain additional disease risk or trait variability. Although sequencing technology provides a supreme opportunity to investigate the roles of rare variants in complex diseases, detection of these variants in sequencing-based association studies presents substantial challenges. In this article, we propose novel statistical tests to test the association between rare and common variants in a genomic region and a complex trait of interest based on cross-validation prediction error (PE). We first propose a PE method based on Ridge regression. Based on PE, we also propose another two tests PE-WS and PE-TOW by testing a weighted combination of variants with two different weighting schemes. PE-WS is the PE version of the test based on the weighted sum statistic (WS) and PE-TOW is the PE version of the test based on the optimally weighted combination of variants (TOW). Using extensive simulation studies, we are able to show that (1) PE-TOW and PE-WS are consistently more powerful than TOW and WS, respectively, and (2) PE is the most powerful test when causal variants contain both common and rare variants.

An investigation of obesity susceptibility genes in Northern Han Chinese by targeted resequencing

Our earlier genome-wide linkage study of body mass index (BMI) showed strong signals from 7q36.3 and 8q21.13. This case-control study set to investigate 2 genomic regions which may harbor variants contributed to development of obesity.We employed targeted resequencing technology to detect single nucleotide polymorphisms (SNPs) in 7q36.3 and 8q21.13 from 16 individuals with obesity. These were compared with 504 East Asians in the 1000 Genomes Project as a reference panel. Linkage disequilibrium (LD) block analysis was performed for the significant SNPs located near the same gene. Genes involved in statistically significant loci were then subject to gene set enrichment analysis (GSEA).The 16 individuals aged between 30 and 60 years with BMI = 33.25 ± 2.22 kg/m. A total of 12,131 genetic variants across all of samples were found. After correcting for multiple testing, 65 SNPs from 25 nearest genes (INSIG1, FABP5, PTPRN2, VIPR2, WDR60, SHH, UBE3C, LMBR1, PAG1, IMPA1, CHMP4, SNX16, BLACE, EN2, CNPY1, LOC100506302, RBM33, LOC389602, LOC285889, LINC01006, NOM1, DNAJB6, LOC101927914, ESYT2, LINC00689) were associated with obesity at significant level q-value ≤ 0.05. LD block analysis showed there were 10 pairs of loci with D' ≥ 0.8 and r ≥ 0.8. GSEA further identified 2 major related gene sets, involving lipid raft and lipid metabolic process, with FDR values <0.12 and <0.4, respectively.Our data are the first documentation of genetic variants in 7q36.3 and 8q21.13 associated with obesity using target capture sequencing and Northern Han Chinese samples. Additional replication and functional studies are merited to validate our findings.

An atypical presentation of ACAD9 deficiency: Diagnosis by whole exome sequencing broadens the phenotypic spectrum and alters treatment approach

Acyl-CoA dehydrogenase 9 (ACAD9), linked to chromosome 3q21.3, is one of a family of multimeric mitochondrial flavoenzymes that catalyze the degradation of fatty acyl-CoA from the carnitine shuttle via β-oxidation (He et al. 2007). ACAD9, specifically, is implicated in the processing of palmitoyl-CoA and long-chain unsaturated substrates, but unlike other acyl-CoA dehydrogenases (ACADs), it has a significant role in mitochondrial complex I assembly (Nouws et al. 2010 & 2014). Mutations in this enzyme typically cause mitochondrial complex I deficiency, as well as a mild defect in long chain fatty acid metabolism (Haack et al. 2010, Kirby et al. 2004, Mcfarland et al. 2003, Nouws et al. 2010 & 2014). The clinical phenotype of ACAD9 deficiency and the associated mitochondrial complex I deficiency reflect this unique duality, and symptoms are variable in severity and onset. Patients classically present with cardiac dysfunction due to hypertrophic cardiomyopathy. Other common features include Leigh syndrome, macrocephaly, and liver disease (Robinson et al. 1998).

We report the case of an 11-month old girl presenting with microcephaly, dystonia, and lactic acidosis, concerning for a mitochondrial disorder, but atypical for ACAD9 deficiency. Muscle biopsy showed mitochondrial proliferation, but normal mitochondrial complex I activity. The diagnosis of ACAD9 deficiency was not initially considered, due both to these findings and to her atypical presentation. Biochemical assay for ACAD9 deficiency is not clinically available. Family trio-based whole exome sequencing (WES) identified 2 compound heterozygous mutations in the ACAD9 gene. This discovery led to optimized treatment of her mitochondrial dysfunction, and supplementation with riboflavin, resulting in clinical improvement.

There have been fewer than 25 reported cases of ACAD9 deficiency in the literature to date. We review these and compare them to the unique features of our patient. ACAD9 deficiency should be considered in the differential diagnosis of patients with lactic acidosis, seizures, and other symptoms of mitochondrial disease, including those with normal mitochondrial enzyme activities. This case demonstrates the utility of WES, in conjunction with biochemical testing, for the appropriate diagnosis and treatment of disorders of energy metabolism.

Ethnic disparities in the prevalence of metabolic syndrome and its risk factors in the Suriname Health Study: a cross-sectional population study

BACKGROUND

The metabolic syndrome (MetS) indicates increased risk for cardiovascular disease and type 2 diabetes. We estimated the overall and ethnic-specific prevalence of MetS and explored the associations of risk factors with MetS among Amerindian, Creole, Hindustani, Javanese, Maroon and Mixed ethnic groups.

METHOD

We used the 2009 Joint Interim Statement (JIS) to define MetS in a subgroup of 2946 participants of the Suriname Health Study, a national survey designed according to the WHO Steps guidelines. The prevalences of MetS and its components were determined for all ethnicities. Hierarchical logistic regressions were used to determine the associations of ethnicity, sex, age, marital status, educational level, income status, employment, smoking status, residence, physical activity, fruit and vegetable intake with MetS.

RESULTS

The overall estimated prevalence of MetS was 39.2%. From MetS components, central obesity and low high-density lipoprotein cholesterol (HDL-C) had the highest prevalences. The prevalence of MetS was highest for the Hindustanis (52.7%) and lowest for Maroons (24.2%). The analyses showed that in the overall population sex (women: OR 1.4; 95% CI 1.2 to 1.6), age (OR 5.5 CI 4.3 to 7.2), education (OR 0.7 CI 0.6 to 0.9), living area (OR 0.6 CI 0.5 to 0.8), income (OR 0.7 CI 0.5 to 0.9) and marital status (OR 1.3 CI 1.1 to 1.6) were associated with MetS. Variations observed in the associations of the risk factors with MetS in the ethnic groups did not materially influence the associations of ethnicities with MetS.

CONCLUSIONS

The prevalence of MetS was high and varied widely among ethnicities. Overall, central obesity and low HDL-C contributed most to MetS. Further studies are needed to assess the prospective associations of risk factors with MetS in different ethnic groups.

Discovery and refinement of genetic loci associated with cardiometabolic risk using dense imputation maps

Large-scale whole-genome sequence data sets offer novel opportunities to identify genetic variation underlying human traits. Here we apply genotype imputation based on whole-genome sequence data from the UK10K and 1000 Genomes Project into 35,981 study participants of European ancestry, followed by association analysis with 20 quantitative cardiometabolic and hematological traits. We describe 17 new associations, including 6 rare (minor allele frequency (MAF) < 1%) or low-frequency (1% < MAF < 5%) variants with platelet count (PLT), red blood cell indices (MCH and MCV) and HDL cholesterol. Applying fine-mapping analysis to 233 known and new loci associated with the 20 traits, we resolve the associations of 59 loci to credible sets of 20 or fewer variants and describe trait enrichments within regions of predicted regulatory function. These findings improve understanding of the allelic architecture of risk factors for cardiometabolic and hematological diseases and provide additional functional insights with the identification of potentially novel biological targets.

Quantifying unobserved protein-coding variants in human populations provides a roadmap for large-scale sequencing projects

As new proposals aim to sequence ever larger collection of humans, it is critical to have a quantitative framework to evaluate the statistical power of these projects. We developed a new algorithm, UnseenEst, and applied it to the exomes of 60,706 individuals to estimate the frequency distribution of all protein-coding variants, including rare variants that have not been observed yet in the current cohorts. Our results quantified the number of new variants that we expect to identify as sequencing cohorts reach hundreds of thousands of individuals. With 500K individuals, we find that we expect to capture 7.5% of all possible loss-of-function variants and 12% of all possible missense variants. We also estimate that 2,900 genes have loss-of-function frequency of <0.00001 in healthy humans, consistent with very strong intolerance to gene inactivation.

Accurate estimations of the frequency distribution of rare variants are needed to quantify the discovery power and guide large-scale human sequencing projects. This study describes an algorithm called UnseenEst to estimate the distribution of genetic variations using tens of thousands of exomes.

Comparing family-based rare variant association tests for dichotomous phenotypes

Background

It has been repeatedly stressed that family-based samples suffer less from genetic heterogeneity and that association analyses with family-based samples are expected to be powerful for detecting susceptibility loci for rare disease. Various approaches for rare-variant analysis with family-based samples have been proposed.

Methods

In this report, performances of the existing methods were compared with the simulated data set provided as part of Genetic Analysis Workshop 19 (GAW19). We considered the rare variant transmission disequilibrium test (RV-TDT), generalized estimating equations-based kernel association (GEE-KM) test, an extended combined multivariate and collapsing test for pedigree data (known as Pedigree Combined Multivariate and Collapsing [PedCMC]), gene-level kernel and burden association tests with disease status for pedigree data (PedGene), and the family-based rare variant association test (FARVAT).

Results

The results show that PedGene and FARVAT are usually the most efficient, and the optimal test statistic provided by FARVAT is robust under different disease models. Furthermore, FARVAT was implemented with C++, which is more computationally faster than other methods.

Conclusions

Considering both statistical and computational efficiency, we conclude that FARVAT is a good choice for rare-variant analysis with extended families.

Increasing Generality and Power of Rare-Variant Tests by Utilizing Extended Pedigrees

Recently, multiple studies have performed whole-exome or whole-genome sequencing to identify groups of rare variants associated with complex traits and diseases. They have primarily utilized case-control study designs that often require thousands of individuals to reach acceptable statistical power. Family-based studies can be more powerful because a rare variant can be enriched in an extended pedigree and segregate with the phenotype. Although many methods have been proposed for using family data to discover rare variants involved in a disease, a majority of them focus on a specific pedigree structure and are designed to analyze either binary or continuously measured outcomes. In this article, we propose RareIBD, a general and powerful approach to identifying rare variants involved in disease susceptibility. Our method can be applied to large extended families of arbitrary structure, including pedigrees with only affected individuals. The method accommodates both binary and quantitative traits. A series of simulation experiments suggest that RareIBD is a powerful test that outperforms existing approaches. In addition, our method accounts for individuals in top generations, which are not usually genotyped in extended families. In contrast to available statistical tests, RareIBD generates accurate p values even when genetic data from these individuals are missing. We applied RareIBD, as well as other methods, to two extended family datasets generated by different genotyping technologies and representing different ethnicities. The analysis of real data confirmed that RareIBD is the only method that properly controls type I error.

Using Genetic Distance to Infer the Accuracy of Genomic Prediction

The prediction of phenotypic traits using high-density genomic data has many applications such as the selection of plants and animals of commercial interest; and it is expected to play an increasing role in medical diagnostics. Statistical models used for this task are usually tested using cross-validation, which implicitly assumes that new individuals (whose phenotypes we would like to predict) originate from the same population the genomic prediction model is trained on. In this paper we propose an approach based on clustering and resampling to investigate the effect of increasing genetic distance between training and target populations when predicting quantitative traits. This is important for plant and animal genetics, where genomic selection programs rely on the precision of predictions in future rounds of breeding. Therefore, estimating how quickly predictive accuracy decays is important in deciding which training population to use and how often the model has to be recalibrated. We find that the correlation between true and predicted values decays approximately linearly with respect to either F_ST or mean kinship between the training and the target populations. We illustrate this relationship using simulations and a collection of data sets from mice, wheat and human genetics.

The availability of increasing amounts of genomic data is making the use of statistical models to predict traits of interest a mainstay of many applications in life sciences. Applications range from medical diagnostics for common and rare diseases to breeding characteristics such as disease resistance in plants and animals of commercial interest. We explored an implicit assumption of how such prediction models are often assessed: that the individuals whose traits we would like to predict originate from the same population as those that are used to train the models. This is commonly not the case, especially in the case of plants and animals that are parts of selection programs. To study this problem we proposed a model-agnostic approach to infer the accuracy of prediction models as a function of two common measures of genetic distance. Using data from plant, animal and human genetics, we find that accuracy decays approximately linearly in either of those measures. Quantifying this decay has fundamental applications in all branches of genetics, as it measures how studies generalise to different populations.