Matthew B. Lanktree

Excess of rare variants in genes identified by genome-wide association study of hypertriglyceridemia

Genome-wide association studies (GWAS) have identified multiple loci associated with plasma lipid concentrations. Common variants at these loci together explain <10% of variation in each lipid trait. Rare variants with large individual effects may also contribute to the heritability of lipid traits; however, the extent to which rare variants affect lipid phenotypes remains to be determined. Here we show an accumulation of rare variants, or a mutation skew, in GWAS-identified genes in individuals with hypertriglyceridemia (HTG). Through GWAS, we identified common variants in APOA5, GCKR, LPL and APOB associated with HTG. Resequencing of these genes revealed a significant burden of 154 rare missense or nonsense variants in 438 individuals with HTG, compared to 53 variants in 327 controls (P = 6.2 × 10−8), corresponding to a carrier frequency of 28.1% of affected individuals and 15.3% of controls (P = 2.6 × 10−5). Considering rare variants in these genes incrementally increased the proportion of genetic variation contributing to HTG.

Mendelian randomization of blood lipids for coronary heart disease.

Aims To investigate the causal role of high-density lipoprotein cholesterol (HDL-C) and triglycerides in coronary heart disease (CHD) using multiple instrumental variables for Mendelian randomization. Methods and results We developed weighted allele scores based on single nucleotide polymorphisms (SNPs) with established associations with HDL-C, triglycerides, and low-density lipoprotein cholesterol (LDL-C). For each trait, we constructed two scores. The first was unrestricted, including all independent SNPs associated with the lipid trait identified from a prior meta-analysis (threshold P < 2 × 10−6); and the second a restricted score, filtered to remove any SNPs also associated with either of the other two lipid traits at P ≤ 0.01. Mendelian randomization meta-analyses were conducted in 17 studies including 62,199 participants and 12,099 CHD events. Both the unrestricted and restricted allele scores for LDL-C (42 and 19 SNPs, respectively) associated with CHD. For HDL-C, the unrestricted allele score (48 SNPs) was associated with CHD (OR: 0.53; 95% CI: 0.40, 0.70), per 1 mmol/L higher HDL-C, but neither the restricted allele score (19 SNPs; OR: 0.91; 95% CI: 0.42, 1.98) nor the unrestricted HDL-C allele score adjusted for triglycerides, LDL-C, or statin use (OR: 0.81; 95% CI: 0.44, 1.46) showed a robust association. For triglycerides, the unrestricted allele score (67 SNPs) and the restricted allele score (27 SNPs) were both associated with CHD (OR: 1.62; 95% CI: 1.24, 2.11 and 1.61; 95% CI: 1.00, 2.59, respectively) per 1-log unit increment. However, the unrestricted triglyceride score adjusted for HDL-C, LDL-C, and statin use gave an OR for CHD of 1.01 (95% CI: 0.59, 1.75). Conclusion The genetic findings support a causal effect of triglycerides on CHD risk, but a causal role for HDL-C, though possible, remains less certain.

Causal effects of body mass index on cardiometabolic traits and events: A Mendelian randomization analysis

Elevated body mass index (BMI) associates with cardiometabolic traits on observational analysis, yet the underlying causal relationships remain unclear. We conducted Mendelian randomization analyses by using a genetic score (GS) comprising 14 BMI-associated SNPs from a recent discovery analysis to investigate the causal role of BMI in cardiometabolic traits and events. We used eight population-based cohorts, including 34,538 European-descent individuals (4,407 type 2 diabetes (T2D), 6,073 coronary heart disease (CHD), and 3,813 stroke cases). A 1 kg/m(2) genetically elevated BMI increased fasting glucose (0.18 mmol/l; 95% confidence interval (CI) = 0.12-0.24), fasting insulin (8.5%; 95% CI = 5.9-11.1), interleukin-6 (7.0%; 95% CI = 4.0-10.1), and systolic blood pressure (0.70 mmHg; 95% CI = 0.24-1.16) and reduced high-density lipoprotein cholesterol (-0.02 mmol/l; 95% CI = -0.03 to -0.01) and low-density lipoprotein cholesterol (LDL-C; -0.04 mmol/l; 95% CI = -0.07 to -0.01). Observational and causal estimates were directionally concordant, except for LDL-C. A 1 kg/m(2) genetically elevated BMI increased the odds of T2D (odds ratio [OR] = 1.27; 95% CI = 1.18-1.36) but did not alter risk of CHD (OR 1.01; 95% CI = 0.94-1.08) or stroke (OR = 1.03; 95% CI = 0.95-1.12). A meta-analysis incorporating published studies reporting 27,465 CHD events in 219,423 individuals yielded a pooled OR of 1.04 (95% CI = 0.97-1.12) per 1 kg/m(2) increase in BMI. In conclusion, we identified causal effects of BMI on several cardiometabolic traits; however, whether BMI causally impacts CHD risk requires further evidence.

Advances in Genomic Analysis of Stroke. What Have We Learned and Where Are We Headed

As a result of technological advances, the genomic analysis of stroke has shifted from candidate gene association studies to genome-wide association studies (GWAS). Agnostic GWAS evaluate up to 90% of common genetic variation in a single experiment, creating an improved framework for identifying novel genetic leads for biochemical and cellular mechanisms underlying stroke. Given the ubiquity of the GWAS approach, it has become essential for stroke researchers and clinicians to be able to interpret GWAS results. Thus, we review the basic elements of design, methods, presentation, and interpretation of GWAS in the context of stroke research. In 8 recent stroke GWAS reports, no single locus has been identified in 2 GWAS at a genome-wide level of significance. Additionally, no significant association signal between stroke and a locus with previous evidence from candidate gene studies of stroke has been identified yet. Some caveats of the approach and future directions for stroke genomics are discussed, including the use of intermediate phenotypes, Mendelian randomization, phenomics, and deep resequencing. Intelligent, appropriately powered, multidisciplinary studies incorporating knowledge from clinical medicine, epidemiology, genetics, and molecular biology will be required to fully characterize the genomic contributors to stroke.

Comprehensive Analysis of Genomic Variation in the LPA Locus And Its Relationship to Plasma Lipoprotein(a) in South Asians, Chinese and European Caucasians

Background—Functional copy number variation in the apolipoprotein(a) gene (LPA) underlies a variable number of protein kringle domains repeated in tandem in the lipoprotein(a) [Lp(a)] particle. Genomic analysis of LPA, including both single-nucleotide polymorphisms (SNPs) and kringle IV type 2 (KIV-2) copy number, has yet to be performed. Methods and Results—First, we genotyped 49 SNPs within 100 kb of LPA in a multiethnic sample comprising South Asians (n=330), Chinese (n=304), and European Caucasians (n=272). Second, using quantitative polymerase chain reaction, we estimated the KIV-2 copy number in each sample. European Caucasians had the lowest KIV-2 copy number but displayed the strongest correlation between KIV-2 copy number and plasma Lp(a) concentration (rs=−0.31, P=4.2×10−7). SNP rs10455872, only prevalent in European Caucasians, was strongly associated with both plasma Lp(a) concentration (P=4.2×10−29) and KIV-2 copy number (P=7.2×10−5). LPA SNP rs6415084, within the same haplotype block as the KIV-2 variation, was significantly associated with both Lp(a) concentration and KIV-2 copy number in the same direction in all 3 ethnicities [Lp(a), P=5.3×10−7; KIV-2, P=2.6×10−4]. SNPs and KIV-2 copy number together explain a larger proportion of variation in plasma Lp(a) concentrations in European Caucasians (36%) than in Chinese (27%) or South Asians (21%). Conclusions—LPA SNPs are in linkage disequilibrium with KIV-2 copy number, but KIV-2 copy number explains an increment in plasma Lp(a) variation over SNPs alone. Thus, both SNPs and KIV-2 copy number should be included in future genetic epidemiology studies of Lp(a).

Gene-centric meta-analyses of 108 912 individuals confirm known body mass index loci and reveal three novel signals

Recent genetic association studies have made progress in uncovering components of the genetic architecture of the body mass index (BMI). We used the ITMAT-Broad-Candidate Gene Association Resource (CARe) (IBC) array comprising up to 49 320 single nucleotide polymorphisms (SNPs) across ~2100 metabolic and cardiovascular-related loci to genotype up to 108 912 individuals of European ancestry (EA), African-Americans, Hispanics and East Asians, from 46 studies, to provide additional insight into SNPs underpinning BMI. We used a five-phase study design: Phase I focused on meta-analysis of EA studies providing individual level genotype data; Phase II performed a replication of cohorts providing summary level EA data; Phase III meta-analyzed results from the first two phases; associated SNPs from Phase III were used for replication in Phase IV; finally in Phase V, a multi-ethnic meta-analysis of all samples from four ethnicities was performed. At an array-wide significance (P < 2.40E-06), we identify novel BMI associations in loci translocase of outer mitochondrial membrane 40 homolog (yeast) - apolipoprotein E - apolipoprotein C-I (TOMM40-APOE-APOC1) (rs2075650, P = 2.95E-10), sterol regulatory element binding transcription factor 2 (SREBF2, rs5996074, P = 9.43E-07) and neurotrophic tyrosine kinase, receptor, type 2 [NTRK2, a brain-derived neurotrophic factor (BDNF) receptor gene, rs1211166, P = 1.04E-06] in the Phase IV meta-analysis. Of 10 loci with previous evidence for BMI association represented on the IBC array, eight were replicated, with the remaining two showing nominal significance. Conditional analyses revealed two independent BMI-associated signals in BDNF and melanocortin 4 receptor (MC4R) regions. Of the 11 array-wide significant SNPs, three are associated with gene expression levels in both primary B-cells and monocytes; with rs4788099 in SH2B adaptor protein 1 (SH2B1) notably being associated with the expression of multiple genes in cis. These multi-ethnic meta-analyses expand our knowledge of BMI genetics.

Gene-gene and gene-environment interactions: new insights into the prevention, detection and management of coronary artery disease

Despite the recent success of genome-wide association studies (GWASs) in identifying loci consistently associated with coronary artery disease (CAD), a large proportion of the genetic components of CAD and its metabolic risk factors, including plasma lipids, type 2 diabetes and body mass index, remain unattributed. Gene-gene and gene-environment interactions might produce a meaningful improvement in quantification of the genetic determinants of CAD. Testing for gene-gene and gene-environment interactions is thus a new frontier for large-scale GWASs of CAD. There are several anecdotal examples of monogenic susceptibility to CAD in which the phenotype was worsened by an adverse environment. In addition, small-scale candidate gene association studies with functional hypotheses have identified gene-environment interactions. For future evaluation of gene-gene and gene-environment interactions to achieve the same success as the single gene associations reported in recent GWASs, it will be important to pre-specify agreed standards of study design and statistical power, environmental exposure measurement, phenomic characterization and analytical strategies. Here we discuss these issues, particularly in relation to the investigation and potential clinical utility of gene-gene and gene-environment interactions in CAD.

An Increased Burden of Common and Rare Lipid-Associated Risk Alleles Contributes to the Phenotypic Spectrum of Hypertriglyceridemia

Objective—Earlier studies have suggested that a common genetic architecture underlies the clinically heterogeneous polygenic Fredrickson hyperlipoproteinemia (HLP) phenotypes defined by hypertriglyceridemia (HTG). Here, we comprehensively analyzed 504 HLP-HTG patients and 1213 normotriglyceridemic controls and confirmed that a spectrum of common and rare lipid-associated variants underlies this heterogeneity. Methods and Results—First, we demonstrated that genetic determinants of plasma lipids and lipoproteins, including common variants associated with plasma triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) from the Global Lipids Genetics Consortium were associated with multiple HLP-HTG phenotypes. Second, we demonstrated that weighted risk scores composed of common TG-associated variants were distinctly increased across all HLP-HTG phenotypes compared with controls; weighted HDL-C and LDL-C risk scores were also increased, although to a less pronounced degree with some HLP-HTG phenotypes. Interestingly, decomposition of HDL-C and LDL-C risk scores revealed that pleiotropic variants (those jointly associated with TG) accounted for the greatest difference in HDL-C and LDL-C risk scores. The APOE E2/E2 genotype was significantly overrepresented in HLP type 3 versus other phenotypes. Finally, rare variants in 4 genes accumulated equally across HLP-HTG phenotypes. Conclusion—HTG susceptibility and phenotypic heterogeneity are both influenced by accumulation of common and rare TG-associated variants.

Excess of Rare Variants in Non–Genome-Wide Association Study Candidate Genes in Patients With Hypertriglyceridemia

Background— Rare variant accumulation studies can implicate genes in disease susceptibility when a significant burden is observed in patients versus control subjects. Such analyses might be particularly useful for candidate genes that are selected based on experiments other than genome-wide association studies (GWAS). We sought to determine whether rare variants in non-GWAS candidate genes identified from mouse models and human mendelian syndromes of hypertriglyceridemia (HTG) accumulate in patients with polygenic adult-onset HTG. Methods and Results— We resequenced protein coding regions of 3 genes with established roles (APOC2, GPIHBP1, LMF1) and 2 genes recently implicated (CREB3L3 and ZHX3) in TG metabolism. We identified 41 distinct heterozygous rare variants, including 29 singleton variants, in the combined sample; in total, we observed 47 rare variants in 413 HTG patients versus 16 in 324 control subjects (odds ratio=2.3; P=0.0050). Post hoc assessment of genetic burden in individual genes using 3 different tests suggested that the genetic burden was most prominent in the established genes LMF1 and APOC2, and also in the recently identified CREB3L3 gene. Conclusions— These extensive resequencing studies show a significant accumulation of rare genetic variants in non-GWAS candidate genes among patients with polygenic HTG, and indicate the importance of testing specific hypotheses in large-scale resequencing studies.

Temtamy Preaxial Brachydactyly Syndrome Is Caused by Loss-of-Function Mutations in Chondroitin Synthase 1, a Potential Target of BMP Signaling

Altered Bone Morphogenetic Protein (BMP) signaling leads to multiple developmental defects, including brachydactyly and deafness. Here we identify chondroitin synthase 1 (CHSY1) as a potential mediator of BMP effects. We show that loss of human CHSY1 function causes autosomal-recessive Temtamy preaxial brachydactyly syndrome (TPBS), mainly characterized by limb malformations, short stature, and hearing loss. After mapping the TPBS locus to chromosome 15q26-qterm, we identified causative mutations in five consanguineous TPBS families. In zebrafish, antisense-mediated chsy1 knockdown causes defects in multiple developmental processes, some of which are likely to also be causative in the etiology of TPBS. In the inner ears of zebrafish larvae, chsy1 is expressed similarly to the BMP inhibitor dan and in a complementary fashion to bmp2b. Furthermore, unrestricted Bmp2b signaling or loss of Dan activity leads to reduced chsy1 expression and, during epithelial morphogenesis, defects similar to those that occur upon Chsy1 inactivation, indicating that Bmp signaling affects inner-ear development by repressing chsy1. In addition, we obtained strikingly similar zebrafish phenotypes after chsy1 overexpression, which might explain why, in humans, brachydactyly can be caused by mutations leading either to loss or to gain of BMP signaling.