Evidence of linkage of HDL level variation to APOC3 in two samples with different ascertainment

Combined hyperlipidemia: familial but not (usually) monogenic

PURPOSE OF REVIEW

Combined hyperlipidemia (CHL) is a complex phenotype that is commonly encountered clinically and is often associated with the expression of early heart disease. The affixed adjective 'familial' gives the impression that the trait is monogenic, like familial hypercholesterolemia. But despite significant efforts, genetic studies have yielded little evidence of single gene determinants of CHL.

RECENT FINDINGS

Sophisticated linkage studies suggest that individual lipid components of the CHL phenotype - such as elevated LDL and triglyceride - each have several determinants that segregate independently in families. Furthermore, DNA sequencing shows that rare large-effect variants in genes such as LDL receptor (LDLR) and lipoprotein lipase are found in some CHL patients, explaining the elevated LDL cholesterol and triglyceride components, respectively. In addition, multiple common small-effect lipid-altering variants accumulate in an individual's genome, raising the LDL cholesterol and/or triglyceride components by multiple mechanisms. Finally, secondary factors, such as poor diet, obesity,fatty liver or diabetes further modulate the expression of the biochemically defined CHL phenotype.

SUMMARY

Given the current state of genetic understanding, CHL may be best conceptualized as a syndrome with common clinical presentation but multigenic causes, similar to other common conditions such as type 2 diabetes.

Joint linkage and association analysis with exome sequence data implicates SLC25A40 in hypertriglyceridemia

Hypertriglyceridemia (HTG) is a heritable risk factor for cardiovascular disease. Investigating the genetics of HTG may identify new drug targets. There are ~35 known single-nucleotide variants (SNVs) that explain only ~10% of variation in triglyceride (TG) level. Because of the genetic heterogeneity of HTG, a family study design is optimal for identification of rare genetic variants with large effect size because the same mutation can be observed in many relatives and cosegregation with TG can be tested. We considered HTG in a five-generation family of European American descent (n = 121), ascertained for familial combined hyperlipidemia. By using Bayesian Markov chain Monte Carlo joint oligogenic linkage and association analysis, we detected linkage to chromosomes 7 and 17. Whole-exome sequence data revealed shared, highly conserved, private missense SNVs in both SLC25A40 on chr7 and PLD2 on chr17. Jointly, these SNVs explained 49% of the genetic variance in TG; however, only the SLC25A40 SNV was significantly associated with TG (p = 0.0001). This SNV, c.374A>G, causes a highly disruptive p.Tyr125Cys substitution just outside the second helical transmembrane region of the SLC25A40 inner mitochondrial membrane transport protein. Whole-gene testing in subjects from the Exome Sequencing Project confirmed the association between TG and SLC25A40 rare, highly conserved, coding variants (p = 0.03). These results suggest a previously undescribed pathway for HTG and illustrate the power of large pedigrees in the search for rare, causal variants.

The genetics of familial combined hyperlipidaemia

Almost 40 years after the first description of familial combined hyperlipidaemia (FCHL) as a discrete entity, the genetic and metabolic basis of this prevalent disease has yet to be fully unveiled. In general, two strategies have been applied to elucidate its complex genetic background, the candidate-gene and the linkage approach, which have yielded an extensive list of genes associated with FCHL or its related traits, with a variable degree of scientific evidence. Some genes influence the FCHL phenotype in many pedigrees, whereas others are responsible for the affected state in only one kindred, thereby adding to the genetic and phenotypic heterogeneity of FCHL. This Review outlines the individual genes that have been described in FCHL and how these genes can be incorporated into the current concept of metabolic pathways resulting in FCHL: adipose tissue dysfunction, hepatic fat accumulation and overproduction, disturbed metabolism and delayed clearance of apolipoprotein-B-containing particles. Genes that affect metabolism and clearance of plasma lipoprotein particles have been most thoroughly studied. The adoption of new traits, in addition to the classic plasma lipid traits, could aid in the identification of new genes implicated in other pathways in FCHL. Moreover, systems genetic analysis, which integrates genetic polymorphisms with data on gene expression levels, lipidomics or metabolomics, will attribute functions to genetic variants in addition to revealing new genes.

Linkage and association of phospholipid transfer protein activity to LASS4

Phospholipid transfer protein activity (PLTPa) is associated with insulin levels and has been implicated in atherosclerotic disease in both mice and humans. Variation at the PLTP structural locus on chromosome 20 explains some, but not all, heritable variation in PLTPa. In order to detect quantitative trait loci (QTLs) elsewhere in the genome that affect PLTPa, we performed both oligogenic and single QTL linkage analysis on four large families (n = 227 with phenotype, n = 330 with genotype, n = 462 total), ascertained for familial combined hyperlipidemia. We detected evidence of linkage between PLTPa and chromosome 19p (lod = 3.2) for a single family and chromosome 2q (lod = 2.8) for all families. Inclusion of additional marker and exome sequence data in the analysis refined the linkage signal on chromosome 19 and implicated coding variation in LASS4, a gene regulated by leptin that is involved in ceramide synthesis. Association between PLTPa and LASS4 variation was replicated in the other three families (P = 0.02), adjusting for pedigree structure. To our knowledge, this is the first example for which exome data was used in families to identify a complex QTL that is not the structural locus.

Genome-scan for IQ discrepancy in autism: evidence for loci on chromosomes 10 and 16

Performance IQ (PIQ) greater than verbal IQ (VIQ) is often observed in studies of the cognitive abilities of autistic individuals. This characteristic is correlated with social and communication impairments, key parts of the autism diagnosis. We present the first genetic analyses of IQ discrepancy (PIQ-VIQ) as an autism-related phenotype. We performed genome-wide joint linkage and segregation analyses on 287 multiplex families, using a Markov chain Monte Carlo approach. Genetic data included a genome-scan of 387 micro-satellite markers in 210 families augmented with additional markers added in a subset of families. Empirical P values were calculated for five interesting regions. Linkage analysis identified five chromosomal regions with substantial regional evidence of linkage; 10p12 [P = 0.001; genome-wide (gw) P = 0.05], 16q23 (P = .015; gw P = 0.53), 2p21 (P = 0.03, gw P = 0.78), 6q25 (P = 0.047, gw P = 0.91) and 15q23-25 (P = 0.053, gw P = 0.93). The location of the chromosome 10 linkage signal coincides with a region noted in a much earlier genome-scan for autism, and the chromosome 16 signal coincides exactly with a linkage signal for non-word repetition in specific language impairment. This study provides strong evidence for a QTL influencing IQ discrepancy in families with autistic individuals on chromosome 10, and suggestive evidence for a QTL on chromosome 16. The location of the chromosome 16 signal suggests a candidate gene, CDH13, a T-cadherin expressed in the brain, which has been implicated in previous SNP studies of autism and ADHD.

Evidence for three loci modifying age-at-onset of Alzheimer's disease in early-onset PSEN2 families

Families with early-onset Alzheimer's disease (AD) sharing a single PSEN2 mutation exhibit a wide range of age-at-onset, suggesting that modifier loci segregate within these families. While APOE is known to be an age-at-onset modifier, it does not explain all of this variation. We performed a genome scan within nine such families for loci influencing age-at-onset, while simultaneously controlling for variation in the primary PSEN2 mutation (N141I) and APOE. We found significant evidence of linkage between age-at-onset and chromosome 1q23.3 (P < 0.001) when analysis included all families, and to chromosomes 1q23.3 (P < 0.001), 17p13.2 (P = 0.0002), 7q33 (P = 0.017), and 11p14.2 (P = 0.017) in a single large pedigree. Simultaneous analysis of these four chromosomes maintained strong evidence of linkage to chromosomes 1q23.3 and 17p13.2 when all families were analyzed, and to chromosomes 1q23.3, 7q33, and 17p13.2 within the same single pedigree. Inclusion of major gene covariates proved essential to detect these linkage signals, as all linkage signals dissipated when PSEN2 and APOE were excluded from the model. The four chromosomal regions with evidence of linkage all coincide with previous linkage signals, associated SNPs, and/or candidate genes identified in independent AD study populations. This study establishes several candidate regions for further analysis and is consistent with an oligogenic model of AD risk and age-at-onset. More generally, this study also demonstrates the value of searching for modifier loci in existing datasets previously used to identify primary causal variants for complex disease traits.

A gene frequency model for QTL mapping using Bayesian inference

Background

Information for mapping of quantitative trait loci (QTL) comes from two sources: linkage disequilibrium (non-random association of allele states) and cosegregation (non-random association of allele origin). Information from LD can be captured by modeling conditional means and variances at the QTL given marker information. Similarly, information from cosegregation can be captured by modeling conditional covariances. Here, we consider a Bayesian model based on gene frequency (BGF) where both conditional means and variances are modeled as a function of the conditional gene frequencies at the QTL. The parameters in this model include these gene frequencies, additive effect of the QTL, its location, and the residual variance. Bayesian methodology was used to estimate these parameters. The priors used were: logit-normal for gene frequencies, normal for the additive effect, uniform for location, and inverse chi-square for the residual variance. Computer simulation was used to compare the power to detect and accuracy to map QTL by this method with those from least squares analysis using a regression model (LSR).

Results

To simplify the analysis, data from unrelated individuals in a purebred population were simulated, where only LD information contributes to map the QTL. LD was simulated in a chromosomal segment of 1 cM with one QTL by random mating in a population of size 500 for 1000 generations and in a population of size 100 for 50 generations. The comparison was studied under a range of conditions, which included SNP density of 0.1, 0.05 or 0.02 cM, sample size of 500 or 1000, and phenotypic variance explained by QTL of 2 or 5%. Both 1 and 2-SNP models were considered. Power to detect the QTL for the BGF, ranged from 0.4 to 0.99, and close or equal to the power of the regression using least squares (LSR). Precision to map QTL position of BGF, quantified by the mean absolute error, ranged from 0.11 to 0.21 cM for BGF, and was better than the precision of LSR, which ranged from 0.12 to 0.25 cM.

Conclusions

In conclusion given a high SNP density, the gene frequency model can be used to map QTL with considerable accuracy even within a 1 cM region.

Joint linkage and segregation analysis under multiallelic trait inheritance: simplifying interpretations for complex traits

Identification of the genetic basis of common traits may be hindered by underlying complex genetic architectures that are inadequately captured by existing models, including both multiallelic and multilocus modes of inheritance (MOI). One useful approach for localizing genes underlying continuous complex traits is the joint oligogenic linkage and segregation analysis implemented in the package Loki. The method uses reversible jump Markov chain Monte Carlo to eliminate the need to prespecify the number of quantitative trait loci (QTLs) in the trait model, thus providing posterior distributions for the number of QTLs in a Bayesian framework. The current implementation assumes QTLs are diallelic, and therefore can overestimate the number of linked QTLs in the presence of a multiallelic QTL. To address the possibility of multiple alleles, we extended the QTL model to allow for a variable number of additive alleles at each locus. Application to simulated data shows that, under a diallelic MOI, the multiallelic and diallelic analysis models give similar results. Under a multiallelic MOI, the multiallelic analysis model provides better mixing and improved convergence, and leads to a more accurate estimate of the underlying trait MOI and model parameter values, than does the diallelic model. Application to real data shows the multiallelic model results in fewer estimated linked QTLs and that the predominant QTL model is similar to one of two predominant models estimated from the diallelic analysis. Our results indicate that use of a multiallelic analysis model can lead to better understanding of the genetic architecture underlying complex traits.

Genetic causes of high and low serum HDL-cholesterol

Plasma levels of HDL cholesterol (HDL-C) have a strong inherited basis with heritability estimates of 40-60%. The well-established inverse relationship between plasma HDL-C levels and the risk of coronary artery disease (CAD) has led to an extensive search for genetic factors influencing HDL-C concentrations. Over the past 30 years, candidate gene, genome-wide linkage, and most recently genome-wide association (GWA) studies have identified several genetic variations for plasma HDL-C levels. However, the functional role of several of these variants remains unknown, and they do not always correlate with CAD. In this review, we will first summarize what is known about HDL metabolism, monogenic disorders associated with both low and high HDL-C levels, and candidate gene studies. Then we will focus this review on recent genetic findings from the GWA studies and future strategies to elucidate the remaining substantial proportion of HDL-C heritability. Comprehensive investigation of the genetic factors conferring to low and high HDL-C levels using integrative approaches is important to unravel novel pathways and their relations to CAD, so that more effective means of diagnosis, treatment, and prevention will be identified.

Linkage and association analyses identify a candidate region for apoB level on chromosome 4q32.3 in FCHL families

Familial combined hyperlipidemia (FCHL) is a complex trait leading to cardiovascular disease (CVD) risk. Elevated levels and size of apolipoprotein B (apoB) and low-density lipoprotein (LDL) are associated with FCHL, which is genetically heterogeneous and is likely caused by rare variants. We carried out a linkage-based genome scan of four large FCHL pedigrees for apoB level that is independent of LDL: apoB level that is adjusted for LDL level and size. Follow-up included SNP genotyping in the region with the strongest evidence of linkage. Several regions with the evidence of linkage in individual pedigrees support the rare variant model. Evidence of linkage was strongest on chromosome 4q, with multipoint analysis in one pedigree giving LOD = 3.1 with a parametric model, and a log Bayes Factor = 1.5 from a Bayesian oligogenic approach. Of the 293 SNPs spanning the implicated region on 4q, rs6829588 completely explained the evidence of linkage. This SNP accounted for 39% of the apoB phenotypic variance, with heterozygotes for this SNP having a trait value that was approximately 30% higher than that of the high-frequency homozygote, thus identifying and considerably refining a strong candidate region. These results illustrate the advantage of using large pedigrees in the search for rare variants: reduced genetic heterogeneity within single pedigrees coupled with the large number of individuals segregating otherwise-rare single variants leads to high power to implicate such variants.

Fine mapping and association studies of a high-density lipoprotein cholesterol linkage region on chromosome 16 in French-Canadian subjects

Low levels of high-density lipoprotein cholesterol (HDL-C) are an independent risk factor for cardiovascular disease. To identify novel genetic variants that contribute to HDL-C, we performed genome-wide scans and quantitative association studies in two study samples: a Quebec-wide study consisting of 11 multigenerational families and a study of 61 families from the Saguenay-Lac St-Jean (SLSJ) region of Quebec. The heritability of HDL-C in these study samples was 0.73 and 0.49, respectively. Variance components linkage methods identified a LOD score of 2.61 at 98 cM near the marker D16S515 in Quebec-wide families and an LOD score of 2.96 at 86 cM near the marker D16S2624 in SLSJ families. In the Quebec-wide sample, four families showed segregation over a 25.5-cM (18 Mb) region, which was further reduced to 6.6 Mb with additional markers. The coding regions of all genes within this region were sequenced. A missense variant in CHST6 segregated in four families and, with additional families, we observed a P value of 0.015 for this variant. However, an association study of this single-nucleotide polymorphism (SNP) in unrelated Quebec-wide samples was not significant. We also identified an SNP (rs11646677) in the same region, which was significantly associated with a low HDL-C (P=0.016) in the SLSJ study sample. In addition, RT-PCR results from cultured cells showed a significant difference in the expression of CHST6 and KIAA1576, another gene in the region. Our data constitute additional evidence for a locus on chromosome 16q23-24 that affects HDL-C levels in two independent French-Canadian studies.

Association of apolipoprotein A1-C3 gene cluster polymorphisms with gallstone disease

INTRODUCTION

Genetic polymorphisms in apolipoprotein genes may be associated with alteration in lipid profile and susceptibility to gallstone disease.

AIM

To determine the association between apolipoprotein A1 (APOA1) -75 guanine [G] to adenine [A] and +83/84 M2(+/-), MspI) and apolipoprotein C3 (APOC3) (SstI) polymorphisms with gallstone disease.

METHODS

MspI polymorphisms of the APOA1 gene and SstI polymorphisms of APOC3 were analyzed in DNA samples of 214 gallstone patients and 322 age- and sex-matched healthy controls. All statistical analyses were performed using SPSS version 11.5 (SPSS, USA) and Arlequin version 2.0 (Arlequin, Switzerland).

RESULTS

The APOA1 -75 G/A polymorphism was significantly associated with gallstone disease. Patients with the GG genotype (P=0.015) and G allele carriers (P=0.004) had a significantly higher risk of gallstone disease (1.087-fold and 1.561-fold, respectively), whereas patients with AA genotypes (P=0.011) and A allele carriers (P=0.004) were protected (OR 0.230 and 0.641, respectively) against gallstone disease. APOA1 +83 M2(+/-) and APOC3 SstI polymorphisms were not associated with gallstone disease. Case-control analysis of haplotypes showed a significant association in males only. G-M2(+)-S1 conferred risk for gallstone disease (P=0.036; OR 1.593, 95% CI 1.029 to 2.464), while A-M2(+)-S1 was protective (P=0.002; OR 0.370, 95% CI 0.197 to 0.695) against gallstone disease. In APOA1(-75)-APOA1(+83) bilocus haplotypes, G-M2(+) was associated (P=0.0001) with very high risk (OR 3.173, 95% CI 1.774 to 5.674) for gallstone disease in males only. APOA1(-75)-APOC3(SstI) haplotypes also showed significant association while APOA1(+83)-APOC3(SstI) haplotypes showed no association with gallstone disease.

CONCLUSIONS

The APOA1 -75 G/A polymorphism is associated with gallstone disease and shows sex-specific differences. On the other hand, APOA1 M2(+/-) and APOC3 SstI polymorphisms may not be associated with gallstone disease. Haplotype analysis is a better predictor of risk for gallstone disease.

Results of a SNP genome screen in a large Costa Rican pedigree segregating for severe bipolar disorder

We have ascertained in the Central Valley of Costa Rica a new kindred (CR201) segregating for severe bipolar disorder (BP-I). The family was identified by tracing genealogical connections among eight persons initially independently ascertained for a genome wide association study of BP-I. For the genome screen in CR201, we trimmed the family down to 168 persons (82 of whom are genotyped), containing 25 individuals with a best-estimate diagnosis of BP-I. A total of 4,690 SNP markers were genotyped. Analysis of the data was hampered by the size and complexity of the pedigree, which prohibited using exact multipoint methods on the entire kindred. Two-point parametric linkage analysis, using a conservative model of transmission, produced a maximum LOD score of 2.78 on chromosome 6, and a total of 39 loci with LOD scores >1.0. Multipoint parametric and non-parametric linkage analysis was performed separately on four sections of CR201, and interesting (nominal P-value from either analysis <0.01), although not statistically significant, regions were highlighted on chromosomes 1, 2, 3, 12, 16, 19, and 22, in at least one section of the pedigree, or when considering all sections together. The difficulties of analyzing genome wide SNP data for complex disorders in large, potentially informative, kindreds are discussed.

Genetics of familial combined hyperlipidemia

PURPOSE OF REVIEW

To provide an overview of recent advances that have defined the first putative genes behind familial combined hyperlipidemia, the most common genetic dyslipidemia and a major risk factor for early coronary heart disease.

RECENT FINDINGS

The first locus for familial combined hyperlipidemia on 1q21-23 revealed a gene encoding a transcription factor critical in lipid and glucose metabolism, USF1. All the associated variants represent noncoding single nucleotide polymorphisms, one of which affects the binding site of nuclear proteins with a putative effect on transcript levels of USF1. Transcript analyses of fat biopsies have exposed risk-allele related changes in the downstream genes. Another recent clue to the molecular pathogenesis of familial combined hyperlipidemia is the association of the high triglyceride trait with the APOA5 gene, located on 11q. More familial combined hyperlipidemia genes are expected to be found, since linkage evidence exists for additional loci on 16q24 and 20q12-q13.1.

SUMMARY

Genetic research of familial combined hyperlipidemia families has revealed several linked loci guiding to susceptibility genes. The USF1 transcription factor is the major gene underlying the 1q21-23 linkage. Modifying genes, especially influencing the high triglyceride trait, include APOC3 and APOA5, the latter representing a downstream target of USF1 and implying a USF1-dependent pathway in the molecular pathogenesis of dyslipidemias.

Genomewide scan for real-word reading subphenotypes of dyslexia: novel chromosome 13 locus and genetic complexity

Dyslexia is a common learning disability exhibited as a delay in acquiring reading skills despite adequate intelligence and instruction. Reading single real words (real-word reading, RWR) is especially impaired in many dyslexics. We performed a genome scan, using variance components (VC) linkage analysis and Bayesian Markov chain Monte Carlo (MCMC) joint segregation and linkage analysis, for three quantitative measures of RWR in 108 multigenerational families, with follow up of the strongest signals with parametric LOD score analyses. We used single-word reading efficiency (SWE) to assess speed and accuracy of RWR, and word identification (WID) to assess accuracy alone. Adjusting SWE for WID provided a third measure of RWR efficiency. All three methods of analysis identified a strong linkage signal for SWE on chromosome 13q. Based on multipoint analysis with 13 markers we obtained a MCMC intensity ratio (IR) of 53.2 (chromosome-wide P < 0.004), a VC LOD score of 2.29, and a parametric LOD score of 2.94, based on a quantitative-trait model from MCMC segregation analysis (SA). A weaker signal for SWE on chromosome 2q occurred in the same location as a significant linkage peak seen previously in a scan for phonological decoding. MCMC oligogenic SA identified three models of transmission for WID, which could be assigned to two distinct linkage peaks on chromosomes 12 and 15. Taken together, these results indicate a locus for efficiency and accuracy of RWR on chromosome 13, and a complex model for inheritance of RWR accuracy with loci on chromosomes 12 and 15.

A novel Markov chain monte carlo approach for constructing accurate meiotic maps

Mapping markers from linkage data continues to be a task performed in many genetic epidemiological studies. Data collected in a study may be used to refine published map estimates and a study may use markers that do not appear in any published map. Furthermore, inaccuracies in meiotic maps can seriously bias linkage findings. To make best use of the available marker information, multilocus linkage analyses are performed. However, two computational issues greatly limit the number of markers currently mapped jointly; the number of candidate marker orders increases exponentially with marker number and computing exact multilocus likelihoods on general pedigrees is computationally demanding. In this article, a new Markov chain Monte Carlo (MCMC) approach that solves both these computational problems is presented. The MCMC approach allows many markers to be mapped jointly, using data observed on general pedigrees with unobserved individuals. The performance of the new mapping procedure is demonstrated through the analysis of simulated and real data. The MCMC procedure performs extremely well, even when there are millions of candidate orders, and gives results superior to those of CRI-MAP.

Genome scan for quantitative trait loci influencing HDL levels: evidence for multilocus inheritance in familial combined hyperlipidemia

Several genome scans in search of high-density lipoprotein (HDL) quantitative trait loci (QTLs) have been performed. However, to date the actual identification of genes implicated in the regulation of common forms of HDL abnormalities remains unsuccessful. This may be due, in part, to the oligogenic and multivariate nature of HDL regulation, and potentially, pleiotropy affecting HDL and other lipid-related traits. Using a Bayesian Markov Chain Monte Carlo (MCMC) approach, we recently provided evidence of linkage of HDL level variation to the APOA1-C3-A4-A5 gene complex, in familial combined hyperlipidemia pedigrees, with an estimated number of two to three large QTLs remaining to be identified. We also presented results consistent with pleiotropy affecting HDL and triglycerides at the APOA1-C3-A4-A5 gene complex. Here we use the same MCMC analytic strategy, which allows for oligogenic trait models, as well as simultaneous incorporation of covariates, in the context of multipoint analysis. We now present results from a genome scan in search for the additional HDL QTLs in these pedigrees. We provide evidence of linkage for additional HDL QTLs on chromosomes 3p14 and 13q32, with results on chromosome 3 further supported by maximum parametric and variance component LOD scores of 3.0 and 2.6, respectively. Weaker evidence of linkage was also obtained for 7q32, 12q12, 14q31-32 and 16q23-24.

Joint oligogenic segregation and linkage analysis using bayesian Markov chain Monte Carlo methods

One of the most challenging areas in human genetics is the dissection of quantitative traits. In this context, the efficient use of available data is important, including, when possible, use of large pedigrees and many markers for gene mapping. In addition, methods that jointly perform linkage analysis and estimation of the trait model are appealing because they combine the advantages of a model-based analysis with the advantages of methods that do not require prespecification of model parameters for linkage analysis. Here we review a Markov chain Monte Carlo approach for such joint linkage and segregation analysis, which allows analysis of oligogenic traits in the context of multipoint linkage analysis of large pedigrees. We provide an outline for practitioners of the salient features of the method, interpretation of the results, effect of violation of assumptions, and an example analysis of a two-locus trait to illustrate the method.

Quantitative founder-effect analysis of French Canadian families identifies specific loci contributing to metabolic phenotypes of hypertension

The Saguenay-Lac St-Jean population of Quebec is relatively isolated and has genealogical records dating to the 17th-century French founders. In 120 extended families with at least one sib pair affected with early-onset hypertension and/or dyslipidemia, we analyzed the genetic determinants of hypertension and related cardiovascular and metabolic conditions. Variance-components linkage analysis revealed 46 loci after 100,000 permutations. The most prominent clusters of overlapping quantitative-trait loci were on chromosomes 1 and 3, a finding supported by principal-components and bivariate analyses. These genetic determinants were further tested by classifying families by use of LOD score density analysis for each measured phenotype at every 5 cM. Our study showed the founder effect over several generations and classes of living individuals. This quantitative genealogical approach supports the notion of the ancestral causality of traits uniquely present and inherited in distinct family classes. With the founder effect, traits determined within population subsets are measurably and quantitatively transmitted through generational lineage, with a precise component contributing to phenotypic variance. These methods should accelerate the uncovering of causal haplotypes in complex diseases such as hypertension and metabolic syndrome.

Genetic determinants of low high-density lipoprotein cholesterol

PURPOSE OF REVIEW

High-density lipoprotein cholesterol (HDL-C) has been well established as an inverse predictor of coronary heart disease (CHD), and in recent years, investigations have focused on the genetic regulation of high-density lipoprotein. Although numerous candidate genes contribute to the low HDL-C phenotype, their impact on CHD is heterogeneous, reflecting diverse gene-gene interactions and gene-environmental relationships. This review summarizes recent data involving HDL regulatory genes and their role in atherothrombosis.

RECENT FINDINGS

The primary genetic determinants associated with relative HDL-C deficiency states are the ATP binding cassette protein, ABCA1; apolipoprotein (APO) A1; and lecithin cholesteryl acyl transferase. Other potentially important candidates invoked in low HDL-C syndromes in humans include APOC3, lipoprotein lipase, sphingomyelin phosphodiesterase 1, and glucocerebrosidase. Molecular variation in ABCAI and APOAI and, in selected cases, lecithin cholesteryl acyl transferase deficiency have been associated with increased CHD, whereas two notable variants, APOAIMilano and APOAIParis, are associated with reduced risk.

SUMMARY

Low HDL-C syndromes have generally been correlated with an increased risk of CHD. However, single-gene abnormalities responsible for HDL-C deficiency states may have variable effects on atherothrombotic risk.

Differential Gene Expression of Blood-Derived Cell Lines in Familial Combined Hyperlipidemia

OBJECTIVES

The genetic background of familial combined hyperlipidemia (FCHL) is currently unclear. We propose transcriptional profiling as a complementary tool for its understanding. Two hypotheses were tested: the existence of a disease-specific modification of gene expression in FCHL and the detectability of such a transcriptional profile in blood derived cell lines.

METHODS AND RESULTS

We established lymphoblastic cell lines from FCHL patients and controls. The cells were cultured in fixed conditions and their basal expression profile was compared using microarrays; 166 genes were differentially expressed in FCHL-derived cell lines compared with controls, with enrichment in metabolism-related genes. Of note was the upregulation of EGR-1, previously found to be upregulated in the adipose tissue of FCHL patients, the upregulation of DCHR-7, the downregulation of LYPLA2, and the differential expression of several genes previously unrelated to FCHL. A cluster of potential EGR-1-regulated transcripts was also differentially expressed in FCHL cells.

CONCLUSIONS

Our data indicate that in FCHL, a disease-specific transcription profile is detectable in immortalized cell lines easily obtained from peripheral blood and provide complementary information to classical genetic approaches to FCHL and/or the metabolic syndrome.

Low-Density Lipoprotein Particle Size Loci in Familial Combined Hyperlipidemia

Objective— Low-density lipoprotein (LDL) size is associated with vascular disease and with familial combined hyperlipidemia (FCHL).

Methods and Results— We used logarithm of odds (lod) score and Bayesian Markov chain Monte Carlo (MCMC) linkage analysis methods to perform a 10-cM genome scan of LDL size, measured as peak particle diameter (PPD) and adjusted for age, sex, body mass index, and triglycerides in 4 large families with FCHL (n=185). We identified significant evidence of linkage to a chromosome 9p locus (multipoint lod max =3.70; MCMC intensity ratio [IR]=21) in a single family, and across all 4 families to chromosomes 16q23 (lod max =3.00; IR=43) near cholesteryl ester transfer protein ( CETP ) and to 11q22 (lod max =3.71; IR=120). Chromosome 14q24-31, a region with previous suggestive LDL PPD linkage evidence, yielded an IR of 71 but an lod max =1.79 in the combined families.

Conclusions— These results of significant evidence of linkage to 3 regions (9p, 16q, and 11q) and confirmatory support of previous reported linkage to 14q in large FCHL pedigrees demonstrate that LDL size is a trait influenced by multiple loci and illustrate the complementary use of lod score and MCMC methods in analysis of a complex trait.

Evidence for a novel late-onset Alzheimer disease locus on chromosome 19p13.2

Late-onset familial Alzheimer disease (LOFAD) is a genetically heterogeneous and complex disease for which only one locus, APOE, has been definitively identified. Difficulties in identifying additional loci are likely to stem from inadequate linkage analysis methods. Nonparametric methods suffer from low power because of limited use of the data, and traditional parametric methods suffer from limitations in the complexity of the genetic model that can be feasibly used in analysis. Alternative methods that have recently been developed include Bayesian Markov chain-Monte Carlo methods. These methods allow multipoint linkage analysis under oligogenic trait models in pedigrees of arbitrary size; at the same time, they allow for inclusion of covariates in the analysis. We applied this approach to an analysis of LOFAD on five chromosomes with previous reports of linkage. We identified strong evidence of a second LOFAD gene on chromosome 19p13.2, which is distinct from APOE on 19q. We also obtained weak evidence of linkage to chromosome 10 at the same location as a previous report of linkage but found no evidence for linkage of LOFAD age-at-onset loci to chromosomes 9, 12, or 21.