Multi-Trait Exome-Wide Association Study of Back Pain-Related Phenotypes.

Back pain (BP) is a major contributor to disability worldwide, with heritability estimated at 40-60%. However, less than half of the heritability is explained by common genetic variants identified by genome-wide association studies. More powerful methods and rare and ultra-rare variant analysis may offer additional insight. This study utilized exome sequencing data from the UK Biobank to perform a multi-trait gene-based association analysis of three BP-related phenotypes: chronic back pain, dorsalgia, and intervertebral disc disorder. We identified the SLC13A1 gene as a contributor to chronic back pain via loss-of-function (LoF) and missense variants. This gene has been previously detected in two studies. A multi-trait approach uncovered the novel FSCN3 gene and its impact on back pain through LoF variants. This gene deserves attention because it is only the second gene shown to have an effect on back pain due to LoF variants and represents a promising drug target for back pain therapy.

sumSTAAR: A flexible framework for gene-based association studies using GWAS summary statistics.

Gene-based association analysis is an effective gene-mapping tool. Many gene-based methods have been proposed recently. However, their power depends on the underlying genetic architecture, which is rarely known in complex traits, and so it is likely that a combination of such methods could serve as a universal approach. Several frameworks combining different gene-based methods have been developed. However, they all imply a fixed set of methods, weights and functional annotations. Moreover, most of them use individual phenotypes and genotypes as input data. Here, we introduce sumSTAAR, a framework for gene-based association analysis using summary statistics obtained from genome-wide association studies (GWAS). It is an extended and modified version of STAAR framework proposed by Li and colleagues in 2020. The sumSTAAR framework offers a wider range of gene-based methods to combine. It allows the user to arbitrarily define a set of these methods, weighting functions and probabilities of genetic variants being causal. The methods used in the framework were adapted to analyse genes with large number of SNPs to decrease the running time. The framework includes the polygene pruning procedure to guard against the influence of the strong GWAS signals outside the gene. We also present new improved matrices of correlations between the genotypes of variants within genes. These matrices estimated on a sample of 265,000 individuals are a state-of-the-art replacement of widely used matrices based on the 1000 Genomes Project data.

Gene-based association analysis identifies 190 genes affecting neuroticism.

Neuroticism is a personality trait, which is an important risk factor for psychiatric disorders. Recent genome-wide studies reported about 600 genes potentially influencing neuroticism. Little is known about the mechanisms of their action. Here, we aimed to conduct a more detailed analysis of genes that can regulate the level of neuroticism. Using UK Biobank-based GWAS summary statistics, we performed a gene-based association analysis using four sets of within-gene variants, each set possessing specific protein-coding properties. To guard against the influence of strong GWAS signals outside the gene, we used a specially designed procedure called "polygene pruning". As a result, we identified 190 genes associated with neuroticism due to the effect of within-gene variants rather than strong GWAS signals outside the gene. Thirty eight of these genes are new. Within all genes identified, we distinguished two slightly overlapping groups obtained from using protein-coding and non-coding variants. Many genes in the former group included potentially pathogenic variants. For some genes in the latter group, we found evidence of pleiotropy with gene expression. Using a bioinformatics analysis, we prioritized the neuroticism genes and showed that the genes that contribute to neuroticism through their within-gene variants are the most appropriate candidate genes.

Gene-based association tests using GWAS summary statistics.

MOTIVATION: A huge number of genome-wide association studies (GWAS) summary statistics freely available in databases provide a new material for gene-based association analysis aimed at identifying rare genetic variants. Only a few of the many popular gene-based methods developed for individual genotype and phenotype data are adapted for the practical use of the GWAS summary statistics as input. RESULTS: We analytically prove and numerically illustrate that all popular powerful methods developed for gene-based association analysis of individual phenotype and genotype data can be modified to utilize GWAS summary statistics. We have modified and implemented all of the popular methods, including burden and kernel machine-based tests, multiple and functional linear regression, principal components analysis and others, in the R package sumFREGAT. Using real summary statistics for coronary artery disease, we show that the new package is able to detect genes not found by the existing packages. AVAILABILITY AND IMPLEMENTATION: The R package sumFREGAT is freely and publicly available at: https://CRAN.R-project.org/package=sumFREGAT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A combined linkage, microarray and exome analysis suggests MAP3K11 as a candidate gene for left ventricular hypertrophy.

BACKGROUND: Electrocardiographic measures of left ventricular hypertrophy (LVH) are used as predictors of cardiovascular risk. We combined linkage and association analyses to discover novel rare genetic variants involved in three such measures and two principal components derived from them. METHODS: The study was conducted among participants from the Erasmus Rucphen Family Study (ERF), a Dutch family-based sample from the southwestern Netherlands. Variance components linkage analyses were performed using Merlin. Regions of interest (LOD > 1.9) were fine-mapped using microarray and exome sequence data. RESULTS: We observed one significant LOD score for the second principal component on chromosome 15 (LOD score = 3.01) and 12 suggestive LOD scores. Several loci contained variants identified in GWAS for these traits; however, these did not explain the linkage peaks, nor did other common variants. Exome sequence data identified two associated variants after multiple testing corrections were applied. CONCLUSIONS: We did not find common SNPs explaining these linkage signals. Exome sequencing uncovered a relatively rare variant in MAPK3K11 on chromosome 11 (MAF = 0.01) that helped account for the suggestive linkage peak observed for the first principal component. Conditional analysis revealed a drop in LOD from 2.01 to 0.88 for MAP3K11, suggesting that this variant may partially explain the linkage signal at this chromosomal location. MAP3K11 is related to the JNK pathway and is a pro-apoptotic kinase that plays an important role in the induction of cardiomyocyte apoptosis in various pathologies, including LVH.

Nonsynonymous Variation in NKPD1 Increases Depressive Symptoms in European Populations.

BACKGROUND: Despite high heritability, little success was achieved in mapping genetic determinants of depression-related traits by means of genome-wide association studies. METHODS: To identify genes associated with depressive symptomology, we performed a gene-based association analysis of nonsynonymous variation captured using exome-sequencing and exome-chip genotyping in a genetically isolated population from the Netherlands (n = 1999). Finally, we reproduced our significant findings in an independent population-based cohort (n = 1604). RESULTS: We detected significant association of depressive symptoms with a gene NKPD1 (p = 3.7 × 10-08). Nonsynonymous variants in the gene explained 0.9% of sex- and age-adjusted variance of depressive symptoms in the discovery study, which is translated into 3.8% of the total estimated heritability (h2 = 0.24). Significant association of depressive symptoms with NKPD1 was also observed (n = 1604; p = 1.5 × 10-03) in the independent replication sample despite little overlap with the discovery cohort in the set of nonsynonymous genetic variants observed in the NKPD1 gene. Meta-analysis of the discovery and replication studies improved the association signal (p = 1.0 × 10-09). CONCLUSIONS: Our study suggests that nonsynonymous variation in the gene NKPD1 affects depressive symptoms in the general population. NKPD1 is predicted to be involved in the de novo synthesis of sphingolipids, which have been implicated in the pathogenesis of depression.

A Combined Linkage and Exome Sequencing Analysis for Electrocardiogram Parameters in the Erasmus Rucphen Family Study.

Electrocardiogram (ECG) measurements play a key role in the diagnosis and prediction of cardiac arrhythmias and sudden cardiac death. ECG parameters, such as the PR, QRS, and QT intervals, are known to be heritable and genome-wide association studies of these phenotypes have been successful in identifying common variants; however, a large proportion of the genetic variability of these traits remains to be elucidated. The aim of this study was to discover loci potentially harboring rare variants utilizing variance component linkage analysis in 1547 individuals from a large family-based study, the Erasmus Rucphen Family Study (ERF). Linked regions were further explored using exome sequencing. Five suggestive linkage peaks were identified: two for QT interval (1q24, LOD = 2.63; 2q34, LOD = 2.05), one for QRS interval (1p35, LOD = 2.52) and two for PR interval (9p22, LOD = 2.20; 14q11, LOD = 2.29). Fine-mapping using exome sequence data identified a C > G missense variant (c.713C > G, p.Ser238Cys) in the FCRL2 gene associated with QT (rs74608430; P = 2.8 × 10-4, minor allele frequency = 0.019). Heritability analysis demonstrated that the SNP explained 2.42% of the trait's genetic variability in ERF (P = 0.02). Pathway analysis suggested that the gene is involved in cytosolic Ca2+ levels (P = 3.3 × 10-3) and AMPK stimulated fatty acid oxidation in muscle (P = 4.1 × 10-3). Look-ups in bioinformatics resources showed that expression of FCRL2 is associated with ARHGAP24 and SETBP1 expression. This finding was not replicated in the Rotterdam study. Combining the bioinformatics information with the association and linkage analyses, FCRL2 emerges as a strong candidate gene for QT interval.

Refining genome-wide linkage intervals using a meta-analysis of genome-wide association studies identifies loci influencing personality dimensions.

Personality traits are complex phenotypes related to psychosomatic health. Individually, various gene finding methods have not achieved much success in finding genetic variants associated with personality traits. We performed a meta-analysis of four genome-wide linkage scans (N=6149 subjects) of five basic personality traits assessed with the NEO Five-Factor Inventory. We compared the significant regions from the meta-analysis of linkage scans with the results of a meta-analysis of genome-wide association studies (GWAS) (N∼17 000). We found significant evidence of linkage of neuroticism to chromosome 3p14 (rs1490265, LOD=4.67) and to chromosome 19q13 (rs628604, LOD=3.55); of extraversion to 14q32 (ATGG002, LOD=3.3); and of agreeableness to 3p25 (rs709160, LOD=3.67) and to two adjacent regions on chromosome 15, including 15q13 (rs970408, LOD=4.07) and 15q14 (rs1055356, LOD=3.52) in the individual scans. In the meta-analysis, we found strong evidence of linkage of extraversion to 4q34, 9q34, 10q24 and 11q22, openness to 2p25, 3q26, 9p21, 11q24, 15q26 and 19q13 and agreeableness to 4q34 and 19p13. Significant evidence of association in the GWAS was detected between openness and rs677035 at 11q24 (P-value=2.6 × 10(-06), KCNJ1). The findings of our linkage meta-analysis and those of the GWAS suggest that 11q24 is a susceptible locus for openness, with KCNJ1 as the possible candidate gene.

Linkage analysis for plasma amyloid beta levels in persons with hypertension implicates Aß-40 levels to presenilin 2.

Plasma concentrations of Aß40 and Aß42 rise with age and are increased in people with mutations that cause early-onset Alzheimer's disease (AD). Amyloid beta (Aß) plasma levels were successfully used as an (endo)phenotype for gene discovery using a linkage approach in families with dominant forms of disease. Here, we searched for loci involved in Aß plasma levels in a series of non-demented patients with hypertension in the Erasmus Rucphen Family study. Aß40 and Aß42 levels were determined in 125 subjects with severe hypertension. All patients were genotyped with a 6,000 single nucleotide polymorphisms (SNPs) illumina array designed for linkage analysis. We conducted linkage analysis of plasma Aß levels. None of the linkage analyses yielded genome-wide significant logarithm of odds (LOD) score over 3.3, but there was suggestive evidence for linkage (LOD > 1.9) for two regions: 1q41 (LOD = 2.07) and 11q14.3 (LOD = 2.97), both for Aß40. These regions were followed up with association analysis in the study subjects and in 320 subjects from a population-based cohort. For the Aß40 region on chromosome 1, association of several SNPs was observed at the presenilin 2 gene (PSEN2) (p = 2.58 × 10(-4) for rs6703170). On chromosome 11q14-21, we found some association (p = 3.1 × 10(-3) for rs2514299). This linkage study of plasma concentrations of Aß40 and Aß42 yielded two suggestive regions, of which one points toward a known locus for familial AD.

Genome-wide association study identifies novel loci associated with circulating phospho- and sphingolipid concentrations.

Phospho- and sphingolipids are crucial cellular and intracellular compounds. These lipids are required for active transport, a number of enzymatic processes, membrane formation, and cell signalling. Disruption of their metabolism leads to several diseases, with diverse neurological, psychiatric, and metabolic consequences. A large number of phospholipid and sphingolipid species can be detected and measured in human plasma. We conducted a meta-analysis of five European family-based genome-wide association studies (N = 4034) on plasma levels of 24 sphingomyelins (SPM), 9 ceramides (CER), 57 phosphatidylcholines (PC), 20 lysophosphatidylcholines (LPC), 27 phosphatidylethanolamines (PE), and 16 PE-based plasmalogens (PLPE), as well as their proportions in each major class. This effort yielded 25 genome-wide significant loci for phospholipids (smallest P-value = 9.88×10(-204)) and 10 loci for sphingolipids (smallest P-value = 3.10×10(-57)). After a correction for multiple comparisons (P-value<2.2×10(-9)), we observed four novel loci significantly associated with phospholipids (PAQR9, AGPAT1, PKD2L1, PDXDC1) and two with sphingolipids (PLD2 and APOE) explaining up to 3.1% of the variance. Further analysis of the top findings with respect to within class molar proportions uncovered three additional loci for phospholipids (PNLIPRP2, PCDH20, and ABDH3) suggesting their involvement in either fatty acid elongation/saturation processes or fatty acid specific turnover mechanisms. Among those, 14 loci (KCNH7, AGPAT1, PNLIPRP2, SYT9, FADS1-2-3, DLG2, APOA1, ELOVL2, CDK17, LIPC, PDXDC1, PLD2, LASS4, and APOE) mapped into the glycerophospholipid and 12 loci (ILKAP, ITGA9, AGPAT1, FADS1-2-3, APOA1, PCDH20, LIPC, PDXDC1, SGPP1, APOE, LASS4, and PLD2) to the sphingolipid pathways. In large meta-analyses, associations between FADS1-2-3 and carotid intima media thickness, AGPAT1 and type 2 diabetes, and APOA1 and coronary artery disease were observed. In conclusion, our study identified nine novel phospho- and sphingolipid loci, substantially increasing our knowledge of the genetic basis for these traits.

GADS software for parametric linkage analysis of quantitative traits distributed as a point-mass mixture.

Often the quantitative data coming from proteomics and metabolomics studies have irregular distribution with a spike. None of the wide used methods for human QTL mapping are applicable to such traits. Researchers have to reduce the sample, excluding the spike, and analyze only continuous measurements. In this study, we propose a method for the parametric linkage analysis of traits with a spike in the distribution, and a software GADS, which implements this method. Our software includes not only the programs for parametric linkage analysis, but also the program for complex segregation analysis, which allows the estimation of the model parameters used in linkage. We tested our method on the real data about vertical cup-to-disc ratio, the important characteristic of the optic disc associated with glaucoma, in a large pedigree from a Dutch isolated population. Significant linkage signal was identified on chromosome 6 with the help of GADS, whereas the analysis of the normal distributed part of the sample demonstrated only a suggestive linkage peak on this chromosome. The software GADS is freely available at http://mga.bionet.nsc.ru/soft/index.html.

Ped_Outlier software for automatic identification of within-family outliers.

A high-throughput resequencing technology has brought family based studies back into genetic research focus. Within-family outliers (the individuals whose phenotype is very much unlike the phenotype of relatives) may carry rare variants of large effects and thus resequencing of these provides a highly powered strategy for rare variants detection. On the other hand, such outliers may complicate search for common variants of smaller effects, because they may obscure a real linkage signal. We have developed a program Ped_Outlier allowing automatic detection of within-family outliers in a sample of pedigrees of arbitrary structure and size. We tested our program by identification of within-family outliers for adult height and intracranial volume in large pedigree. Results of linkage analysis of these traits demonstrated that identification of within-family outliers is one of the important steps of pedigree analysis. The program Ped_outlier is freely available at http://mga.bionet.nsc.ru/soft/index.html.

A genome-wide screen for depression in two independent Dutch populations.

BACKGROUND: Depression has a strong genetic component but candidate gene studies conducted to date have not shown consistent associations. METHODS: We conducted a genome-wide parametric and nonparametric linkage analysis in a large-scale family-based study including 115 individuals with depression who were identified based on the Hospital Anxiety Depression Scale, Center for Epidemiologic Studies Depression Rating Scale, or use of antidepressive medication. Further, we investigated the most promising chromosomal regions found in the genome-wide linkage analysis with an association analysis in 734 individuals in the family-based study and 2373 individuals in the population-based study. RESULTS: Our study demonstrated evidence for significant linkage of depression to chromosome 2p16.1-15 (logarithm of odds [LOD] = 5.13; parametric analysis) and suggestive evidence for linkage in nonparametric analysis to chromosome 5p15.33 (LOD = 2.14), 11q25 (LOD = 2.27), and 19p13.3 (LOD = 2.66). The subsequent association analysis in the family-based study showed region-wide significant association in intron 1 of the OPCML gene on chromosome 11q25 (empirical p value = .04). The association analysis in the population-based study did not show any region-wide significant association, yet showed suggestive association in intron 1 of the APLP2 gene on chromosome 11q25. CONCLUSIONS: Our linkage and association studies suggest a locus for depression on chromosomes 2p16.1-15 and 11q25. The linkage to chromosome 11q25 may be, in part, explained by the OPCML or the APLP2 gene. Further, there is evidence for a role of the GNG7 gene (chromosome 19p13.3).

Genetic architecture of plasma adiponectin overlaps with the genetics of metabolic syndrome-related traits.

OBJECTIVE: Adiponectin, a hormone secreted by adipose tissue, is of particular interest in metabolic syndrome, because it is inversely correlated with obesity and insulin sensitivity. However, it is not known to what extent the genetics of plasma adiponectin and the genetics of obesity and insulin sensitivity are interrelated. We aimed to evaluate the heritability of plasma adiponectin and its genetic correlation with the metabolic syndrome and metabolic syndrome-related traits and the association between these traits and 10 ADIPOQ single nucleotide polymorphisms (SNPs). RESEARCH DESIGN AND METHODS: We made use of a family-based population, the Erasmus Rucphen Family study (1,258 women and 967 men). Heritability analysis was performed using a polygenic model. Genetic correlations were estimated using bivariate heritability analyses. Genetic association analysis was performed using a mixed model. RESULTS: Plasma adiponectin showed a heritability of 55.1%. Genetic correlations between plasma adiponectin HDL cholesterol and plasma insulin ranged from 15 to 24% but were not significant for fasting glucose, triglycerides, blood pressure, homeostasis model assessment of insulin resistance (HOMA-IR), and C-reactive protein. A significant association with plasma adiponectin was found for ADIPOQ variants rs17300539 and rs182052. A nominally significant association was found with plasma insulin and HOMA-IR and ADIPOQ variant rs17300539 after adjustment for plasma adiponectin. CONCLUSIONS: The significant genetic correlation between plasma adiponectin and HDL cholesterol and plasma insulin should be taken into account in the interpretation of genome-wide association studies. Association of ADIPOQ SNPs with plasma adiponectin was replicated, and we showed association between one ADIPOQ SNP and plasma insulin and HOMA-IR.

Linkage and genome-wide association analysis of obesity-related phenotypes: association of weight with the MGAT1 gene.

As major risk-factors for diabetes and cardiovascular diseases, the genetic contribution to obesity-related traits has been of interest for decades. Recently, a limited number of common genetic variants, which have replicated in different populations, have been identified. One approach to increase the statistical power in genetic mapping studies is to focus on populations with increased levels of linkage disequilibrium (LD) and reduced genetic diversity. We have performed joint linkage and genome-wide association analyses for weight and BMI in 3,448 (linkage) and 3,925 (association) partly overlapping healthy individuals from five European populations. A total of four chromosomal regions (two for weight and two for BMI) showed suggestive linkage (lod >2.69) either in one of the populations or in the joint data. At the genome-wide level (nominal P < 1.6 x 10(-7), Bonferroni-adjusted P < 0.05) one single-nucleotide polymorphism (SNP) (rs12517906) (nominal P = 7.3 x 10(-8)) was associated with weight, whereas none with BMI. The SNP associated with weight is located close to MGAT1. The monoacylglycerol acyltransferase (MGAT) enzyme family is known to be involved in dietary fat absorption. There was no overlap between the linkage regions and the associated SNPs. Our results show that genetic effects influencing weight and BMI are shared across diverse European populations, even though some of these populations have experienced recent population bottlenecks and/or been affected by genetic drift. The analysis enabled us to identify a new candidate gene, MGAT1, associated with weight in women.

The apolipoprotein E gene and its age-specific effects on cognitive function.

The E4 allele of the apolipoprotein E gene (APOE) is a well-established determinant of Alzheimer's disease but its relation to cognitive function is much less understood. We studied the age-specific effects of the APOE*E4 allele on cognitive function and cardiovascular risk factors in 2208 related individuals. APOE*E4 allele was significantly associated with reduced test scores for Adult Verbal Learning Test, particularly on the memory and learning sub domains, in persons older than 50 years of age. The effect of APOE*E4 was independent of the effect of APOE*E4 on vascular risk factors and most pronounced on learning ability. Our findings suggest that APOE*E4 has an effect on cognitive function predominantly in the elderly, independent of vascular risk factors.

Genetic determinants of circulating sphingolipid concentrations in European populations.

Sphingolipids have essential roles as structural components of cell membranes and in cell signalling, and disruption of their metabolism causes several diseases, with diverse neurological, psychiatric, and metabolic consequences. Increasingly, variants within a few of the genes that encode enzymes involved in sphingolipid metabolism are being associated with complex disease phenotypes. Direct experimental evidence supports a role of specific sphingolipid species in several common complex chronic disease processes including atherosclerotic plaque formation, myocardial infarction (MI), cardiomyopathy, pancreatic beta-cell failure, insulin resistance, and type 2 diabetes mellitus. Therefore, sphingolipids represent novel and important intermediate phenotypes for genetic analysis, yet little is known about the major genetic variants that influence their circulating levels in the general population. We performed a genome-wide association study (GWAS) between 318,237 single-nucleotide polymorphisms (SNPs) and levels of circulating sphingomyelin (SM), dihydrosphingomyelin (Dih-SM), ceramide (Cer), and glucosylceramide (GluCer) single lipid species (33 traits); and 43 matched metabolite ratios measured in 4,400 subjects from five diverse European populations. Associated variants (32) in five genomic regions were identified with genome-wide significant corrected p-values ranging down to 9.08x10(-66). The strongest associations were observed in or near 7 genes functionally involved in ceramide biosynthesis and trafficking: SPTLC3, LASS4, SGPP1, ATP10D, and FADS1-3. Variants in 3 loci (ATP10D, FADS3, and SPTLC3) associate with MI in a series of three German MI studies. An additional 70 variants across 23 candidate genes involved in sphingolipid-metabolizing pathways also demonstrate association (p = 10(-4) or less). Circulating concentrations of several key components in sphingolipid metabolism are thus under strong genetic control, and variants in these loci can be tested for a role in the development of common cardiovascular, metabolic, neurological, and psychiatric diseases.

Common variants in the JAZF1 gene associated with height identified by linkage and genome-wide association analysis.

Genes for height have gained interest for decades, but only recently have candidate genes started to be identified. We have performed linkage analysis and genome-wide association for height in approximately 4000 individuals from five European populations. A total of five chromosomal regions showed suggestive linkage and in one of these regions, two SNPs (rs849140 and rs1635852) were associated with height (nominal P = 7.0 x 10(-8) and P = 9.6 x 10(-7), respectively). In total, five SNPs across the genome showed an association with height that reached the threshold of genome-wide significance (nominal P < 1.6 x 10(-7)). The association with height was replicated for two SNPs (rs1635852 and rs849140) using three independent studies (n = 31 077, n=1268 and n = 5746) with overall meta P-values of 9.4 x 10(-10) and 5.3 x 10(-8). These SNPs are located in the JAZF1 gene, which has recently been associated with type II diabetes, prostate and endometrial cancer. JAZF1 is a transcriptional repressor of NR2C2, which results in low IGF1 serum concentrations, perinatal and early postnatal hypoglycemia and growth retardation when knocked out in mice. Both the linkage and association analyses independently identified the JAZF1 region affecting human height. We have demonstrated, through replication in additional independent populations, the consistency of the effect of the JAZF1 SNPs on height. Since this gene also has a key function in the metabolism of growth, JAZF1 represents one of the strongest candidates influencing human height identified so far.

Breaking loops in large complex pedigrees.

For pedigrees with multiple loops, exact likelihoods could not be computed in an acceptable time frame and thus, approximate methods are used. Some of these methods are based on breaking loops and approximations of complex pedigree likelihoods using the exact likelihood of the corresponding zero-loop pedigree. Due to ignoring loops, this method results in a loss of genetic information and a decrease in the power to detect linkage. To minimize this loss, an optimal set of loop breakers has to be selected. In this paper, we present a graph theory based algorithm for automatic selection of an optimal set of loop breakers. We propose using a total relationship between measured pedigree members as a proxy to power. To minimize the loss of genetic information, we suggest selection of such breakers whose duplication in a pedigree would be accompanied by a minimal loss of total relationship between measured pedigree members. We show that our algorithm compares favorably with other existing loop-breaker selection algorithms in terms of conservation of genetic information, statistical power and CPU time of subsequent linkage analysis. We implemented our method in a software package LOOP_EDGE, which is available at http://mga.bionet.nsc.ru/nlru/.

Heritability of blood pressure traits and the genetic contribution to blood pressure variance explained by four blood-pressure-related genes.

OBJECTIVE: To study the heritability of four blood pressure traits and the proportion of variance explained by four blood-pressure-related genes. METHODS: All participants are members of an extended pedigree from a Dutch genetically isolated population. Heritability and genetic correlations of systolic blood pressure, diastolic blood pressure, mean arterial pressure and pulse pressure were assessed using a variance components approach (SOLAR). Polymorphisms of the alpha-adducin (ADD1), angiotensinogen (AGT), angiotensin II type 1 receptor (AT1R) and G protein beta3 (GNB3) genes were typed. RESULTS: Heritability estimates were significant for all four blood pressure traits, ranging between 0.24 and 0.37. Genetic correlations between systolic blood pressure, diastolic blood pressure and mean arterial pressure were high (0.93-0.98), and those between pulse pressure and diastolic blood pressure were low (0.05). The ADD1 polymorphism explained 0.3% of the variance of pulse pressure (P = 0.07), and the polymorphism of GNB3 explained 0.4% of the variance of systolic blood pressure (P = 0.02), 0.2% of mean arterial pressure (P = 0.05) and 0.3% of pulse pressure (P = 0.06). CONCLUSION: Genetic factors contribute to a substantial proportion of blood pressure variance. In this study, the effect of polymorphisms of ADD1, AGT, AT1R and GNB3 explained a very small proportion of the heritability of blood pressure traits. As new genes associated with blood pressure are localized in the future, their effect on blood pressure variance should be calculated.