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.

In silico genome-wide gene-based association analysis reveals new genes predisposing to coronary artery disease.

Genome-wide association study (GWAS) have identified more than 300 single nucleotide polymorphisms at 163 independent loci associated with coronary artery disease (CAD). However, there is no full understanding about the causal genes for CAD and the mechanisms of their action. We aimed to perform a post GWAS analysis to identify genes whose polymorphism may influence the risk of CAD. Using the UK Biobank GWAS summary statistics, we performed a gene-based association analysis. We found 63 genes significantly associated with CAD due to their within-gene polymorphisms. Many of these genes are well known. Some known CAD genes such as FURIN and SORT1 did not show the gene-based association because their variants had low GWAS signals or gene-based association was inflated by the strong GWAS signal outside the gene. For several known CAD genes, we demonstrated that their effects could be explained not only or not at all by their own variants but by the variants within the neighboring genes controlling their expression. Using several bioinformatics techniques, we suggested potential mechanisms underlying gene-CAD associations. Three genes, CDK19, NCALD, and ARHGEF12 were not previously associated with CAD. The role of these genes should be clarified in further studies.

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.

FREGAT: an R package for region-based association analysis.

UNLABELLED: Several approaches to the region-based association analysis of quantitative traits have recently been developed and successively applied. However, no software package has been developed that implements all of these approaches for either independent or structured samples. Here we introduce FREGAT (Family REGional Association Tests), an R package that can handle family and population samples and implements a wide range of region-based association methods including burden tests, functional linear models, and kernel machine-based regression. FREGAT can be used in genome/exome-wide region-based association studies of quantitative traits and candidate gene analysis. FREGAT offers many useful options to empower its users and increase the effectiveness and applicability of region-based association analysis. AVAILABILITY AND IMPLEMENTATION: https://cran.r-project.org/web/packages/FREGAT/index.html SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics Online. CONTACT: belon@bionet.nsc.ru.

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.

Noncoding rare variants in PANX3 are associated with chronic back pain.

ABSTRACT: Back pain is the leading cause of years lived with disability worldwide, yet surprisingly, little is known regarding the biology underlying this condition. The impact of genetics is known for chronic back pain: its heritability is estimated to be at least 40%. Large genome-wide association studies have shown that common variation may account for up to 35% of chronic back pain heritability; rare variants may explain a portion of the heritability not explained by common variants. In this study, we performed the first gene-based association analysis of chronic back pain using UK Biobank imputed data including rare variants with moderate imputation quality. We discovered 2 genes, SOX5 and PANX3 , influencing chronic back pain. The SOX5 gene is a well-known back pain gene. The PANX3 gene has not previously been described as having a role in chronic back pain. We showed that the association of PANX3 with chronic back pain is driven by rare noncoding intronic polymorphisms. This result was replicated in an independent sample from UK Biobank and validated using a similar phenotype, dorsalgia, from FinnGen Biobank. We also found that the PANX3 gene is associated with intervertebral disk disorders. We can speculate that a possible mechanism of action of PANX3 on back pain is due to its effect on the intervertebral disks.

Loci Associated with Negative Heterosis for Viability and Meat Productivity in Interspecific Sheep Hybrids.

Negative heterosis can occur on different economically important traits, but the exact biological mechanisms of this phenomenon are still unknown. The present study focuses on determining the genetic factors associated with negative heterosis in interspecific hybrids between domestic sheep (Ovis aries) and argali (Ovis ammon). One locus (rs417431015) associated with viability and two loci (rs413302370, rs402808951) associated with meat productivity were identified. One gene (ARAP2) was prioritized for viability and three for meat productivity (PDE2A, ARAP1, and PCDH15). The loci associated with meat productivity were demonstrated to fit the overdominant inheritance model and could potentially be involved int negative heterosis mechanisms.

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.

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.

Linkage and association analyses of glaucoma related traits in a large pedigree from a Dutch genetically isolated population.

BACKGROUND: Despite extensive research on the genetic determinants of glaucoma, the genes identified to date explain only a small proportion of cases in the general population. METHODS: Genome-wide linkage and association analyses of quantitative traits related to glaucoma were performed: intraocular pressure, size and morphology of the optic disc (individual and combined by method of principal components) and thickness of the retinal nerve fibre layer (RNFL), in a large pedigree from a genetically isolated Dutch population. RESULTS: For the size of the optic disc, the study demonstrated a significant linkage signal (logarithm of odds (LOD)=3.6) at the LRP1B region on chromosome 2q21.2-q22.2 and significant association (p=8.95×10(-12)) with the previously reported CDC7/TGFBR3 locus at 1p22. For parameters describing morphology of the optic disc, the study obtained significant linkage signal (LOD=4.6) at regions SIRPA and RNF24/PANK2 at 20p13 (false discovery rate (FDR) based q value <0.05) and genome-wide significant association (p=2.38×10(-9)) with a common variant in the RERE gene at 1p36. Suggestive linkage and association signals indicated loci for morphology of the optic disc at 2q31-q33 (IGFBP2 locus) and for RNFL thickness at 3p22.2 (DCLK3 locus) and 14q22-q23 (SIX1 locus). CONCLUSION: This study identified new linkage regions at 20p13 (SIRPA and RNF24/PANK2 loci) and 2q33-q34 (IGFBP2 locus) for parameters describing morphology of the optic disc. The results of the study also suggested common genetic control of these parameters and RNFL thickness by SIX1 and doublecotin family genes. Finally, association signals for the recently reported RERE and LRP1B loci and the well known CDC7, TGFBR3, and ATOH7 loci were replicated.

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.

A Novel Framework for Analysis of the Shared Genetic Background of Correlated Traits.

We propose a novel effective framework for the analysis of the shared genetic background for a set of genetically correlated traits using SNP-level GWAS summary statistics. This framework called SHAHER is based on the construction of a linear combination of traits by maximizing the proportion of its genetic variance explained by the shared genetic factors. SHAHER requires only full GWAS summary statistics and matrices of genetic and phenotypic correlations between traits as inputs. Our framework allows both shared and unshared genetic factors to be effectively analyzed. We tested our framework using simulation studies, compared it with previous developments, and assessed its performance using three real datasets: anthropometric traits, psychiatric conditions and lipid concentrations. SHAHER is versatile and applicable to summary statistics from GWASs with arbitrary sample sizes and sample overlaps, allows for the incorporation of different GWAS models (Cox, linear and logistic), and is computationally fast.

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.

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.

The limits of normal approximation for adult height.

Adult height inspired the first biometrical and quantitative genetic studies and is a test-case trait for understanding heritability. The studies of height led to formulation of the classical polygenic model, that has a profound influence on the way we view and analyse complex traits. An essential part of the classical model is an assumption of additivity of effects and normality of the distribution of the residuals. However, it may be expected that the normal approximation will become insufficient in bigger studies. Here, we demonstrate that when the height of hundreds of thousands of individuals is analysed, the model complexity needs to be increased to include non-additive interactions between sex, environment and genes. Alternatively, the use of log-normal approximation allowed us to still use the additive effects model. These findings are important for future genetic and methodologic studies that make use of adult height as an exemplar trait.

MQScore_SNP software for multipoint parametric linkage analysis of quantitative traits in large pedigrees.

We describe software for multipoint parametric linkage analysis of quantitative traits using information about SNP genotypes. A mixed model of major gene and polygene inheritance is implemented in this software. Implementation of several algorithms to avoid computational underflow and decrease running time permits application of our software to the analysis of very large pedigrees collected in human genetically isolated populations. We tested our software by performing linkage analysis of adult height in a large pedigree from a Dutch isolated population. Three significant and four suggestive loci were identified with the help of our programs, whereas variance-component-based linkage analysis, which requires the pedigree fragmentation, demonstrated only three suggestive peaks. The software package MQScore_SNP is available at http://mga.bionet.nsc.ru/soft/index.html.

PedStr software for cutting large pedigrees for haplotyping, IBD computation and multipoint linkage analysis.

We propose an automatic heuristic algorithm for splitting large pedigrees into fragments of no more than a user-specified bit size. The algorithm specifically aims to split large pedigrees where many close relatives are genotyped and to produce a set of sub-pedigrees for haplotype reconstruction, IBD computation or multipoint linkage analysis with the help of the Lander-Green-Kruglyak algorithm. We demonstrate that a set of overlapping pedigree fragments constructed with the help of our algorithm allows fast and effective haplotype reconstruction and detection of an allele's parental origin. Moreover, we compared pedigree fragments constructed with the help of our algorithm and existing programs PedCut and Jenti for multipoint linkage analysis. Our algorithm demonstrated significantly higher linkage power than the algorithm of Jenti and significantly shorter running time than the algorithm of PedCut. The software package PedStr implementing our algorithms is available at http://mga.bionet.nsc.ru/soft/index.html.

Linkage analysis of adult height in a large pedigree from a Dutch genetically isolated population.

Despite extensive research of genetic determinants of human adult height, the genes identified up until now allow to predict only a small proportion of the trait's variance. To identify new genes we analyzed 2,486 genotyped and phenotyped individuals in a large pedigree including 23,612 members in 18 generations. The pedigree was derived from a young genetically isolated Dutch population, where genetic heterogeneity is expected to be low and linkage disequilibrium has been shown to be increased. Complex segregation analysis confirmed high heritability of adult height, and suggested mixed model of height inheritance in this population. The estimates of the model parameters obtained from complex segregation analysis were used in parametric linkage analysis, which highlighted three genome-wide significant and additionally at least four suggestive loci involved in height. Significant peaks were located at the chromosomal regions 1p32 (LOD score = 3.35), 2p16 (LOD score = 3.29) and 16q24 (LOD score = 3.94). For the latter region, a strong association signal (FDR q < 0.05) was obtained for 19 SNPs, 17 of them were located in the CDH13 (cadherin 13) gene of which one (rs1035569) explained 1.5% of the total height variance.

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/.

An approach for cutting large and complex pedigrees for linkage analysis.

Utilizing large pedigrees in linkage analysis is a computationally challenging task. The pedigree size limits applicability of the Lander-Green-Kruglyak algorithm for linkage analysis. A common solution is to split large pedigrees into smaller computable subunits. We present a pedigree-splitting method that, within a user supplied bit-size limit, identifies subpedigrees having the maximal number of subjects of interest (eg patients) who share a common ancestor. We compare our method with the maximum clique partitioning method using a large and complex human pedigree consisting of 50 patients with Alzheimer's disease ascertained from genetically isolated Dutch population. We show that under a bit-size limit our method can assign more patients to subpedigrees than the clique partitioning method, particularly when splitting deep pedigrees where the subjects of interest are scattered in recent generations and are relatively distantly related via multiple genealogic connections. Our pedigree-splitting algorithm and associated software can facilitate genome-wide linkage scans searching for rare mutations in large pedigrees coming from genetically isolated populations. The software package PedCut implementing our approach is available at http://mga.bionet.nsc.ru/soft/index.html.