Joint analysis for genome-wide association studies in family-based designs

In family-based data, association information can be partitioned into the between-family information and the within-family information. Based on this observation, Steen et al. (Nature Genetics. 2005, 683-691) proposed an interesting two-stage test for genome-wide association (GWA) studies under family-based designs which performs genomic screening and replication using the same data set. In the first stage, a screening test based on the between-family information is used to select markers. In the second stage, an association test based on the within-family information is used to test association at the selected markers. However, we learn from the results of case-control studies (Skol et al. Nature Genetics. 2006, 209-213) that this two-stage approach may be not optimal. In this article, we propose a novel two-stage joint analysis for GWA studies under family-based designs. For this joint analysis, we first propose a new screening test that is based on the between-family information and is robust to population stratification. This new screening test is used in the first stage to select markers. Then, a joint test that combines the between-family information and within-family information is used in the second stage to test association at the selected markers. By extensive simulation studies, we demonstrate that the joint analysis always results in increased power to detect genetic association and is robust to population stratification.     

Family-based designs in the age of large-scale gene-association studies

Both population-based and family-based designs are commonly used in genetic association studies to locate genes that underlie complex diseases. The simplest version of the family-based design--the transmission disequilibrium test--is well known, but the numerous extensions that broaden its scope and power are less widely appreciated. Family-based designs have unique advantages over population-based designs, as they are robust against population admixture and stratification, allow both linkage and association to be tested for and offer a solution to the problem of model building. Furthermore, the fact that family-based designs contain both within- and between-family information has substantial benefits in terms of multiple-hypothesis testing, especially in the context of whole-genome association studies.     

Genomewide weighted hypothesis testing in family-based association studies, with an application to a 100K scan

For genomewide association (GWA) studies in family-based designs, we propose a novel two-stage strategy that weighs the association P values with the use of independently estimated weights. The association information contained in the family sample is partitioned into two orthogonal components--namely, the between-family information and the within-family information. The between-family component is used in the first (i.e., screening) stage to obtain a relative ranking of all the markers. The within-family component is used in the second (i.e., testing) stage in the framework of the standard family-based association test, and the resulting P values are weighted using the estimated marker ranking from the screening step. The approach is appealing, in that it ensures that all the markers are tested in the testing step and, at the same time, also uses information from the screening step. Through simulation studies, we show that testing all the markers is more powerful than testing only the most promising ones from the screening step, which was the method suggested by Van Steen et al. A comparison with a population-based approach shows that the approach achieves comparable power. In the presence of a reasonable level of population stratification, our approach is only slightly affected in terms of power and, since it is a family-based method, is completely robust to spurious effects. An application to a 100K scan in the Framingham Heart Study illustrates the practical advantages of our approach. The proposed method is of general applicability; it extends to any setting in which prior, independent ranking of hypotheses is available.     

A Generalized Family-Based Association Test for Dichotomous Traits

Recent advances in genotyping technology make it possible to utilize large-scale association analysis for disease-gene mapping. Powerful and robust family-based association methods are crucial for successful gene mapping. We propose a family-based association method, the generalized disequilibrium test (GDT), in which the genotype differences of all discordant relative pairs are utilized in assessing association within a family. The improvement of the GDT over existing methods is threefold: (1) information beyond first-degree relatives is incorporated efficiently, yielding substantial gains in power in comparison to existing tests; (2) the GDT statistic is implemented via a robust technique that does not rely on large sample theory, resulting in further power gains, especially at high levels of significance; and (3) covariates and weights based on family size are incorporated. Advantages of the GDT over existing methods are demonstrated by extensive computer simulations and by application to recently published large-scale genome-wide linkage data from the Type 1 Diabetes Genetics Consortium (T1DGC). In our simulations, the GDT consistently outperforms other tests for a common disease and frequently outperforms other tests for a rare disease; the power improvement is > 13% in 6 out of 8 extended pedigree scenarios. All of the six strongest associations identified by the GDT have been reported by other studies, whereas only three or four of these associations can be identified by existing methods. For the T1D association at gene UBASH3A, the GDT resulted in a genome-wide significance (p = 4.3 × 10⁻⁶), much stronger than the published significance (p = 10⁻⁴).     

Robust Rare-Variant Association Tests for Quantitative Traits in General Pedigrees

Next-generation sequencing technology has propelled the development of statistical methods to identify rare polygenetic variation associated with complex traits. The majority of these statistical methods are designed for case–control or population-based studies, with few methods that are applicable to family-based studies. Moreover, existing methods for family-based studies mainly focus on trios or nuclear families; there are far fewer existing methods available for analyzing larger pedigrees of arbitrary size and structure. To fill this gap, we propose a method for rare-variant analysis in large pedigree studies that can utilize information from all available relatives. Our approach is based on a kernel machine regression (KMR) framework, which has the advantages of high power, as well as fast and easy calculation of p-values using the asymptotic distribution. Our method is also robust to population stratification due to integration of a QTDT framework (Abecasis et al., Eur J Hum Genet 8(7):545–551, 2000b) with the KMR framework. In our method, we first calculate the expected genotype (between-family component) of a non-founder using all founders’ information and then calculate the deviates (within-family component) of observed genotype from the expectation, where the deviates are robust to population stratification by design. The test statistic, which is constructed using within-family component, is thus robust to population stratification. We illustrate and evaluate our method using simulated data and sequence data from Genetic Analysis Workshop 18.     

NOD2 and CCDC122-LACC1 genes are associated with leprosy susceptibility in Brazilians

Leprosy is a complex disease with phenotypes strongly influenced by genetic variation. A Chinese genome-wide association study (GWAS) depicted novel genes and pathways associated with leprosy susceptibility, only partially replicated by independent studies in different ethnicities. Here, we describe the results of a validation and replication study of the Chinese GWAS in Brazilians, using a stepwise strategy that involved two family-based and three independent case–control samples, resulting in 3,614 individuals enrolled. First, we genotyped a family-based sample for 36 tag single-nucleotide polymorphisms (SNPs) of five genes located in four different candidate loci: CCDC122-LACC1, NOD2, TNFSF15 and RIPK2. Association between leprosy and tag SNPs at NOD2 (rs8057431) and CCDC122-LACC1 (rs4942254) was then replicated in three additional, independent samples (combined OR_AA = 0.49, P = 1.39e?06; OR_CC = 0.72, P = 0.003, respectively). These results clearly implicate the NOD2 pathway in the regulation of leprosy susceptibility across diverse populations.     

Genetic Association Analysis of Complex Diseases Incorporating Intermediate Phenotype Information

Genetic researchers often collect disease related quantitative traits in addition to disease status because they are interested in understanding the pathophysiology of disease processes. In genome-wide association (GWA) studies, these quantitative phenotypes may be relevant to disease development and serve as intermediate phenotypes or they could be behavioral or other risk factors that predict disease risk. Statistical tests combining both disease status and quantitative risk factors should be more powerful than case-control studies, as the former incorporates more information about the disease. In this paper, we proposed a modified inverse-variance weighted meta-analysis method to combine disease status and quantitative intermediate phenotype information. The simulation results showed that when an intermediate phenotype was available, the inverse-variance weighted method had more power than did a case-control study of complex diseases, especially in identifying susceptibility loci having minor effects. We further applied this modified meta-analysis to a study of imputed lung cancer genotypes with smoking data in 1154 cases and 1137 matched controls. The most significant SNPs came from the CHRNA3-CHRNA5-CHRNB4 region on chromosome 15q24–25.1, which has been replicated in many other studies. Our results confirm that this CHRNA region is associated with both lung cancer development and smoking behavior. We also detected three significant SNPs—rs1800469, rs1982072, and rs2241714—in the promoter region of the TGFB1 gene on chromosome 19 (p = 1.46×10⁻⁵, 1.18×10⁻⁵, and 6.57×10⁻⁶, respectively). The SNP rs1800469 is reported to be associated with chronic obstructive pulmonary disease and lung cancer in cigarette smokers. The present study is the first GWA study to replicate this result. Signals in the 3q26 region were also identified in the meta-analysis. We demonstrate the intermediate phenotype can potentially enhance the power of complex disease association analysis and the modified meta-analysis method is robust to incorporate intermediate phenotype or other quantitative risk factor in the analysis.     

Genetic Evidence for Possible Involvement of the Calcium Channel Gene CACNA1A in Autism Pathogenesis in Chinese Han Population

Autism spectrum disorders (ASD) are a group of neurodevelopmental disorders. Recent studies suggested that calcium channel genes might be involved in the genetic etiology of ASD. CACNA1A, encoding an alpha-1 subunit of voltage-gated calcium channel, has been reported to play an important role in neural development. Previous study detected that a single nucleotide polymorphism (SNP) in CACNA1A confers risk to ASD in Central European population. However, the genetic relationship between autism and CACNA1A in Chinese Han population remains unclear. To explore the association of CACNA1A with autism, we performed a family-based association study. First, we carried out a family-based association test between twelve tagged SNPs and autism in 239 trios. To further confirm the association, the sample size was expanded to 553 trios by recruiting 314 additional trios. In a total of 553 trios, we identified association of rs7249246 and rs12609735 with autism though this would not survive after Bonferroni correction. Our findings suggest that CACNA1A might play a role in the etiology of autism.     

Association of IL10 Polymorphisms and Leprosy: A Meta-Analysis

Leprosy is a chronic infectious disease that depends on the interplay of several factors. Single nucleotide polymorphisms (SNPs) in host immune related genes have been consistently suggested as participants in susceptibility towards disease. Interleukin-10 (IL-10) is a crucial immunomodulatory cytokine in mycobacterial pathogenesis and especially the -819C>T SNP (rs1800871) has been tested in several case-control studies indicating association with leprosy risk, although a recent consensus estimate is still missing. In this study, we evaluated the association of the -819C>T SNP and leprosy in two new Brazilian family-based populations. Then, we performed meta-analysis for this polymorphism summarizing published studies including these Brazilian family-based groups. Finally, we also retrieved published studies for other distal and proximal IL10 polymorphisms: -3575 T>A (rs1800890), -2849 G>A (rs6703630), -2763 C>A (rs6693899), -1082 G>A (rs1800896) and -592 C>A (rs1800872). Results from meta-analysis supported a significant susceptibility association for the -819T allele, with pooled Odds Ratio of 1.22 (CI = 1.11–1.34) and P-value = 3x10^–5 confirming previous data. This result remained unaltered after inclusion of the Brazilian family-based groups (OR = 1.2, CI = 1.10–1.31, P-value = 2x10^–5). Also, meta-analysis confirmed association of -592 A allele and leprosy outcome (OR = 1.24, CI = 1.03–1.50, P-value = 0.02). In support of this, linkage disequilibrium analysis in 1000 genomes AFR, EUR, ASN and AMR populations pointed to r² = 1.0 between the -592C>A and -819C>T SNPs. We found no evidence of association for the other IL10 polymorphisms analyzed for leprosy outcome. Our results reinforce the role of the -819C>T as a tag SNP (rs1800871) and its association with leprosy susceptibility.     

Detection of QTL in Rainbow Trout Affecting Survival When Challenged with Flavobacterium psychrophilum

Bacterial cold water disease (BCWD) causes significant economic loss in salmonid aquaculture. We previously detected genetic variation in survival following challenge with Flavobacterium psychrophilum (Fp), the causative agent of BCWD in rainbow trout (Oncorhynchus mykiss). A family-based selection program to improve resistance was initiated in 2005 at the USDA National Center for Cool and Cold Water Aquaculture. Select crosses were made in 2007 and 2009 to evaluate family-based disease survival using Fp injection challenges. From each putative F₂/BC₁ family generated in 2009, 200–260 fish were challenged in 4–7 replicates per family. Whole genome QTL scans of three F₂/BC₁ families were conducted with about 270 informative microsatellite loci per family spaced at an average interval size of 6 cM throughout the rainbow trout genome. Markers on chromosomes containing QTL were further evaluated in three additional F₂/BC₁ families. The additional F₂/BC₁ families were sire or dam half-sibs (HS) of the initially genome scanned families. Overall, we identified nine major QTL on seven chromosomes that were significant or highly significant with moderate to large effects of at least 13 % of the total phenotypic variance. The largest effect QTL for BCWD resistance explaining up to 40 % of the phenotypic variance was detected on chromosome OMY8 in family 2009070 and in the combined dam HS family 2009069–070. The nine major QTL identified in this study are candidates for fine mapping to identify new markers that are tightly linked to disease resistance loci for using in marker assisted selection strategies.     

Multiple Changes of Gene Expression and Function Reveal Genomic and Phenotypic Complexity in SLE-like Disease

The complexity of clinical manifestations commonly observed in autoimmune disorders poses a major challenge to genetic studies of such diseases. Systemic lupus erythematosus (SLE) affects humans as well as other mammals, and is characterized by the presence of antinuclear antibodies (ANA) in patients’ sera and multiple disparate clinical features. Here we present evidence that particular sub-phenotypes of canine SLE-related disease, based on homogenous (ANA^H) and speckled ANA (ANA^S) staining pattern, and also steroid-responsive meningitis-arteritis (SRMA) are associated with different but overlapping sets of genes. In addition to association to certain MHC alleles and haplotypes, we identified 11 genes (WFDC3, HOMER2, VRK1, PTPN3, WHAMM, BANK1, AP3B2, DAPP1, LAMTOR3, DDIT4L and PPP3CA) located on five chromosomes that contain multiple risk haplotypes correlated with gene expression and disease sub-phenotypes in an intricate manner. Intriguingly, the association of BANK1 with both human and canine SLE appears to lead to similar changes in gene expression levels in both species. Our results suggest that molecular definition may help unravel the mechanisms of different clinical features common between and specific to various autoimmune disease phenotypes in dogs and humans.     

FamPipe: An Automatic Analysis Pipeline for Analyzing Sequencing Data in Families for Disease Studies

In disease studies, family-based designs have become an attractive approach to analyzing next-generation sequencing (NGS) data for the identification of rare mutations enriched in families. Substantial research effort has been devoted to developing pipelines for automating sequence alignment, variant calling, and annotation. However, fewer pipelines have been designed specifically for disease studies. Most of the current analysis pipelines for family-based disease studies using NGS data focus on a specific function, such as identifying variants with Mendelian inheritance or identifying shared chromosomal regions among affected family members. Consequently, some other useful family-based analysis tools, such as imputation, linkage, and association tools, have yet to be integrated and automated. We developed FamPipe, a comprehensive analysis pipeline, which includes several family-specific analysis modules, including the identification of shared chromosomal regions among affected family members, prioritizing variants assuming a disease model, imputation of untyped variants, and linkage and association tests. We used simulation studies to compare properties of some modules implemented in FamPipe, and based on the results, we provided suggestions for the selection of modules to achieve an optimal analysis strategy. The pipeline is under the GNU GPL License and can be downloaded for free at http://fampipe.sourceforge.net.

This is a PLOS Computational Biology Software article.

     

Canonical Correlation Analysis for Gene-Based Pleiotropy Discovery

Genome-wide association studies have identified a wealth of genetic variants involved in complex traits and multifactorial diseases. There is now considerable interest in testing variants for association with multiple phenotypes (pleiotropy) and for testing multiple variants for association with a single phenotype (gene-based association tests). Such approaches can increase statistical power by combining evidence for association over multiple phenotypes or genetic variants respectively. Canonical Correlation Analysis (CCA) measures the correlation between two sets of multidimensional variables, and thus offers the potential to combine these two approaches. To apply CCA, we must restrict the number of attributes relative to the number of samples. Hence we consider modules of genetic variation that can comprise a gene, a pathway or another biologically relevant grouping, and/or a set of phenotypes. In order to do this, we use an attribute selection strategy based on a binary genetic algorithm. Applied to a UK-based prospective cohort study of 4286 women (the British Women''s Heart and Health Study), we find improved statistical power in the detection of previously reported genetic associations, and identify a number of novel pleiotropic associations between genetic variants and phenotypes. New discoveries include gene-based association of NSF with triglyceride levels and several genes (ACSM3, ERI2, IL18RAP, IL23RAP and NRG1) with left ventricular hypertrophy phenotypes. In multiple-phenotype analyses we find association of NRG1 with left ventricular hypertrophy phenotypes, fibrinogen and urea and pleiotropic relationships of F7 and F10 with Factor VII, Factor IX and cholesterol levels.     

An assessment of gene-by-gene interactions as a tool to unfold missing heritability in dyslexia

Even if substantial heritability has been reported and candidate genes have been identified extensively, all known marker associations explain only a small proportion of the phenotypic variance of developmental dyslexia (DD) and related quantitative phenotypes. Gene-by-gene interaction (also known as “epistasis”—G × G) triggers a non-additive effect of genes at different loci and should be taken into account in explaining part of the missing heritability of this complex trait. We assessed potential G × G interactions among five DD candidate genes, i.e., DYX1C1, DCDC2, KIAA0319, ROBO1, and GRIN2B, upon DD-related neuropsychological phenotypes in 493 nuclear families with DD, by implementing two complementary regression-based approaches: (1) a general linear model equation whereby the trait is predicted by the main effect of the number of rare alleles of the two genes and by the effect of the interaction between them, and (2) a family-based association test to detect G × G interactions between two unlinked markers by splitting up the association effect into a between- and a within-family genetic orthogonal components. After applying 500,000 permutations and correcting for multiple testing, both methods show that G × G effects between markers within the DYX1C1, KIAA0319/TTRAP, and GRIN2B genes lower the memory letters composite z-score of on average 0.55 standard deviation. We provided initial evidence that the effects of familial transmission of synergistic interactions between genetic risk variants can be exploited in the study of the etiology of DD, explain part of its missing heritability, and assist in designing customized charts of individualized neurocognitive impairments in complex disorders, such as DD.     

Combined analysis of genome-wide association studies for Crohn disease and psoriasis identifies seven shared susceptibility loci

Psoriasis (PS) and Crohn disease (CD) have been shown to be epidemiologically, pathologically, and therapeutically connected, but little is known about their shared genetic causes. We performed meta-analyses of five published genome-wide association studies on PS (2,529 cases and 4,955 controls) and CD (2,142 cases and 5,505 controls), followed up 20 loci that showed strongest evidence for shared disease association and, furthermore, tested cross-disease associations for previously reported PS and CD risk alleles in additional 6,115 PS cases, 4,073 CD cases, and 10,100 controls. We identified seven susceptibility loci outside the human leukocyte antigen region (9p24 near JAK2, 10q22 at ZMIZ1, 11q13 near PRDX5, 16p13 near SOCS1, 17q21 at STAT3, 19p13 near FUT2, and 22q11 at YDJC) shared between PS and CD with genome-wide significance (p < 5 × 10⁻⁸) and confirmed four already established PS and CD risk loci (IL23R, IL12B, REL, and TYK2). Three of the shared loci are also genome-wide significantly associated with PS alone (10q22 at ZMIZ1, p_rs1250544 = 3.53 × 10⁻⁸, 11q13 near PRDX5, p_rs694739 = 3.71 × 10⁻⁰⁹, 22q11 at YDJC, p_rs181359 = 8.02 × 10⁻¹⁰). In addition, we identified one susceptibility locus for CD (16p13 near SOCS1, p_rs4780355 = 4.99 × 10⁻⁸). Refinement of association signals identified shared genome-wide significant associations for exonic SNPs at 10q22 (ZMIZ1) and in silico expression quantitative trait locus analyses revealed that the associations at ZMIZ1 and near SOCS1 have a potential functional effect on gene expression. Our results show the usefulness of joint analyses of clinically distinct immune-mediated diseases and enlarge the map of shared genetic risk loci.     

Genome-wide association of familial late-onset Alzheimer's disease replicates BIN1 and CLU and nominates CUGBP2 in interaction with APOE

Late-onset Alzheimer''s disease (LOAD) is the most common form of dementia in the elderly. The National Institute of Aging-Late Onset Alzheimer''s Disease Family Study and the National Cell Repository for Alzheimer''s Disease conducted a joint genome-wide association study (GWAS) of multiplex LOAD families (3,839 affected and unaffected individuals from 992 families plus additional unrelated neurologically evaluated normal subjects) using the 610 IlluminaQuad panel. This cohort represents the largest family-based GWAS of LOAD to date, with analyses limited here to the European-American subjects. SNPs near APOE gave highly significant results (e.g., rs2075650, p = 3.2×10⁻⁸¹), but no other genome-wide significant evidence for association was obtained in the full sample. Analyses that stratified on APOE genotypes identified SNPs on chromosome 10p14 in CUGBP2 with genome-wide significant evidence for association within APOE ε4 homozygotes (e.g., rs201119, p = 1.5×10⁻⁸). Association in this gene was replicated in an independent sample consisting of three cohorts. There was evidence of association for recently-reported LOAD risk loci, including BIN1 (rs7561528, p = 0.009 with, and p = 0.03 without, APOE adjustment) and CLU (rs11136000, p = 0.023 with, and p = 0.008 without, APOE adjustment), with weaker support for CR1. However, our results provide strong evidence that association with PICALM (rs3851179, p = 0.69 with, and p = 0.039 without, APOE adjustment) and EXOC3L2 is affected by correlation with APOE, and thus may represent spurious association. Our results indicate that genetic structure coupled with ascertainment bias resulting from the strong APOE association affect genome-wide results and interpretation of some recently reported associations. We show that a locus such as APOE, with large effects and strong association with disease, can lead to samples that require appropriate adjustment for this locus to avoid both false positive and false negative evidence of association. We suggest that similar adjustments may also be needed for many other large multi-site studies.     

Parental phenotypes in family-based association analysis

Family-based association designs are popular, because they offer inherent control of population stratification based on age, sex, ethnicity, and environmental exposure. However, the efficiency of these designs is hampered by current analytic strategies that consider only offspring phenotypes. Here, we describe the incorporation of parental phenotypes and, specifically, the inclusion of parental genotype-phenotype correlation terms in association tests, providing a series of tests that effectively span an efficiency-robustness spectrum. The model is based on the between-within-sibship association model presented in 1999 by Fulker and colleagues for quantitative traits and extended here to nuclear families. By use of a liability-threshold-model approach, standard dichotomous and/or qualitative disease phenotypes can be analyzed (and can include appropriate corrections for phenotypically ascertained samples), which allows for the application of this model to analysis of the commonly used affected-proband trio design. We show that the incorporation of parental phenotypes can considerably increase power, as compared with the standard transmission/disequilibrium test and equivalent quantitative tests, while providing both significant protection against stratification and a means of evaluating the contribution of stratification to positive results. This methodology enables the extraction of more information from existing family-based collections that are currently being genotyped and analyzed by use of standard approaches.     

TrioMDR: Detecting SNP interactions in trio families with model-based multifactor dimensionality reduction

Single nucleotide polymorphism (SNP) interactions can explain the missing heritability of common complex diseases. Many interaction detection methods have been proposed in genome-wide association studies, and they can be divided into two types: population-based and family-based. Compared with population-based methods, family-based methods are robust vs. population stratification. Several family-based methods have been proposed, among which Multifactor Dimensionality Reduction (MDR)-based methods are popular and powerful. However, current MDR-based methods suffer from heavy computational burden. Furthermore, they do not allow for main effect adjustment. In this work we develop a two-stage model-based MDR approach (TrioMDR) to detect multi-locus interaction in trio families (i.e., two parents and one affected child). TrioMDR combines the MDR framework with logistic regression models to check interactions, so TrioMDR can adjust main effects. In addition, unlike consuming permutation procedures used in traditional MDR-based methods, TrioMDR utilizes a simple semi-parameter P-values correction procedure to control type I error rate, this procedure only uses a few permutations to achieve the significance of a multi-locus model and significantly speeds up TrioMDR. We performed extensive experiments on simulated data to compare the type I error and power of TrioMDR under different scenarios. The results demonstrate that TrioMDR is fast and more powerful in general than some recently proposed methods for interaction detection in trios. The R codes of TrioMDR are available at: https://github.com/TrioMDR/TrioMDR.     

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 β-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 9.08×10⁻⁶⁶. 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.     

Estimation of heritability from limited family data using genome-wide identity-by-descent sharing

Background

In classical pedigree-based analysis, additive genetic variance is estimated from between-family variation, which requires the existence of larger phenotyped and pedigreed populations involving numerous families (parents). However, estimation is often complicated by confounding of genetic and environmental family effects, with the latter typically occurring among full-sibs. For this reason, genetic variance is often inferred based on covariance among more distant relatives, which reduces the power of the analysis. This simulation study shows that genome-wide identity-by-descent sharing among close relatives can be used to quantify additive genetic variance solely from within-family variation using data on extremely small family samples.

Methods

Identity-by-descent relationships among full-sibs were simulated assuming a genome size similar to that of humans (effective number of loci ~80). Genetic variance was estimated from phenotypic data assuming that genomic identity-by-descent relationships could be accurately re-created using information from genome-wide markers. The results were compared with standard pedigree-based genetic analysis.

Results

For a polygenic trait and a given number of phenotypes, the most accurate estimates of genetic variance were based on data from a single large full-sib family only. Compared with classical pedigree-based analysis, the proposed method is more robust to selection among parents and for confounding of environmental and genetic effects. Furthermore, in some cases, satisfactory results can be achieved even with less ideal data structures, i.e., for selectively genotyped data and for traits for which the genetic variance is largely under the control of a few major genes.

Conclusions

Estimation of genetic variance using genomic identity-by-descent relationships is especially useful for studies aiming at estimating additive genetic variance of highly fecund species, using data from small populations with limited pedigree information and/or few available parents, i.e., parents originating from non-pedigreed or even wild populations.