Gene-level association analysis of bivariate ordinal traits with functional regressions.

In genetic studies, many phenotypes have multiple naturally ordered discrete values. The phenotypes can be correlated with each other. If multiple correlated ordinal traits are analyzed simultaneously, the power of analysis may increase significantly while the false positives can be controlled well. In this study, we propose bivariate functional ordinal linear regression (BFOLR) models using latent regressions with cumulative logit link or probit link to perform a gene-based analysis for bivariate ordinal traits and sequencing data. In the proposed BFOLR models, genetic variant data are viewed as stochastic functions of physical positions, and the genetic effects are treated as a function of physical positions. The BFOLR models take the correlation of the two ordinal traits into account via latent variables. The BFOLR models are built upon functional data analysis which can be revised to analyze the bivariate ordinal traits and high-dimension genetic data. The methods are flexible and can analyze three types of genetic data: (1) rare variants only, (2) common variants only, and (3) a combination of rare and common variants. Extensive simulation studies show that the likelihood ratio tests of the BFOLR models control type I errors well and have good power performance. The BFOLR models are applied to analyze Age-Related Eye Disease Study data, in which two genes, CFH and ARMS2, are found to strongly associate with eye drusen size, drusen area, age-related macular degeneration (AMD) categories, and AMD severity scale.

Targeted Sequencing of Candidate Regions Associated with Sagittal and Metopic Nonsyndromic Craniosynostosis.

Craniosynostosis (CS) is a major birth defect in which one or more skull sutures fuse prematurely. We previously performed a genome-wide association study (GWAS) for sagittal non-syndromic CS (sNCS), identifying associations downstream from BMP2 on 20p12.3 and intronic to BBS9 on 7p14.3; analyses of imputed variants in DLG1 on 3q29 were also genome-wide significant. We followed this work with a GWAS for metopic non-syndromic NCS (mNCS), discovering a significant association intronic to BMP7 on 20q13.31. In the current study, we sequenced the associated regions on 3q29, 7p14.3, and 20p12.3, including two candidate genes (BMP2 and BMPER) near some of these regions in 83 sNCS child-parent trios, and sequenced regions on 7p14.3 and 20q13.2-q13.32 in 80 mNCS child-parent trios. These child-parent trios were selected from the original GWAS cohorts if the probands carried at least one copy of the top associated GWAS variant (rs1884302 C allele for sNCS; rs6127972 T allele for mNCS). Many of the variants sequenced in these targeted regions are strongly predicted to be within binding sites for transcription factors involved in craniofacial development or bone morphogenesis. Variants enriched in more than one trio and predicted to be damaging to gene function are prioritized for functional studies.

Gene-level association analysis of ordinal traits with functional ordinal logistic regressions.

In this paper, we develop functional ordinal logistic regression (FOLR) models to perform gene-based analysis of ordinal traits. In the proposed FOLR models, genetic variant data are viewed as stochastic functions of physical positions and the genetic effects are treated as a function of physical positions. The FOLR models are built upon functional data analysis which can be revised to analyze the ordinal traits and high dimension genetic data. The proposed methods are capable of dealing with dense genotype data which is usually encountered in analyzing the next-generation sequencing data. The methods are flexible and can analyze three types of genetic data: (1) rare variants only, (2) common variants only, and (3) a combination of rare and common variants. Simulation studies show that the likelihood ratio test statistics of the FOLR models control type I errors well and have good power performance. The proposed methods achieve the goals of analyzing ordinal traits directly, reducing high dimensionality of dense genetic variants, being computationally manageable, facilitating model convergence, properly controlling type I errors, and maintaining high power levels. The FOLR models are applied to analyze Age-Related Eye Disease Study data, in which two genes are found to strongly associate with four ordinal traits.

Gene-Based Association Testing of Dichotomous Traits With Generalized Functional Linear Mixed Models Using Extended Pedigrees: Applications to Age-Related Macular Degeneration.

Genetics plays a role in age-related macular degeneration (AMD), a common cause of blindness in the elderly. There is a need for powerful methods for carrying out region-based association tests between a dichotomous trait like AMD and genetic variants on family data. Here, we apply our new generalized functional linear mixed models (GFLMM) developed to test for gene-based association in a set of AMD families. Using common and rare variants, we observe significant association with two known AMD genes: CFH and ARMS2. Using rare variants, we find suggestive signals in four genes: ASAH1, CLEC6A, TMEM63C, and SGSM1. Intriguingly, ASAH1 is down-regulated in AMD aqueous humor, and ASAH1 deficiency leads to retinal inflammation and increased vulnerability to oxidative stress. These findings were made possible by our GFLMM which model the effect of a major gene as a fixed mean, the polygenic contributions as a random variation, and the correlation of pedigree members by kinship coefficients. Simulations indicate that the GFLMM likelihood ratio tests (LRTs) accurately control the Type I error rates. The LRTs have similar or higher power than existing retrospective kernel and burden statistics. Our GFLMM-based statistics provide a new tool for conducting family-based genetic studies of complex diseases. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Gene-based analysis of bi-variate survival traits via functional regressions with applications to eye diseases.

Genetic studies of two related survival outcomes of a pleiotropic gene are commonly encountered but statistical models to analyze them are rarely developed. To analyze sequencing data, we propose mixed effect Cox proportional hazard models by functional regressions to perform gene-based joint association analysis of two survival traits motivated by our ongoing real studies. These models extend fixed effect Cox models of univariate survival traits by incorporating variations and correlation of multivariate survival traits into the models. The associations between genetic variants and two survival traits are tested by likelihood ratio test statistics. Extensive simulation studies suggest that type I error rates are well controlled and power performances are stable. The proposed models are applied to analyze bivariate survival traits of left and right eyes in the age-related macular degeneration progression.

Genome-wide association study of café-au-lait macule number in neurofibromatosis type 1.

BACKGROUND: Neurofibromatosis type 1 (NF1) is a tumor-predisposition disorder that arises due to pathogenic variants in tumor suppressor NF1. NF1 has variable expressivity that may be due, at least in part, from heritable elements such as modifier genes; however, few genetic modifiers have been identified to date. METHODS: In this study, we performed a genome-wide association analysis of the number of café-au-lait macules (CALM) that are considered a tumor-like trait as a clinical phenotype modifying NF1. RESULTS: A borderline genome-wide significant association was identified in the discovery cohort (CALM1, N = 112) between CALM number and rs12190451 (and rs3799603, r2  = 1.0; p = 7.4 × 10-8 ) in the intronic region of RPS6KA2. Although, this association was not replicated in the second cohort (CALM2, N = 59) and a meta-analysis did not show significantly associated variants in this region, a significant corroboration score (0.72) was obtained for the RPS6KA2 signal in the discovery cohort (CALM1) using Complementary Pairs Stability Selection for Genome-Wide Association Studies (ComPaSS-GWAS) analysis, suggesting that the lack of replication may be due to heterogeneity of the cohorts rather than type I error. CONCLUSION: rs12190451 is located in a melanocyte-specific enhancer and may influence RPS6KA2 expression in melanocytes-warranting further functional studies.

A genome-wide association study implicates the BMP7 locus as a risk factor for nonsyndromic metopic craniosynostosis.

Our previous genome-wide association study (GWAS) for sagittal nonsyndromic craniosynostosis (sNCS) provided important insights into the genetics of midline CS. In this study, we performed a GWAS for a second midline NCS, metopic NCS (mNCS), using 215 non-Hispanic white case-parent triads. We identified six variants with genome-wide significance (P ≤ 5 × 10-8): rs781716 (P = 4.71 × 10-9; odds ratio [OR] = 2.44) intronic to SPRY3; rs6127972 (P = 4.41 × 10-8; OR = 2.17) intronic to BMP7; rs62590971 (P = 6.22 × 10-9; OR = 0.34), located ~ 155 kb upstream from TGIF2LX; and rs2522623, rs2573826, and rs2754857, all intronic to PCDH11X (P = 1.76 × 10-8, OR = 0.45; P = 3.31 × 10-8, OR = 0.45; P = 1.09 × 10-8, OR = 0.44, respectively). We performed a replication study of these variants using an independent non-Hispanic white sample of 194 unrelated mNCS cases and 333 unaffected controls; only the association for rs6127972 (P = 0.004, OR = 1.45; meta-analysis P = 1.27 × 10-8, OR = 1.74) was replicated. Our meta-analysis examining single nucleotide polymorphisms common to both our mNCS and sNCS studies showed the strongest association for rs6127972 (P = 1.16 × 10-6). Our imputation analysis identified a linkage disequilibrium block encompassing rs6127972, which contained an enhancer overlapping a CTCF transcription factor binding site (chr20:55,798,821-55,798,917) that was significantly hypomethylated in mesenchymal stem cells derived from fused metopic compared to open sutures from the same probands. This study provides additional insights into genetic factors in midline CS.

Gene-based association analysis of survival traits via functional regression-based mixed effect cox models for related samples.

The importance to integrate survival analysis into genetics and genomics is widely recognized, but only a small number of statisticians have produced relevant work toward this study direction. For unrelated population data, functional regression (FR) models have been developed to test for association between a quantitative/dichotomous/survival trait and genetic variants in a gene region. In major gene association analysis, these models have higher power than sequence kernel association tests. In this paper, we extend this approach to analyze censored traits for family data or related samples using FR based mixed effect Cox models (FamCoxME). The FamCoxME model effect of major gene as fixed mean via functional data analysis techniques, the local gene or polygene variations or both as random, and the correlation of pedigree members by kinship coefficients or genetic relationship matrix or both. The association between the censored trait and the major gene is tested by likelihood ratio tests (FamCoxME FR LRT). Simulation results indicate that the LRT control the type I error rates accurately/conservatively and have good power levels when both local gene or polygene variations are modeled. The proposed methods were applied to analyze a breast cancer data set from the Consortium of Investigators of Modifiers of BRCA1 and BRCA2 (CIMBA). The FamCoxME provides a new tool for gene-based analysis of family-based studies or related samples.

Linear mixed models for association analysis of quantitative traits with next-generation sequencing data.

We develop linear mixed models (LMMs) and functional linear mixed models (FLMMs) for gene-based tests of association between a quantitative trait and genetic variants on pedigrees. The effects of a major gene are modeled as a fixed effect, the contributions of polygenes are modeled as a random effect, and the correlations of pedigree members are modeled via inbreeding/kinship coefficients. F -statistics and χ 2 likelihood ratio test (LRT) statistics based on the LMMs and FLMMs are constructed to test for association. We show empirically that the F -distributed statistics provide a good control of the type I error rate. The F -test statistics of the LMMs have similar or higher power than the FLMMs, kernel-based famSKAT (family-based sequence kernel association test), and burden test famBT (family-based burden test). The F -statistics of the FLMMs perform well when analyzing a combination of rare and common variants. For small samples, the LRT statistics of the FLMMs control the type I error rate well at the nominal levels α = 0.01 and 0.05 . For moderate/large samples, the LRT statistics of the FLMMs control the type I error rates well. The LRT statistics of the LMMs can lead to inflated type I error rates. The proposed models are useful in whole genome and whole exome association studies of complex traits.

ComPaSS-GWAS: A method to reduce type I error in genome-wide association studies when replication data are not available.

Results from association studies are traditionally corroborated by replicating the findings in an independent data set. Although replication studies may be comparable for the main trait or phenotype of interest, it is unlikely that secondary phenotypes will be comparable across studies, making replication problematic. Alternatively, there may simply not be a replication sample available because of the nature or frequency of the phenotype. In these situations, an approach based on complementary pairs stability selection for genome-wide association study (ComPaSS-GWAS), is proposed as an ad-hoc alternative to replication. In this method, the sample is randomly split into two conditionally independent halves multiple times (resamples) and a GWAS is performed on each half in each resample. Similar in spirit to testing for association with independent discovery and replication samples, a marker is corroborated if its p-value is significant in both halves of the resample. Simulation experiments were performed for both nongenetic and genetic models. The type I error rate and power of ComPaSS-GWAS were determined and compared to the statistical properties of a traditional GWAS. Simulation results show that the type I error rate decreased as the number of resamples increased with only a small reduction in power and that these results were comparable with those from a traditional GWAS. Blood levels of vitamin pyridoxal 5'-phosphate from the Trinity Student Study (TSS) were used to validate this approach. The results from the validation study were compared to, and were consistent with, those obtained from previously published independent replication data and functional studies.

The 677C→T variant of MTHFR is the major genetic modifier of biomarkers of folate status in a young, healthy Irish population.

Background: Genetic polymorphisms can explain some of the population- and individual-based variations in nutritional status biomarkers. Objective: We sought to screen the entire human genome for common genetic polymorphisms that influence folate-status biomarkers in healthy individuals. Design: We carried out candidate gene analyses and genome-wide association scans in 2232 young, healthy Irish subjects to evaluate which common genetic polymorphisms influence red blood cell folate, serum folate, and plasma total homocysteine. Results: The 5,10-methylenetetrahydrofolate reductase (MTHFR) 677C→T (rs1801133) variant was the major genetic modifier of all 3 folate-related biomarkers in this Irish population and reached genome-wide significance for red blood cell folate (P = 1.37 × 10-17), serum folate (P = 2.82 × 10-11), and plasma total homocysteine (P = 1.26 × 10-19) concentrations. A second polymorphism in the MTHFR gene (rs3753584, P = 1.09 × 10-11) was the only additional MTHFR variant to exhibit any significant independent effect on red blood cell folate. Other MTHFR variants, including the 1298A→C variant (rs1801131), appeared to reach genome-wide significance, but these variants shared linkage disequilibrium with MTHFR 677C→T and were not significant when analyzed in MTHFR 677CC homozygotes. No additional non-MTHFR modifiers of red blood cell or plasma folate were detected. Two additional genome-wide significant modifiers of plasma homocysteine were found in the region of the dipeptidase 1 (DPEP1) gene on chromosome 16 and the Twist neighbor B (TWISTNB) gene on chromosome 7. Conclusions: The MTHFR 677C→T variant is the predominant genetic modifier of folate status biomarkers in this healthy Irish population. It is not necessary to determine MTHFR 677C→T genotype to evaluate folate status because its effect is reflected in concentrations of standard folate biomarkers. The MTHFR 1298A→C variant had no independent effect on folate status biomarkers. To our knowledge, this is the first genome-wide association study report on red blood cell folate and the first report of an association between homocysteine and TWISTNB.

Genetic associations with childhood brain growth, defined in two longitudinal cohorts.

Genome-wide association studies (GWASs) are unraveling the genetics of adult brain neuroanatomy as measured by cross-sectional anatomic magnetic resonance imaging (aMRI). However, the genetic mechanisms that shape childhood brain development are, as yet, largely unexplored. In this study we identify common genetic variants associated with childhood brain development as defined by longitudinal aMRI. Genome-wide single nucleotide polymorphism (SNP) data were determined in two cohorts: one enriched for attention-deficit/hyperactivity disorder (ADHD) (LONG cohort: 458 participants; 119 with ADHD) and the other from a population-based cohort (Generation R: 257 participants). The growth of the brain's major regions (cerebral cortex, white matter, basal ganglia, and cerebellum) and one region of interest (the right lateral prefrontal cortex) were defined on all individuals from two aMRIs, and a GWAS and a pathway analysis were performed. In addition, association between polygenic risk for ADHD and brain growth was determined for the LONG cohort. For white matter growth, GWAS meta-analysis identified a genome-wide significant intergenic SNP (rs12386571, P = 9.09 × 10-9 ), near AKR1B10. This gene is part of the aldo-keto reductase superfamily and shows neural expression. No enrichment of neural pathways was detected and polygenic risk for ADHD was not associated with the brain growth phenotypes in the LONG cohort that was enriched for the diagnosis of ADHD. The study illustrates the use of a novel brain growth phenotype defined in vivo for further study.

A variant associated with sagittal nonsyndromic craniosynostosis alters the regulatory function of a non-coding element.

Craniosynostosis presents either as a nonsyndromic congenital anomaly or as a finding in nearly 200 genetic syndromes. Our previous genome-wide association study of sagittal nonsyndromic craniosynostosis identified associations with variants downstream from BMP2 and intronic in BBS9. Because no coding variants in BMP2 were identified, we hypothesized that conserved non-coding regulatory elements may alter BMP2 expression. In order to identify and characterize noncoding regulatory elements near BMP2, two conserved noncoding regions near the associated region on chromosome 20 were tested for regulatory activity with a Renilla luciferase assay. For a 711 base pair noncoding fragment encompassing the most strongly associated variant, rs1884302, the luciferase assay showed that the risk allele (C) of rs1884302 drives higher expression of the reporter than the common allele (T). When this same DNA fragment was tested in zebrafish transgenesis studies, a strikingly different expression pattern of the green fluorescent reporter was observed depending on whether the transgenic fish had the risk (C) or the common (T) allele at rs1884302. The in vitro results suggest that altered BMP2 regulatory function at rs1884302 may contribute to the etiology of sagittal nonsyndromic craniosynostosis. The in vivo results indicate that differences in regulatory activity depend on the presence of a C or T allele at rs1884302.

The FUT2 secretor variant p.Trp154Ter influences serum vitamin B12 concentration via holo-haptocorrin, but not holo-transcobalamin, and is associated with haptocorrin glycosylation.

Vitamin B12 deficiency is common in older individuals. Circulating vitamin B12 concentration can be used to diagnose deficiency, but this test has substantial false positive and false negative rates. We conducted genome-wide association studies (GWAS) in which we resolved total serum vitamin B12 into the fractions bound to transcobalamin and haptocorrin: two carrier proteins with very different biological properties. We replicated reported associations between total circulating vitamin B12 concentrations and a common null variant in FUT2. This allele determines the secretor phenotype in which blood group antigens are found in non-blood body fluids. Vitamin B12 bound to haptocorrin (holoHC) remained highly associated with FUT2 rs601338 (p.Trp154Ter). Transcobalamin bound vitamin B12 (holoTC) was not influenced by this variant. HoloTC is the bioactive the form of the vitamin and is taken up by all tissues. In contrast, holoHC is only taken up by the liver. Using holoHC from individuals with known FUT2 genotypes, we demonstrated that FUT2 rs601338 genotype influences the glycosylation of haptocorrin. We then developed an experimental model demonstrating that holoHC is transported into cultured hepatic cells (HepG2) via the asialoglycoprotein receptor (ASGR). Our data challenge current published hypotheses on the influence of genetic variation on this clinically important measure and are consistent with a model in which FUT2 rs601338 influences holoHC by altering haptocorrin glycosylation, whereas B12 bound to non-glycosylated transcobalamin (i.e. holoTC) is not affected. Our findings explain some of the observed disparity between use of total B12 or holoTC as first-line clinical tests of vitamin B12 status.

A comparison study of multivariate fixed models and Gene Association with Multiple Traits (GAMuT) for next-generation sequencing.

In this paper, extensive simulations are performed to compare two statistical methods to analyze multiple correlated quantitative phenotypes: (1) approximate F-distributed tests of multivariate functional linear models (MFLM) and additive models of multivariate analysis of variance (MANOVA), and (2) Gene Association with Multiple Traits (GAMuT) for association testing of high-dimensional genotype data. It is shown that approximate F-distributed tests of MFLM and MANOVA have higher power and are more appropriate for major gene association analysis (i.e., scenarios in which some genetic variants have relatively large effects on the phenotypes); GAMuT has higher power and is more appropriate for analyzing polygenic effects (i.e., effects from a large number of genetic variants each of which contributes a small amount to the phenotypes). MFLM and MANOVA are very flexible and can be used to perform association analysis for (i) rare variants, (ii) common variants, and (iii) a combination of rare and common variants. Although GAMuT was designed to analyze rare variants, it can be applied to analyze a combination of rare and common variants and it performs well when (1) the number of genetic variants is large and (2) each variant contributes a small amount to the phenotypes (i.e., polygenes). MFLM and MANOVA are fixed effect models that perform well for major gene association analysis. GAMuT can be viewed as an extension of sequence kernel association tests (SKAT). Both GAMuT and SKAT are more appropriate for analyzing polygenic effects and they perform well not only in the rare variant case, but also in the case of a combination of rare and common variants. Data analyses of European cohorts and the Trinity Students Study are presented to compare the performance of the two methods.

Type I error rates of rare single nucleotide variants are inflated in tests of association with non-normally distributed traits using simple linear regression methods.

In this study, the effects of (a) the minor allele frequency of the single nucleotide variant (SNV), (b) the degree of departure from normality of the trait, and (c) the position of the SNVs on type I error rates were investigated in the Genetic Analysis Workshop (GAW) 19 whole exome sequence data. To test the distribution of the type I error rate, 5 simulated traits were considered: standard normal and gamma distributed traits; 2 transformed versions of the gamma trait (log10 and rank-based inverse normal transformations); and trait Q1 provided by GAW 19. Each trait was tested with 313,340 SNVs. Tests of association were performed with simple linear regression and average type I error rates were determined for minor allele frequency classes. Rare SNVs (minor allele frequency < 0.05) showed inflated type I error rates for non-normally distributed traits that increased as the minor allele frequency decreased. The inflation of average type I error rates increased as the significance threshold decreased. Normally distributed traits did not show inflated type I error rates with respect to the minor allele frequency for rare SNVs. There was no consistent effect of transformation on the uniformity of the distribution of the location of SNVs with a type I error.

A Comparison Study of Fixed and Mixed Effect Models for Gene Level Association Studies of Complex Traits.

In association studies of complex traits, fixed-effect regression models are usually used to test for association between traits and major gene loci. In recent years, variance-component tests based on mixed models were developed for region-based genetic variant association tests. In the mixed models, the association is tested by a null hypothesis of zero variance via a sequence kernel association test (SKAT), its optimal unified test (SKAT-O), and a combined sum test of rare and common variant effect (SKAT-C). Although there are some comparison studies to evaluate the performance of mixed and fixed models, there is no systematic analysis to determine when the mixed models perform better and when the fixed models perform better. Here we evaluated, based on extensive simulations, the performance of the fixed and mixed model statistics, using genetic variants located in 3, 6, 9, 12, and 15 kb simulated regions. We compared the performance of three models: (i) mixed models that lead to SKAT, SKAT-O, and SKAT-C, (ii) traditional fixed-effect additive models, and (iii) fixed-effect functional regression models. To evaluate the type I error rates of the tests of fixed models, we generated genotype data by two methods: (i) using all variants, (ii) using only rare variants. We found that the fixed-effect tests accurately control or have low false positive rates. We performed simulation analyses to compare power for two scenarios: (i) all causal variants are rare, (ii) some causal variants are rare and some are common. Either one or both of the fixed-effect models performed better than or similar to the mixed models except when (1) the region sizes are 12 and 15 kb and (2) effect sizes are small. Therefore, the assumption of mixed models could be satisfied and SKAT/SKAT-O/SKAT-C could perform better if the number of causal variants is large and each causal variant contributes a small amount to the traits (i.e., polygenes). In major gene association studies, we argue that the fixed-effect models perform better or similarly to mixed models in most cases because some variants should affect the traits relatively large. In practice, it makes sense to perform analysis by both the fixed and mixed effect models and to make a comparison, and this can be readily done using our R codes and the SKAT packages.

Evaluation of IRX Genes and Conserved Noncoding Elements in a Region on 5p13.3 Linked to Families with Familial Idiopathic Scoliosis and Kyphosis.

Because of genetic heterogeneity present in idiopathic scoliosis, we previously defined clinical subsets (a priori) from a sample of families with idiopathic scoliosis to find genes involved with spinal curvature. Previous genome-wide linkage analysis of seven families with at least two individuals with kyphoscoliosis found linkage (P-value = 0.002) in a 3.5-Mb region on 5p13.3 containing only three known genes, IRX1, IRX2, and IRX4 In this study, the exons of IRX1, IRX2, and IRX4, the conserved noncoding elements in the region, and the exons of a nonprotein coding RNA, LOC285577, were sequenced. No functional sequence variants were identified. An intrafamilial test of association found several associated noncoding single nucleotide variants. The strongest association was with rs12517904 (P = 0.00004), located 6.5 kb downstream from IRX1 In one family, the genotypes of nine variants differed from the reference allele in all individuals with kyphoscoliosis, and two of three individuals with scoliosis, but did not differ from the reference allele in all other genotyped individuals. One of these variants, rs117273909, was located in a conserved noncoding region that functions as an enhancer in mice. To test whether the variant allele at rs117273909 had an effect on enhancer activity, zebrafish transgenesis was performed with overlapping fragments of 198 and 687 bp containing either the wild type or the variant allele. Our data suggests that this region acts as a regulatory element; however, its size and target gene(s) need to be identified to determine its role in idiopathic scoliosis.

A Common Polymorphism in HIBCH Influences Methylmalonic Acid Concentrations in Blood Independently of Cobalamin.

Methylmalonic acid (MMA) is a by-product of propionic acid metabolism through the vitamin B12 (cobalamin)-dependent enzyme methylmalonyl CoA mutase. Elevated MMA concentrations are a hallmark of several inborn errors of metabolism and indicators of cobalamin deficiency in older persons. In a genome-wide analysis of 2,210 healthy young Irish adults (median age 22 years) we identified a strong association of plasma MMA with SNPs in 3-hydroxyisobutyryl-CoA hydrolase (HIBCH, p = 8.42 × 10(-89)) and acyl-CoA synthetase family member 3 (ACSF3, p = 3.48 × 10(-19)). These loci accounted for 12% of the variance in MMA concentration. The most strongly associated SNP (HIBCH rs291466; c:2T>C) causes a missense change of the initiator methionine codon (minor-allele frequency = 0.43) to threonine. Surprisingly, the resulting variant, p.Met1?, is associated with increased expression of HIBCH mRNA and encoded protein. These homozygotes had, on average, 46% higher MMA concentrations than methionine-encoding homozygotes in young adults with generally low MMA concentrations (0.17 [0.14-0.21] µmol/L; median [25(th)-75(th) quartile]). The association between MMA levels and HIBCH rs291466 was highly significant in a replication cohort of 1,481 older individuals (median age 79 years) with elevated plasma MMA concentrations (0.34 [0.24-0.51] µmol/L; p = 4.0 × 10(-26)). In a longitudinal study of 185 pregnant women and their newborns, the association of this SNP remained significant across the gestational trimesters and in newborns. HIBCH is unique to valine catabolism. Studies evaluating flux through the valine catabolic pathway in humans should account for these variants. Furthermore, this SNP could help resolve equivocal clinical tests where plasma MMA values have been used to diagnose cobalamin deficiency.

Common Variants at Putative Regulatory Sites of the Tissue Nonspecific Alkaline Phosphatase Gene Influence Circulating Pyridoxal 5'-Phosphate Concentration in Healthy Adults.

BACKGROUND: Vitamin B-6 interconversion enzymes are important for supplying pyridoxal 5'-phosphate (PLP), the co-enzyme form, to tissues. Variants in the genes for these enzymes [tissue nonspecific alkaline phosphatase (ALPL), pyridoxamine 5'-phosphate oxidase, pyridoxal kinase, and pyridoxal phosphatase] could affect enzyme function and vitamin B-6 status. OBJECTIVES: We tested whether single-nucleotide polymorphisms (SNPs) in these genes influence vitamin B-6 status markers [plasma PLP, pyridoxal (PL), and 4-pyridoxic acid (PA)], and explored potential functional effects of the SNPs. METHODS: Study subjects were young, healthy adults from Ireland (n = 2345). We measured plasma PLP, PL, and PA with liquid chromatography-tandem mass spectrometry and genotyped 66 tag SNPs in the 4 genes. We tested for associations with single SNPs in candidate genes and also performed genome-wide association study (GWAS) and gene-based analyses. RESULTS: Seventeen SNPs in ALPL were associated with altered plasma PLP in candidate gene analyses (P < 1.89 × 10(-4)). In the GWAS, 5 additional ALPL SNPs were associated with altered plasma PLP (P < 5.0 × 10(-8)). Gene-based analyses that used the functional linear model ß-spline (P = 4.04 × 10(-15)) and Fourier spline (P = 5.87 × 10(-15)) methods also showed associations between ALPL and altered plasma PLP. No SNPs in other genes were associated with plasma PLP. The association of the minor CC genotype of 1 ALPL SNP, rs1256341, with reduced ALPL expression in the HapMap Northern European ancestry population is consistent with the positive association between the CC genotype and plasma PLP in our study (P = 0.008). No SNP was associated with altered plasma PL or PA. CONCLUSIONS: In healthy adults, common variants in ALPL influence plasma PLP concentration, the most frequently used biomarker for vitamin B-6 status. Whether these associations are indicative of functional changes in vitamin B-6 status requires more investigation.