Comparison of GC-MS and GC×GC-MS in the Analysis of Human Serum Samples for Biomarker Discovery

We compared the performance of gas chromatography time-of-flight mass spectrometry (GC-MS) and comprehensive two-dimensional gas chromatography mass spectrometry (GC×GC-MS) for metabolite biomarker discovery. Metabolite extracts from 109 human serum samples were analyzed on both platforms with a pooled serum sample analyzed after every 9 biological samples for the purpose of quality control (QC). The experimental data derived from the pooled QC samples showed that the GC×GC-MS platform detected about three times as many peaks as the GC-MS platform at a signal-to-noise ratio SNR ≥ 50, and three times the number of metabolites were identified by mass spectrum matching with a spectral similarity score <i>R</i>_sim ≥ 600. Twenty-three metabolites had statistically significant abundance changes between the patient samples and the control samples in the GC-MS data set while 34 metabolites in the GC×GC-MS data set showed statistically significant differences. Among these two groups of metabolite biomarkers, nine metabolites were detected in both the GC-MS and GC×GC-MS data sets with the same direction and similar magnitude of abundance changes between the control and patient sample groups. Manual verification indicated that the difference in the number of the biomarkers discovered using these two platforms was mainly due to the limited resolution of chromatographic peaks by the GC-MS platform, which can result in severe peak overlap making subsequent spectrum deconvolution for metabolite identification and quantification difficult

Stratified Whole Genome Linkage Analysis of Chiari Type I Malformation Implicates Known Klippel-Feil Syndrome Genes as Putative Disease Candidates

<div><p>Chiari Type I Malformation (CMI) is characterized by displacement of the cerebellar tonsils below the base of the skull, resulting in significant neurologic morbidity. Although multiple lines of evidence support a genetic contribution to disease, no genes have been identified. We therefore conducted the largest whole genome linkage screen to date using 367 individuals from 66 families with at least two individuals presenting with nonsyndromic CMI with or without syringomyelia. Initial findings across all 66 families showed minimal evidence for linkage due to suspected genetic heterogeneity. In order to improve power to localize susceptibility genes, stratified linkage analyses were performed using clinical criteria to differentiate families based on etiologic factors. Families were stratified on the presence or absence of clinical features associated with connective tissue disorders (CTDs) since CMI and CTDs frequently co-occur and it has been proposed that CMI patients with CTDs represent a distinct class of patients with a different underlying disease mechanism. Stratified linkage analyses resulted in a marked increase in evidence of linkage to multiple genomic regions consistent with reduced genetic heterogeneity. Of particular interest were two regions (Chr8, Max LOD = 3.04; Chr12, Max LOD = 2.09) identified within the subset of “CTD-negative” families, both of which harbor growth differentiation factors (GDF6, GDF3) implicated in the development of Klippel-Feil syndrome (KFS). Interestingly, roughly 3–5% of CMI patients are diagnosed with KFS. In order to investigate the possibility that CMI and KFS are allelic, GDF3 and GDF6 were sequenced leading to the identification of a previously known KFS missense mutation and potential regulatory variants in GDF6. This study has demonstrated the value of reducing genetic heterogeneity by clinical stratification implicating several convincing biological candidates and further supporting the hypothesis that multiple, distinct mechanisms are responsible for CMI.</p></div

Segregation of the missense mutation, rs121909352 (A249E), in two CMI pedigrees.

<p>Family 9453 (A) and Family 9476 (B). Symbols shaded in black indicate a diagnosis of CMI with or without syringomyelia, small diamonds represent a miscarriage, and symbols shaded in grey indicate an uncertain diagnosis. 9453-0001 has been diagnosed with a suspected Chiari Malformation Type 0 and 9476–1001 has been diagnosed with tonsillar ectopia. “+/+” indicates homozygous for the reference allele; “+/−“ indicates heterozygous for the variant allele. Sequences were generated in both the forward and reverse direction and are shown below each sampled individual. Progeny 8 (Delray Beach, FL) was used to construct the pedigrees and Sequencher 5.0 (Ann Arbor, MI) was used to create the chromatograms.</p

Most significant two-point and multipoint LOD scores.^a

a<p>The top two most significant two-point results within each model and family subset as well as any maximum multipoint LOD score exceeding 2 are included.</p>b<p>When two markers are listed, the first corresponds to the marker used for the two-point result shown. The second corresponds to the closest marker included in the multipoint analysis.</p>c<p>LOD scores exceeding 2 are bold and LOD scores exceeding 3 are bold and italicized. For the parametric model, HLOD scores are shown.</p>d<p>Empirical p-values less than 0.05 are bold.</p><p>Abbreviations: CTD: connective tissue disorder, NPL: nonparametric linkage, LOD: logarithm of the odds, Emp: empirical, CW: chromosome-wide, GW: genome-wide, N/A: not applicable.</p

Two-point and multipoint LOD scores obtained from stratified analysis.

<p>Only chromosomes with a maximum multipoint LOD score >2 are shown. LOD score thresholds of 2 and 3 are indicated by the blue and red lines, respectively. Green points and lines represent LOD scores under a linear model, blue points and lines represent HLOD scores, and red points and black lines represent LOD scores under an exponential model. CTD-positive families: Chr1 (A), CTD-positive families: Chr9 (B), CTD-negative families: Chr8 (C), CTD-negative families: Chr9 (D), CTD-negative families: Chr12 (E), and CTD-negative families: Chr17 (F). Negative two-point and multipoint LOD scores are set to zero. Plots were created in R 2.15.0.</p

GDF6 and GDF3 selected sequence variants.^a

a<p>Only variants which were followed-up are shown here (See Methods section); Variants were validated by bidirectional sequencing and all sampled affected and unaffected individuals within each identified family were sequenced.</p>b<p>Base pair positions based on human genome build GRCh37/hg19.</p>c<p>The nomenclature used to describe novel variants was based on recommendations by the Human Genome Variation Society (den Dunnen and Antonarakis 2001). Nucleotide numbering was based on the GDF6 RefSeq genomic sequence, NG_008981.1, and intron-exon boundaries were defined based on the GDF6 mRNA sequence, NM_001001557.</p>d<p>Alleles: Reference allele/Alternate allele.</p>e<p>CMI MAF estimate based on all affected family members initially screened; 1KG MAF: Based on 1000 Genomes Integrated Phase 1 Release v3: European population.</p>f<p>Is sharing observed across all affected individuals within each family?</p>g<p>Numbers in parentheses: Numerator: number of sampled individuals carrying the variant, Denominator: total number of sampled individuals. Only affecteds were considered for “All affecteds” and only unaffecteds/uncertains were considered for “Reduced penetrance”.</p>h<p>MAF estimate was not available from 1000 Genomes; MAF estimate based on the Exome sequencing project: European population.</p>i<p>Individual suspected to have Chiari Malformation Type 0 is counted as “affected” for the purposes of this table.</p

Population characteristics.

a<p>Only considered genotyped individuals after exclusions were applied (See Methods section for details).</p>b<p>Mean +/− standard deviation [range].</p>c<p>Only considered affected individuals.</p><p>Abbreviations: CMI: Chiari Malformation Type I; No.: number.</p

Case-Only Survival Analysis Reveals Unique Effects of Genotype, Sex, and Coronary Disease Severity on Survivorship

<div><p>Survival bias may unduly impact genetic association with complex diseases; gene-specific survival effects may further complicate such investigations. Coronary artery disease (CAD) is a complex phenotype for which little is understood about gene-specific survival effects; yet, such information can offer insight into refining genetic associations, improving replications, and can provide candidate genes for both mortality risk and improved survivorship in CAD. Building on our previous work, the purpose of this current study was to: evaluate <i>LSAMP</i> SNP-specific hazards for all-cause mortality post-catheterization in a larger cohort of our CAD cases; and, perform additional replication in an independent dataset. We examined two <i>LSAMP</i> SNPs—rs1462845 and rs6788787—using CAD case-only Cox proportional hazards regression for additive genetic effects, censored on time-to-all-cause mortality or last follow-up among Caucasian subjects from the Catheterization Genetics Study (CATHGEN; <i>n</i> = 2,224) and the Intermountain Heart Collaborative Study (IMHC; <i>n</i> = 3,008). Only after controlling for age, sex, body mass index, histories of smoking, type 2 diabetes, hyperlipidemia and hypertension (<i>HR</i> = 1.11, 95%<i>CI</i> = 1.01–1.22, <i>p</i> = 0.032), rs1462845 conferred significantly increased hazards of all-cause mortality among CAD cases. Even after controlling for multiple covariates, but in only the primary cohort, rs6788787 conferred significantly improved survival (<i>HR</i> = 0.80, 95% <i>CI</i> = 0.69–0.92, <i>p</i> = 0.002). Post-hoc analyses further stratifying by sex and disease severity revealed replicated effects for rs1462845: even after adjusting for aforementioned covariates and coronary interventional procedures, males with severe burden of CAD had significantly amplified hazards of death with the minor variant of rs1462845 in both cohorts (<i>HR</i> = 1.29, <i>95% CI</i> = 1.08–1.55, <i>p</i> = 0.00456; replication <i>HR</i> = 1.25, <i>95% CI</i> = 1.05–1.49, <i>p</i> = 0.013). Kaplan-Meier curves revealed unique cohort-specific genotype effects on survival. Additional analyses demonstrated that the homozygous risk genotype (‘A/A’) fully explained the increased hazard in both cohorts. None of the post-hoc analyses in control subjects were significant for any model. This suggests that genetic effects of rs1462845 on survival are unique to CAD presence. This represents formal, replicated evidence of genetic contribution of rs1462845 to increased risk for all-cause mortality; the contribution is unique to CAD case status and specific to males with severe burden of CAD.</p></div

Kaplan–Meier survival curves for CATHGEN males with severe CAD by genotype for <i>LSAMP</i> SNP rs6788787.

<p><i>X</i>-axis displays the number of days from catheterization to death (all-cause mortality). <i>Y</i>-axis displays the Kaplan–Meier survival probability by genotype. A is the minor allele; GG, wild-type genotype (reference; black curve); GA, heterozygous genotype; and AA, risk homozygous genotype (red curve). Only after controlling for covariates was this model significant for reduced hazards of death by genotype (<i>HR</i> = 0.71, <i>95% CI</i> = 0.54–0.93, <i>p</i> = 0.0148, additive genetic model).</p

Hazards of Death in Caucasian Control Subjects.

<p>Hazards of Death in Caucasian Control Subjects.</p