Mapping Bias Overestimates Reference Allele Frequencies at the HLA Genes in the 1000 Genomes Project Phase I Data.

Next-generation sequencing (NGS) technologies have become the standard for data generation in studies of population genomics, as the 1000 Genomes Project (1000G). However, these techniques are known to be problematic when applied to highly polymorphic genomic regions, such as the human leukocyte antigen (HLA) genes. Because accurate genotype calls and allele frequency estimations are crucial to population genomics analyses, it is important to assess the reliability of NGS data. Here, we evaluate the reliability of genotype calls and allele frequency estimates of the single-nucleotide polymorphisms (SNPs) reported by 1000G (phase I) at five HLA genes (HLA-A, -B, -C, -DRB1, and -DQB1). We take advantage of the availability of HLA Sanger sequencing of 930 of the 1092 1000G samples and use this as a gold standard to benchmark the 1000G data. We document that 18.6% of SNP genotype calls in HLA genes are incorrect and that allele frequencies are estimated with an error greater than ±0.1 at approximately 25% of the SNPs in HLA genes. We found a bias toward overestimation of reference allele frequency for the 1000G data, indicating mapping bias is an important cause of error in frequency estimation in this dataset. We provide a list of sites that have poor allele frequency estimates and discuss the outcomes of including those sites in different kinds of analyses. Because the HLA region is the most polymorphic in the human genome, our results provide insights into the challenges of using of NGS data at other genomic regions of high diversity.

LC-MS/MS based assay and reference intervals in children and adolescents for oxysterols elevated in Niemann-Pick diseases.

BACKGROUND: Niemann-Pick type C (NP-C) is a rare progressive neurodegenerative lipid storage disorder with heterogeneous clinical presentation and challenging diagnostic procedures. Recently oxysterols have been reported to be specific biomarkers for NP-C but knowledge on the intra-individual variation and on reference intervals in children and adolescents are lacking. METHODS: We established a LC-MS/MS assay to measure Cholestane-3β, 5α, 6β-triol (C-triol) and 7-Ketocholesterol (7-KC) following Steglich esterification. To assess reference intervals and intra-individual variation we determined oxysterols in 148 children and adolescents from 0 to 18 years and repeat measurements in 19 of them. RESULTS: The reported method is linear (r>0.99), sensitive (detection limit of 0.03 ng/mL [0.07 nM] for C-triol, and 0.54 ng/mL [1.35 nM] for 7-KC) and precise, with an intra-day imprecision of 4.8% and 4.1%, and an inter-day imprecision of 7.0% and 11.0% for C-triol (28 ng/ml, 67 nM) and 7-KC (32 ng/ml, 80 nM), respectively. Recoveries for 7-KC and C-triol range between 93% and 107%. The upper reference limit obtained for C-triol is 40.4 ng/mL (95% CI: 26.4-61.7 ng/mL, 96.0 nM, 95% CI: 62.8-146.7 nM) and 75.0 ng/mL for 7-KC (95% CI: 55.5-102.5 ng/mL, 187.2 nM, 95% CI: 138.53-255.8 nM), with no age or gender dependency. Both oxysterols have a broad intra-individual variation of 46%±23% for C-triol and 52%±29% for 7-KC. Nevertheless, all Niemann-Pick patients showed increased C-triol levels including Niemann-Pick type A and B patients. CONCLUSIONS: The LC-MS/MS assay is a robust assay to quantify C-triol and 7-KC in plasma with well documented reference intervals in children and adolescents to screen for NP-C in the pediatric population. In addition our results suggest that especially the C-triol is a biomarker for all three Niemann-Pick diseases.

Estimation of reference curves for the urinary steroid metabolome in the first year of life in healthy children : Tracing the complexity of human postnatal steroidogenesis.

CONTEXT: Complex steroid disorders such as P450 oxidoreductase deficiency or apparent cortisone reductase deficiency may be recognized by steroid profiling using chromatographic mass spectrometric methods. These methods are highly specific and sensitive, and provide a complete spectrum of steroid metabolites in a single measurement of one sample which makes them superior to immunoassays. The steroid metabolome during the fetal-neonatal transition is characterized by (a) the metabolites of the fetal-placental unit at birth, (b) the fetal adrenal androgens until its involution 3-6 months postnatally, and (c) the steroid metabolites produced by the developing endocrine organs. All these developmental events change the steroid metabolome in an age- and sex-dependent manner during the first year of life. OBJECTIVE: The aim of this study was to provide normative values for the urinary steroid metabolome of healthy newborns at short time intervals in the first year of life. METHODS: We conducted a prospective, longitudinal study to measure 67 urinary steroid metabolites in 21 male and 22 female term healthy newborn infants at 13 time-points from week 1 to week 49 of life. Urine samples were collected from newborn infants before discharge from hospital and from healthy infants at home. Steroid metabolites were measured by gas chromatography-mass spectrometry (GC-MS) and steroid concentrations corrected for urinary creatinine excretion were calculated. RESULTS: 61 steroids showed age and 15 steroids sex specificity. Highest urinary steroid concentrations were found in both sexes for progesterone derivatives, in particular 20α-DH-5α-DH-progesterone, and for highly polar 6α-hydroxylated glucocorticoids. The steroids peaked at week 3 and decreased by ∼80% at week 25 in both sexes. The decline of progestins, androgens and estrogens was more pronounced than of glucocorticoids whereas the excretion of corticosterone and its metabolites and of mineralocorticoids remained constant during the first year of life. CONCLUSION: The urinary steroid profile changes dramatically during the first year of life and correlates with the physiologic developmental changes during the fetal-neonatal transition. Thus detailed normative data during this time period permit the use of steroid profiling as a powerful diagnostic tool.

Reference intervals for 24 laboratory parameters determined in 24-hour urine collections.

BACKGROUND: Reference intervals for many laboratory parameters determined in 24-h urine collections are either not publicly available or based on small numbers, not sex specific or not from a representative sample. METHODS: Osmolality and concentrations or enzymatic activities of sodium, potassium, chloride, glucose, creatinine, citrate, cortisol, pancreatic α-amylase, total protein, albumin, transferrin, immunoglobulin G, α1-microglobulin, α2-macroglobulin, as well as porphyrins and their precursors (δ-aminolevulinic acid and porphobilinogen) were determined in 241 24-h urine samples of a population-based cohort of asymptomatic adults (121 men and 120 women). For 16 of these 24 parameters creatinine-normalized ratios were calculated based on 24-h urine creatinine. The reference intervals for these parameters were calculated according to the CLSI C28-A3 statistical guidelines. RESULTS: By contrast to most published reference intervals, which do not stratify for sex, reference intervals of 12 of 24 laboratory parameters in 24-h urine collections and of eight of 16 parameters as creatinine-normalized ratios differed significantly between men and women. For six parameters calculated as 24-h urine excretion and four parameters calculated as creatinine-normalized ratios no reference intervals had been published before. For some parameters we found significant and relevant deviations from previously reported reference intervals, most notably for 24-h urine cortisol in women. Ten 24-h urine parameters showed weak or moderate sex-specific correlations with age. CONCLUSIONS: By applying up-to-date analytical methods and clinical chemistry analyzers to 24-h urine collections from a large population-based cohort we provide as yet the most comprehensive set of sex-specific reference intervals calculated according to CLSI guidelines for parameters determined in 24-h urine collections.