Correction: Investigation of common, low-frequency and rare genome-wide variation in anorexia nervosa.

The fortieth author's name was listed incorrectly. The correct presentation is A Keski-Rahkonen.

Investigation of common, low-frequency and rare genome-wide variation in anorexia nervosa.

Anorexia nervosa (AN) is a complex neuropsychiatric disorder presenting with dangerously low body weight, and a deep and persistent fear of gaining weight. To date, only one genome-wide significant locus associated with AN has been identified. We performed an exome-chip based genome-wide association studies (GWAS) in 2158 cases from nine populations of European origin and 15 485 ancestrally matched controls. Unlike previous studies, this GWAS also probed association in low-frequency and rare variants. Sixteen independent variants were taken forward for in silico and de novo replication (11 common and 5 rare). No findings reached genome-wide significance. Two notable common variants were identified: rs10791286, an intronic variant in OPCML (P=9.89 × 10-6), and rs7700147, an intergenic variant (P=2.93 × 10-5). No low-frequency variant associations were identified at genome-wide significance, although the study was well-powered to detect low-frequency variants with large effect sizes, suggesting that there may be no AN loci in this genomic search space with large effect sizes.

Lifestyle may modify the glucose-raising effect of genetic loci. A study in the Greek population.

BACKGROUND AND AIMS: Lifestyle habits including dietary intake and physical activity are closely associated with multiple body processes including glucose metabolism and are known to affect human health. Recent genome-wide association studies have identified several single nucleotide polymorphisms (SNPs) associated with glucose levels. The hypothesis tested here is whether a healthy lifestyle assessed via a score is associated with glycaemic traits and whether there is an interaction between the lifestyle and known glucose-raising genetic variants in association with glycaemic traits. METHODS AND RESULTS: Participants of Greek descent from the THISEAS study were included in this analysis. We developed a glucose preventive score (GPS) including dietary and physical activity characteristics. We also modelled a weighted genetic risk score (wGRS), based on 20 known glucose-raising loci, in order to investigate the impact of lifestyle-gene interaction on glucose levels. The GPS was observed to be significantly associated with lower glucose concentrations (ß ± SE: -0.083 ± 0.021 mmol/L, P = 1.6 × 10(-04)) and the wGRS, as expected, with increased glucose levels (ß ± SE: 0.020 ± 0.007 mmol/L, P = 8.4 × 10(-3)). The association of the wGRS with glucose levels was attenuated after interaction with the GPS. A higher GPS indicated decreasing glucose levels in the presence of an increasing wGRS (ß interaction ± SE: -0.019 ± 0.007 mmol/L, P = 0.014). CONCLUSION: Our results indicate that lower glucose levels underlie a healthier lifestyle and also support an interaction between the wGRS for known glycaemic loci and GPS associated with lower glucose levels. These scores could be useful tools for monitoring glucose metabolism.

A novel interaction between the FLJ33534 locus and smoking in obesity: a genome-wide study of 14 131 Pakistani adults.

BACKGROUND: Obesity is a complex disease caused by the interplay of genetic and lifestyle factors, but identification of gene-lifestyle interactions in obesity has remained challenging. Few large-scale studies have reported use of genome-wide approaches to investigate gene-lifestyle interactions in obesity. METHODS: In the Pakistan Risk of Myocardial Infraction Study, a cross-sectional study based in Pakistan, we calculated body mass index (BMI) variance estimates (square of the residual of inverse-normal transformed BMI z-score) in 14 131 participants and conducted genome-wide heterogeneity of variance analyses (GWHVA) for this outcome. All analyses were adjusted for age, age(2), sex and genetic ancestry. RESULTS: The GWHVA analyses identified an intronic variant, rs140133294, in the FLJ33544 gene in association with BMI variance (P-value=3.1 × 10(-8)). In explicit tests of gene × lifestyle interaction, smoking was found to significantly modify the effect of rs140133294 on BMI (Pinteraction=0.0005), whereby the minor allele (T) was associated with lower BMI in current smokers, while positively associated with BMI in never smokers. Analyses of ENCODE data at the FLJ33534 locus revealed features indicative of open chromatin and high confidence DNA-binding motifs for several transcription factors, providing suggestive biological support for a mechanism of interaction. CONCLUSIONS: In summary, we have identified a novel interaction between smoking and variation at the FLJ33534 locus in relation to BMI in people from Pakistan.

Differential methylation of the TRPA1 promoter in pain sensitivity.

Chronic pain is a global public health problem, but the underlying molecular mechanisms are not fully understood. Here we examine genome-wide DNA methylation, first in 50 identical twins discordant for heat pain sensitivity and then in 50 further unrelated individuals. Whole-blood DNA methylation was characterized at 5.2 million loci by MeDIP sequencing and assessed longitudinally to identify differentially methylated regions associated with high or low pain sensitivity (pain DMRs). Nine meta-analysis pain DMRs show robust evidence for association (false discovery rate 5%) with the strongest signal in the pain gene TRPA1 (P=1.2 × 10(-13)). Several pain DMRs show longitudinal stability consistent with susceptibility effects, have similar methylation levels in the brain and altered expression in the skin. Our approach identifies epigenetic changes in both novel and established candidate genes that provide molecular insights into pain and may generalize to other complex traits.

The link between family history and risk of type 2 diabetes is not explained by anthropometric, lifestyle or genetic risk factors: the EPIC-InterAct study.

AIMS/HYPOTHESIS: Although a family history of type 2 diabetes is a strong risk factor for the disease, the factors mediating this excess risk are poorly understood. In the InterAct case-cohort study, we investigated the association between a family history of diabetes among different family members and the incidence of type 2 diabetes, as well as the extent to which genetic, anthropometric and lifestyle risk factors mediated this association. METHODS: A total of 13,869 individuals (including 6,168 incident cases of type 2 diabetes) had family history data available, and 6,887 individuals had complete data on all mediators. Country-specific Prentice-weighted Cox models were fitted within country, and HRs were combined using random effects meta-analysis. Lifestyle and anthropometric measurements were performed at baseline, and a genetic risk score comprising 35 polymorphisms associated with type 2 diabetes was created. RESULTS: A family history of type 2 diabetes was associated with a higher incidence of the condition (HR 2.72, 95% CI 2.48, 2.99). Adjustment for established risk factors including BMI and waist circumference only modestly attenuated this association (HR 2.44, 95% CI 2.03, 2.95); the genetic score alone explained only 2% of the family history-associated risk of type 2 diabetes. The greatest risk of type 2 diabetes was observed in those with a biparental history of type 2 diabetes (HR 5.14, 95% CI 3.74, 7.07) and those whose parents had been diagnosed with diabetes at a younger age (<50 years; HR 4.69, 95% CI 3.35, 6.58), an effect largely confined to a maternal family history. CONCLUSIONS/INTERPRETATION: Prominent lifestyle, anthropometric and genetic risk factors explained only a marginal proportion of the excess risk associated with family history, highlighting the fact that family history remains a strong, independent and easily assessed risk factor for type 2 diabetes. Discovering factors that will explain the association of family history with type 2 diabetes risk will provide important insight into the aetiology of type 2 diabetes.

Design and cohort description of the InterAct Project: an examination of the interaction of genetic and lifestyle factors on the incidence of type 2 diabetes in the EPIC Study.

AIMS/HYPOTHESIS: Studying gene-lifestyle interaction may help to identify lifestyle factors that modify genetic susceptibility and uncover genetic loci exerting important subgroup effects. Adequately powered studies with prospective, unbiased, standardised assessment of key behavioural factors for gene-lifestyle studies are lacking. This case-cohort study aims to investigate how genetic and potentially modifiable lifestyle and behavioural factors, particularly diet and physical activity, interact in their influence on the risk of developing type 2 diabetes. METHODS: Incident cases of type 2 diabetes occurring in European Prospective Investigation into Cancer and Nutrition (EPIC) cohorts between 1991 and 2007 from eight of the ten EPIC countries were ascertained and verified. Prentice-weighted Cox regression and random-effects meta-analyses were used to investigate differences in diabetes incidence by age and sex. RESULTS: A total of 12,403 verified incident cases of type 2 diabetes occurred during 3.99 million person-years of follow-up of 340,234 EPIC participants eligible for InterAct. We defined a centre-stratified subcohort of 16,154 individuals for comparative analyses. Individuals with incident diabetes who were randomly selected into the subcohort (n = 778) were included as cases in the analyses. All prevalent diabetes cases were excluded from the study. InterAct cases were followed-up for an average of 6.9 years; 49.7% were men. Mean baseline age and age at diagnosis were 55.6 and 62.5 years, mean BMI and waist circumference values were 29.4 kg/m(2) and 102.7 cm in men, and 30.1 kg/m(2) and 92.8 cm in women, respectively. Risk of type 2 diabetes increased linearly with age, with an overall HR of 1.56 (95% CI 1.48-1.64) for a 10 year age difference, adjusted for sex. A male excess in the risk of incident diabetes was consistently observed across all countries, with a pooled HR of 1.51 (95% CI 1.39-1.64), adjusted for age. CONCLUSIONS/INTERPRETATION: InterAct is a large, well-powered, prospective study that will inform our understanding of the interplay between genes and lifestyle factors on the risk of type 2 diabetes development.

Insights into the genetic architecture of osteoarthritis from stage 1 of the arcOGEN study.

OBJECTIVES: The genetic aetiology of osteoarthritis has not yet been elucidated. To enable a well-powered genome-wide association study (GWAS) for osteoarthritis, the authors have formed the arcOGEN Consortium, a UK-wide collaborative effort aiming to scan genome-wide over 7500 osteoarthritis cases in a two-stage genome-wide association scan. Here the authors report the findings of the stage 1 interim analysis. METHODS: The authors have performed a genome-wide association scan for knee and hip osteoarthritis in 3177 cases and 4894 population-based controls from the UK. Replication of promising signals was carried out in silico in five further scans (44,449 individuals), and de novo in 14 534 independent samples, all of European descent. RESULTS: None of the association signals the authors identified reach genome-wide levels of statistical significance, therefore stressing the need for corroboration in sample sets of a larger size. Application of analytical approaches to examine the allelic architecture of disease to the stage 1 genome-wide association scan data suggests that osteoarthritis is a highly polygenic disease with multiple risk variants conferring small effects. CONCLUSIONS: Identifying loci conferring susceptibility to osteoarthritis will require large-scale sample sizes and well-defined phenotypes to minimise heterogeneity.

Integration of genetic, clinical, and INR data to refine warfarin dosing.

Well-characterized genes that affect warfarin metabolism (cytochrome P450 (CYP) 2C9) and sensitivity (vitamin K epoxide reductase complex 1 (VKORC1)) explain one-third of the variability in therapeutic dose before the international normalized ratio (INR) is measured. To determine genotypic relevance after INR becomes available, we derived clinical and pharmacogenetic refinement algorithms on the basis of INR values (on day 4 or 5 of therapy), clinical factors, and genotype. After adjusting for INR, CYP2C9 and VKORC1 genotypes remained significant predictors (P < 0.001) of warfarin dose. The clinical algorithm had an R(2) of 48% (median absolute error (MAE): 7.0 mg/week) and the pharmacogenetic algorithm had an R(2) of 63% (MAE: 5.5 mg/week) in the derivation set (N = 969). In independent validation sets, the R(2) was 26-43% with the clinical algorithm and 42-58% when genotype was added (P = 0.002). After several days of therapy, a pharmacogenetic algorithm estimates the therapeutic warfarin dose more accurately than one using clinical factors and INR response alone.

A pharmacometric model describing the relationship between warfarin dose and INR response with respect to variations in CYP2C9, VKORC1, and age.

The objective of the study was to update a previous NONMEM model to describe the relationship between warfarin dose and international normalized ratio (INR) response, to decrease the dependence of the model on pharmacokinetic (PK) data, and to improve the characterization of rare genotype combinations. The effects of age and CYP2C9 genotype on S-warfarin clearance were estimated from high-quality PK data. Thereafter, a temporal dose-response (K-PD) model was developed from information on dose, INR, age, and CYP2C9 and VKORC1 genotype, with drug clearance as a covariate. Two transit compartment chains accounted for the delay between exposure and response. CYP2C9 genotype was identified as the single most important predictor of required dose, causing a difference of up to 4.2-fold in the maintenance dose. VKORC1 accounted for a difference of up to 2.1-fold in dose, and age reduced the dose requirement by ~6% per decade. This reformulated K-PD model decreases dependence on PK data and enables robust assessment of INR response and dose predictions, even in individuals with rare genotype combinations.

A genome-wide association study suggests that a locus within the ataxin 2 binding protein 1 gene is associated with hand osteoarthritis: the Treat-OA consortium.

To identify the susceptibility gene in hand osteoarthritis (OA) the authors used a two-stage approach genome-wide association study using two discovery samples (the TwinsUK cohort and the Rotterdam discovery subset; a total of 1804 subjects) and four replication samples (the Chingford Study, the Chuvasha Skeletal Aging Study, the Rotterdam replication subset and the Genetics, Arthrosis, and Progression (GARP) Study; a total of 3266 people). Five single-nucleotide polymorphisms (SNPs) had a likelihood of association with hand OA in the discovery stage and one of them (rs716508), was successfully confirmed in the replication stage (meta-analysis p = 1.81x10(-5)). The C allele conferred a reduced risk of 33% to 41% using a case-control definition. The SNP is located in intron 1 of the A2BP1 gene. This study also found that the same allele of the SNP significantly reduced bone density at both the hip and spine (p<0.01), suggesting the potential mechanism of the gene in hand OA might be via effects on subchondral bone. The authors' findings provide a potential new insight into genetic mechanisms in the development of hand OA.

A genome-wide association study identifies a novel locus on chromosome 18q12.2 influencing white cell telomere length.

BACKGROUND: Telomere length is a predictor for a number of common age related diseases and is a heritable trait. METHODS AND RESULTS: To identify new loci associated with mean leukocyte telomere length we conducted a genome wide association study of 314,075 single nucleotide polymorphisms (SNPs) and validated the results in a second cohort (n for both cohorts combined = 2790). We identified two novel associated variants (rs2162440, p = 2.6 x 10(-6); and rs7235755, p = 5.5 x 10(-6)) on chromosome 18q12.2 in the same region as the VPS34/PIKC3C gene, which has been directly implicated in the pathway controlling telomere length variation in yeast. CONCLUSION: These results provide new insights into the pathways regulating telomere homeostasis in humans.

Overview of the MHC fine mapping data.

AIM: The aim of this study was to perform quality control (QC) and initial family-based association analyses on the major histocompatibility complex (MHC) single nucleotide polymorphism (SNP) and microsatellite marker data for the MHC Fine Mapping Workshop through the Type 1 Diabetes Genetics Consortium (T1DGC). METHODS: A random sample of blind duplicates was sent for analysis of QC. DNA samples collected from participants were shipped to the genotyping laboratory from several T1DGC DNA Repository sites. Quality checks including examination of plate-panel yield, marker yield, Hardy-Weinberg equilibrium, mismatch error rate, Mendelian error rate and allele distribution across plates were performed. RESULTS: Genotypes from 2325 families within nine cohorts were obtained and subjected to QC procedures. The MHC project consisted of three marker panels - two 1536 SNP sets (Illumina Golden Gate platform performed at the Wellcome Trust Sanger Institute, Cambridge, UK) and one 66 microsatellite marker panel (performed at deCODE). In the raw SNP data, the overall concordance rate was 99.1% (+/-0.02). CONCLUSIONS: The T1DGC MHC Fine Mapping project resulted in a 2300 family, 9992 genotyped individuals database comprising of two 1536 SNP panels and a 66 microsatellite panel to densely cover the 4 Mb MHC core region for use in statistical genetic analyses.

Haplotype-based search for SNPs associated with differential type 1 diabetes risk among chromosomes carrying a specific HLA DRB1-DQA1-DQB1 haplotype.

AIM: The aim of this study was to test chromosomes carrying the same DRB1-DQA1-DQB1 haplotype for single nucleotide polymorphisms (SNPs) in the major histocompatibility complex (MHC) that might mark subgroups of the haplotype with different risks for type 1 diabetes (T1D). METHODS: Chromosomes from T1D children, their parents and non-diabetic siblings in families of the Type 1 Diabetes Genetics Consortium (T1DGC) were analysed by two haplotype-based methods: (i) logistic regression analysis restricted to phased chromosomes carrying the same DRB1-DQA1-DQB1 haplotype but differentiated by the two alleles at MHC SNPs, which were individually tested for association with T1D and (ii) homozygous parent transmission disequilibrium test (TDT) for biased transmission of a SNP allele to diabetic children from parents who are heterozygous at the SNP but homozygous for the specific DRB1-DQA1-DQB1 haplotype being evaluated. RESULTS: A number of SNPs gave nominally significant (p < 0.05) evidence of marking two subsets of the 301-501-201 haplotype that might differ with respect to their diabetogenic potency. However, none of the SNPs achieved experiment-wide significance and hence may be false-positive associations. CONCLUSIONS: We discuss limitations and possible deficiencies of our study suggesting further work that might yield more robust SNP associations marking two subgroups of a DRB1-DQA1-DQB1 haplotype with different T1D risks.

Large scale association analysis of novel genetic loci for coronary artery disease.

BACKGROUND: Combined analysis of 2 genome-wide association studies in cases enriched for family history recently identified 7 loci (on 1p13.3, 1q41, 2q36.3, 6q25.1, 9p21, 10q11.21, and 15q22.33) that may affect risk of coronary artery disease (CAD). Apart from the 9p21 locus, the other loci await substantive replication. Furthermore, the effect of these loci on CAD risk in a broader range of individuals remains to be determined. METHODS AND RESULTS: We undertook association analysis of single nucleotide polymorphisms at each locus with CAD risk in 11,550 cases and 11,205 controls from 9 European studies. The 9p21.3 locus showed unequivocal association (rs1333049, combined odds ratio [OR]=1.20, 95% CI [1.16 to 1.25], probability value=2.81 x 10(-21)). We also confirmed association signals at 1p13.3 (rs599839, OR=1.13 [1.08 to 1.19], P=1.44 x 10(-7)), 1q41 (rs3008621, OR=1.10 [1.04 to 1.17], P=1.02 x 10(-3)), and 10q11.21 (rs501120, OR=1.11 [1.05 to 1.18], P=4.34 x 10(-4)). The associations with 6q25.1 (rs6922269, P=0.020) and 2q36.3 (rs2943634, P=0.032) were borderline and not statistically significant after correction for multiple testing. The 15q22.33 locus did not replicate. The 10q11.21 locus showed a possible sex interaction (P=0.015), with a significant effect in women (OR=1.29 [1.15 to 1.45], P=1.86 x 10(-5)) but not men (OR=1.03 [0.96 to 1.11], P=0.387). There were no other strong interactions of any of the loci with other traditional risk factors. The loci at 9p21, 1p13.3, 2q36.3, and 10q11.21 acted independently and cumulatively increased CAD risk by 15% (12% to 18%), per additional risk allele. CONCLUSIONS: The findings provide strong evidence for association between at least 4 genetic loci and CAD risk. Cumulatively, these novel loci have a significant impact on risk of CAD at least in European populations.

Genetic association analyses of non-synonymous single nucleotide polymorphisms in diabetic nephropathy.

AIMS/HYPOTHESIS: Diabetic nephropathy, characterised by persistent proteinuria, hypertension and progressive kidney failure, affects a subset of susceptible individuals with diabetes. It is also a leading cause of end-stage renal disease (ESRD). Non-synonymous (ns) single nucleotide polymorphisms (SNPs) have been reported to contribute to genetic susceptibility in both monogenic disorders and common complex diseases. The objective of this study was to investigate whether nsSNPs are involved in susceptibility to diabetic nephropathy using a case-control design. METHODS: White type 1 diabetic patients with (cases) and without (controls) nephropathy from eight centres in the UK and Ireland were genotyped for a selected subset of nsSNPs using Illumina's GoldenGate BeadArray assay. A chi (2) test for trend, stratified by centre, was used to assess differences in genotype distribution between cases and controls. Genomic control was used to adjust for possible inflation of test statistics, and the False Discovery Rate method was used to account for multiple testing. RESULTS: We assessed 1,111 nsSNPs for association with diabetic nephropathy in 1,711 individuals with type 1 diabetes (894 cases, 817 controls). A number of SNPs demonstrated a significant difference in genotype distribution between groups before but not after correction for multiple testing. Furthermore, neither subgroup analysis (diabetic nephropathy with ESRD or diabetic nephropathy without ESRD) nor stratification by duration of diabetes revealed any significant differences between groups. CONCLUSIONS/INTERPRETATION: The nsSNPs investigated in this study do not appear to contribute significantly to the development of diabetic nephropathy in patients with type 1 diabetes.

Bone mineral density, osteoporosis, and osteoporotic fractures: a genome-wide association study.

BACKGROUND: Osteoporosis is diagnosed by the measurement of bone mineral density, which is a highly heritable and multifactorial trait. We aimed to identify genetic loci that are associated with bone mineral density. METHODS: In this genome-wide association study, we identified the most promising of 314 075 single nucleotide polymorphisms (SNPs) in 2094 women in a UK study. We then tested these SNPs for replication in 6463 people from three other cohorts in western Europe. We also investigated allelic expression in lymphoblast cell lines. We tested the association between the replicated SNPs and osteoporotic fractures with data from two studies. FINDINGS: We identified genome-wide evidence for an association between bone mineral density and two SNPs (p<5x10(-8)). The SNPs were rs4355801, on chromosome 8, near to the TNFRSF11B (osteoprotegerin) gene, and rs3736228, on chromosome 11 in the LRP5 (lipoprotein-receptor-related protein) gene. A non-synonymous SNP in the LRP5 gene was associated with decreased bone mineral density (rs3736228, p=6.3x10(-12) for lumbar spine and p=1.9x10(-4) for femoral neck) and an increased risk of both osteoporotic fractures (odds ratio [OR] 1.3, 95% CI 1.09-1.52, p=0.002) and osteoporosis (OR 1.3, 1.08-1.63, p=0.008). Three SNPs near the TNFRSF11B gene were associated with decreased bone mineral density (top SNP, rs4355801: p=7.6x10(-10) for lumbar spine and p=3.3x10(-8) for femoral neck) and increased risk of osteoporosis (OR 1.2, 95% CI 1.01-1.42, p=0.038). For carriers of the risk allele at rs4355801, expression of TNFRSF11B in lymphoblast cell lines was halved (p=3.0x10(-6)). 1883 (22%) of 8557 people were at least heterozygous for these risk alleles, and these alleles had a cumulative association with bone mineral density (trend p=2.3x10(-17)). The presence of both risk alleles increased the risk of osteoporotic fractures (OR 1.3, 1.08-1.63, p=0.006) and this effect was independent of bone mineral density. INTERPRETATION: Two gene variants of key biological proteins increase the risk of osteoporosis and osteoporotic fracture. The combined effect of these risk alleles on fractures is similar to that of most well-replicated environmental risk factors, and they are present in more than one in five white people, suggesting a potential role in screening.

Genetic analysis of the LGI/Epitempin gene family in sporadic and familial lateral temporal lobe epilepsy.

Mutations in the LGI1/Epitempin gene cause autosomal dominant lateral temporal lobe epilepsy (ADLTE), a partial epilepsy characterized by the presence of auditory seizures. However, not all the pedigrees with a phenotype consistent with ADLTE show mutations in LGI1/Epitempin, or evidence for linkage to the 10q24 locus. Other authors as well as ourselves have found an internal repeat (EPTP, pfam# PF03736) that allowed the identification of three other genes sharing a sequence and structural similarity with LGI1/Epitempin. In this work, we present the sequencing of these genes in a set of ADLTE families without mutations in both LGI1/Epitempin and sporadic cases. No analyzed polymorphisms modified susceptibility in either the familial or sporadic forms of this partial epilepsy.

Common VKORC1 and GGCX polymorphisms associated with warfarin dose.

We report a novel combination of factors that explains almost 60% of variable response to warfarin. Warfarin is a widely used anticoagulant, which acts through interference with vitamin K epoxide reductase that is encoded by VKORC1. In the next step of the vitamin K cycle, gamma-glutamyl carboxylase encoded by GGCX uses reduced vitamin K to activate clotting factors. We genotyped 201 warfarin-treated patients for common polymorphisms in VKORC1 and GGCX. All the five VKORC1 single-nucleotide polymorphisms covary significantly with warfarin dose, and explain 29-30% of variance in dose. Thus, VKORC1 has a larger impact than cytochrome P450 2C9, which explains 12% of variance in dose. In addition, one GGCX SNP showed a small but significant effect on warfarin dose. Incorrect dosage, especially during the initial phase of treatment, carries a high risk of either severe bleeding or failure to prevent thromboembolism. Genotype-based dose predictions may in future enable personalised drug treatment from the start of warfarin therapy.

The DNA sequence and comparative analysis of human chromosome 10.

The finished sequence of human chromosome 10 comprises a total of 131,666,441 base pairs. It represents 99.4% of the euchromatic DNA and includes one megabase of heterochromatic sequence within the pericentromeric region of the short and long arm of the chromosome. Sequence annotation revealed 1,357 genes, of which 816 are protein coding, and 430 are pseudogenes. We observed widespread occurrence of overlapping coding genes (either strand) and identified 67 antisense transcripts. Our analysis suggests that both inter- and intrachromosomal segmental duplications have impacted on the gene count on chromosome 10. Multispecies comparative analysis indicated that we can readily annotate the protein-coding genes with current resources. We estimate that over 95% of all coding exons were identified in this study. Assessment of single base changes between the human chromosome 10 and chimpanzee sequence revealed nonsense mutations in only 21 coding genes with respect to the human sequence.