Dataset Information

Pitfalls in homozygosity mapping.

ABSTRACT: There is much interest in use of identity-by-descent (IBD) methods to map genes, both in Mendelian and in complex disorders. Homozygosity mapping provides a rapid means of mapping autosomal recessive genes in consanguineous families by identifying chromosomal regions that show homozygous IBD segments in pooled samples. In this report, we point out some potential pitfalls that arose during the course of homozygosity mapping of the enhanced S-cone syndrome gene, resulting from (1) unexpected allelic heterogeneity, so that the region containing the disease locus was missed as a result of pooling; (2) identification of a homozygous IBD region unrelated to the disease locus; and (3) the potential for inflation of LOD scores as a result of underestimation of the extent of inbreeding, which Broman and Weber suggest may be quite common.

SUBMITTER: Miano MG

PROVIDER: S-EPMC1288578 | biostudies-literature | 2000 Nov

REPOSITORIES: biostudies-literature

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Pitfalls in homozygosity mapping.

Miano M G MG Jacobson S G SG Carothers A A Hanson I I Teague P P Lovell J J Cideciyan A V AV Haider N N Stone E M EM Sheffield V C VC Wright A F AF

American journal of human genetics 20000927 5

There is much interest in use of identity-by-descent (IBD) methods to map genes, both in Mendelian and in complex disorders. Homozygosity mapping provides a rapid means of mapping autosomal recessive genes in consanguineous families by identifying chromosomal regions that show homozygous IBD segments in pooled samples. In this report, we point out some potential pitfalls that arose during the course of homozygosity mapping of the enhanced S-cone syndrome gene, resulting from (1) unexpected allel ...[more]

PMID: 11007652

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Project description:Premature ovarian insufficiency (POI) is defined as a primary ovarian defect characterized by absent menarche (primary amenorrhea) or premature depletion of ovarian follicles before the age of 40 (secondary amenorrhea) with hypergonadotropism and hypoestrogenism. Premature ovarian insufficiency has few known genetic causes but in familial cases a genetic link is often suspected. A large consanguineous family with three female affected with POI was investigated. All samples including 3 affected and 5 unaffecd underwent whole genome SNP genotyping using Affymetric Axiom_GW_Hu_SNP array. Linkage analysis was carried out using HomozygosityMapper and Allegro softwares.Linkage analysis mapped the disease phenotype to long arm of chromosome 20. Sequence data analysis of potential candidate genes failed to detect any pathogenic variant. Affymetrix SNP arrays were performed according to the manufacturer's directions on DNA extracted from peripheral blood samples. DNA of eight individuals including three affected subjects was used for homozygosity mapping. Genotyping was performed using the Affymetrix Axiom_GW_Hu_SNP array. Briefly, 250 ng genomic DNA was digested with Digestion Master Mix containing 2 µl NE buffer 2 (10X), 0.5 µl BSA (100X; 10 mg/ml) and 1 µl Nsp1. Digested DNA sample was ligated to Nsp1 adaptor using T4 DNA ligase and amplified by 2 µl of TITANIUM Taq DNA polymerase (50X) and 100 µM PCR primer. PCR products were purified on a Clean-Up plate (Clontech Lab, Madison, USA) and eluted by RB buffer. Purified PCR products were fragmented using Fragmentation Reagent (0.05U/µl DNase 1) for 35 minutes at 37°C followed by labeling of fragmented samples with Labeling Master Mix (30 mM GeneChip DNA Labeling Reagent, 30 U/µl Terminal Deoxynucleotidyl Transferase) for 4 hours at 37°C. Labeled samples were hybridized to Axiom_GW_Hu_SNP array by mixing the sample with Hybridization Master Mix, denatured on thermoblock and loaded on to Array. Array was then placed in a hybridization oven (GeneChip Hybridization Oven 640, USA) for 16-18 hours. After hybridization, array was washed and stained on an automated Fluidic Station 450 followed by scanning on GeneChip Scanner 3000 7G using GeneChip Operating Software (GCOS).

Project description:BackgroundSnakebites are a public health problem in Nicaragua: it is a tropical developing country, venomous snakes are present and there are reports of snakebites treated both in the formal and informal health care system. We aimed to produce an incidence map using data reported by the health care system that would be used to allocate resources. However, this map may suffer from case detection bias and decisions based on this map will neglect snakebite victims who do not receive healthcare. To avoid this error, we try to identify where underreporting is likely based on available information.Method and findingsThe Nicaraguan municipalities are categorized by precipitation, altitude and geographical location into regions of assumed homogenous snake prevalence. Socio-economic and healthcare variables hypothesized to be related to underreporting of snakebites are aggregated into an index. The environmental region variable, the underreporting index and three demographic variables (rurality, sex and age distribution) are entered in a Poisson regression model of municipality-level snakebite incidence. In this model, the underreporting index is non-linearly associated with snakebite incidence, a finding we attribute to underreporting in the most deprived municipalities. The municipalities with the worst scoring on the underreporting index and those with combined low reported incidence and large rural population are identified as likely underreporting. 3,286 snakebite cases were reported in 2005-2009, corresponding to a 5-year incidence of 56 bites per 100,000 inhabitants (municipality range: 0-600 cases per 100,000 inhabitants).ConclusionsUsing publicly available data, we identified areas likely to be underreporting snakebites and highlighted these areas instead of leaving them "white" on the incidence map. The effects of the case detection bias on the distribution of resources against snakebites could decrease. Although not yet verified empirically, our study provides an example of how snake bite epidemiology may be investigated in similar settings worldwide at a low cost.

Dataset Information

Pitfalls in homozygosity mapping.

Publications

Pitfalls in homozygosity mapping.

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