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Pedigree Analysis

Pedigree Analysis

Incomplete Dominance

Incomplete Dominance

Gregor Mendel's work (1822 - 1884) was primarily focused on pea plants. Through his initial experiments, he determined that every gene in a diploid cell has two variants called alleles inherited from each parent. He suggested that amongst these two alleles, one allele is dominant in character and the other recessive. The combination of alleles determines the phenotype of a gene in an organism.

Probability Laws

Probability Laws

Hardy-Weinberg Principle

Hardy-Weinberg Principle

Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.

Genome-wide Association Studies-GWAS

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

Epistasis Analysis

Epistasis Analysis

Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...

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Related Experiment Video

Updated: Jun 5, 2025

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information

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Published on: August 15, 2019

BICEP: Bayesian inference for rare genomic variant causality evaluation in pedigrees.

Cathal Ormond¹, Niamh M Ryan¹, Mathieu Cap¹

¹Neuropsychiatric Genetics Research Group, Department of Psychiatry, Trinity Centre for Health Sciences, Trinity College Dublin, St James's Hospital, Dublin 8, Ireland.

Briefings in Bioinformatics

|December 10, 2024

Summary

We developed BICEP, a Bayesian tool to identify rare disease-causing genetic variants in families. BICEP accurately evaluates variant causality using cosegregation and prior evidence, outperforming other methods for Mendelian and complex traits.

Keywords:

Bayes factor Bayesian inference next-generation sequencing pedigree posterior odds of causality prior odds of causality

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Published on: January 16, 2019

Area of Science:

Genetics
Bioinformatics
Computational Biology

Background:

Next-generation sequencing (NGS) is crucial for gene discovery using pedigree data.
Identifying causative variants within a robust statistical framework remains a significant challenge.

Purpose of the Study:

Introduce BICEP (Bayesian Inference tool for Causal variant Evaluation in Pedigrees).
Provide a robust statistical framework for rare variant causality evaluation in pedigree-based cohorts.

Main Methods:

BICEP employs Bayesian inference to calculate posterior odds of variant causality.
Integrates variant cosegregation data with a priori evidence (deleteriousness, functional consequence).
Evaluates variants within pedigree structures for Mendelian and complex genetic architectures.

Main Results:

BICEP accurately identifies causal rare variants, outperforming existing methods.
Effectively down-weights common variants unlikely to be causal, even with good cosegregation.
Provides quantitative metrics for comparing variant causality within and across pedigrees.

Conclusions:

BICEP offers a superior approach for rare variant causality assessment in family studies.
Enables more accurate gene discovery for both simple and complex genetic diseases.
Facilitates quantitative, cross-pedigree variant causality comparisons.