Module 6: Lecture 1c - Kinship Analysis

Introduction to Kinship Analysis

  • IMPORTANT: This lecture continues on the topic of kinship analysis and aims to solidify the concepts discussed previously.

  • Reference: Separate slide deck available for the lecture, particularly slides three and four which contain essential coefficients and cheat sheets for kinship analysis.

Objective of Kinship Analysis

  • Kinship analysis primarily involves calculating likelihood ratios, similar to parentage analysis.

  • The calculation process includes:

    • Evaluating a numerator and a denominator.

    • The numerator reflects the hypothesis that the DNA profiles of two subjects show a specific biological relationship.

    • The observed genotype can arise in three possible ways:

    • K sub 2: Both alleles at the locus are identical.

    • Two times K sub 1: One allele at the locus is identical by descent.

    • K sub 0: No alleles at the locus are identical.

Identity by Descent Coefficients

  • K coefficients represent the proportion of time individuals share alleles identical by descent. Default definitions include:

    • K0: Proportion of time two individuals share no alleles identical by descent.

    • K1: Proportion of time individuals share one allele identical by descent.

    • K2: Proportion of time individuals share both alleles identical by descent.

  • Important relatedness coefficient calculation (though not directly used in calculations): Relatednessextcoefficient=K12+K2Relatedness ext{ coefficient} = \frac{K1}{2} + K2

Summary of Coefficients

  • A table or chart is referenced noting K0, K1, and K2 values for common relationships:

    • Parent-Child Relation:

    • K2 (both alleles) = 0.0%

    • K1 (one allele) = 100%

    • K0 (none) = 0%

    • Full Siblings:

    • K2 = 25%

    • K1 = 50%

    • K0 = 25%

    • First Cousins:

    • K2 = 0%

    • K1 = 50%

    • K0 = 75%

Understanding Genotype Sharing

  • Conceptual understanding of allele sharing is crucial:

    • Parent-child relationship always shares one allele.

    • In relation to full siblings, variations occur in allele sharing:

    • 25% chance to share both alleles.

    • 50% chance to share one allele.

    • 25% chance to share no alleles.

  • First cousins share one allele 50% of the time and none 75% of the time.

Cheat Sheet for Kinship Analysis

  • Analyses are based on the genotype of a reference individual compared with other individuals. Example relationship structures are provided on slides including:

    • Genotype reference and compared sample genotypes.

    • Calculation structure for numerator = specific biological relationships sharing alleles.

    • Denominator = genotype frequency for random individuals.

Formula Breakdown for Kinship Coefficient Calculation

  • Mathematical representation of coefficients:

    • Denominator: A2A^2 where A is allele frequency.

    • Calculation for numerator includes:

    • Contributions of K2 and K1 must account for the specific biological relationship.

    • Final format of kinship index calculation involves combinations of K and allele frequencies, leading to final coefficient values.

Worked Examples of Kinship Calculation

  • Working Example: Full Siblings

    • Assume the frequency of alleles is 0.05. The coefficients for siblings at three loci average kinship indices across them:

    • For locus 1, calculate using K values, leading to a kinship index of 60.25.

    • Adjusts for locus 2 yielding a kinship index of 5.25.

    • Locus 3 not showing any allele sharing yields K0 value leading to final kinship index evidence.

  • Working Example: Half Siblings

    • Repeat similar calculations for loci 1-3 using half-sibling values for K coefficients.

    • Results in lesser kinship index value, emphasizing decreased likelihood of being half siblings compared to being full siblings.

Statistical Considerations in Kinship Analysis

  • Important to note statistical power when analyzing relatedness:

    • Relies on the effect of multiple markers analyzed.

    • Statistical power issues lead to possibilities of false positives and negatives in assessments of relatedness.

  • The distribution of values from comparisons illustrates Mendelian segregation effects.

    • Related individuals may fall within a range of relationship probabilities as opposed to non-related individuals.

Summary of Kinship Coefficients

  • Parent offspring have the highest statistical confidence due to the direct parentage (both alleles = 1).

  • Full siblings (K coefficient = 0.25) and half siblings (K coefficient = 0.125) demonstrate lower certainty.

  • Variability in allele sharing data increases the complexity of kinship determination as relationships distance shifts away from parent-child dynamics.

Applications of Kinship Analysis in Real-World Scenarios

  • Research in genetics and conservation (e.g., salmon hatcheries) utilizes kinship analysis for breeding programs to avoid inbreeding.

  • Recent examples focus on analyzing the genetic relationships and patterns among populations using markers.

Closing Remarks

  • Additional supplemental information offered in main slide deck for reference to improve understanding.

  • Any questions can be directed through email to the instructor for further clarification on kinship analysis topics.