Understanding Genetic Relationships

This guide explains the genetic concepts behind lineage and how to interpret your results.

Note

A note on genetic discoveries: DNA analysis can sometimes reveal unexpected relationships or information that wasn’t previously known. Approach discoveries with care and respect for everyone involved.

How DNA is Inherited

Humans have 23 pairs of chromosomes. For each pair, you inherit one chromosome from your mother and one from your father. This means:

• You share exactly 50% of your DNA with each parent — one complete chromosome from each pair

• Siblings share variable amounts of DNA — on average ~50%, but it varies due to the randomness of inheritance

Identity By Descent (IBD)

When lineage analyzes shared DNA, it uses Identity By Descent (IBD) to classify sharing patterns at each genomic segment:

• IBD0: No chromosomes shared — the individuals inherited different chromosomes from both parents

• IBD1: One chromosome shared (half-identical region) — the individuals share DNA on one chromosome of a pair (shown in light blue in plots)

• IBD2: Both chromosomes shared (fully identical region) — the individuals share DNA on both chromosomes of a pair (shown in dark purple in plots)

Parent-Child Relationships

A child inherits exactly one chromosome from each parent at every position. This means:

• All positions between parent and child are IBD1 (one chromosome shared)

• There is no IBD2 sharing (a child cannot inherit both chromosomes from one parent)

• The total shared DNA is approximately 3400–3700 centiMorgans

Sibling Relationships

Siblings inherit chromosomes from the same two parents, but which specific segments they inherit is random due to a process called meiotic recombination. During the formation of egg and sperm cells, chromosomes exchange segments, creating unique combinations.

For full siblings, at any given position in the genome:

• ~25% chance: IBD0 (no sharing) — each sibling inherited different chromosomes from both parents

• ~50% chance: IBD1 (one chromosome shared) — siblings inherited the same chromosome from one parent

• ~25% chance: IBD2 (both chromosomes shared) — siblings inherited the same chromosomes from both parents

At any genomic position, these states are mutually exclusive: IBD1 means exactly one chromosome copy is shared, while IBD2 means both copies are shared. Total shared DNA equals IBD1 + IBD2.

What are CentiMorgans?

A centiMorgan (cM) is a unit that measures genetic distance based on recombination frequency, not physical distance. One centiMorgan corresponds to a 1% chance that a segment will be separated by recombination in a single generation.

Why use centiMorgans instead of base pairs?

• Recombination rates vary across the genome — some regions recombine frequently, others rarely

• CentiMorgans account for these differences, giving a more accurate measure of genetic relatedness

• The total human genome is approximately 3400–3700 cM (depending on the genetic map used)

Typical shared DNA amounts:

Relationship	Shared DNA (cM)	IBD1 (cM)	IBD2 (cM)
Parent/Child	~3400–3700	~3400–3700	0
Full Siblings	~2300–2900	~1700–2000	~600–900
Half Siblings	~1300–2300	~1300–2300	0
First Cousins	~550–1200	~550–1200	0

Note

Total shared DNA = IBD1 + IBD2. Only relationships through both parents (full siblings, double first cousins, etc.) can have IBD2 sharing, since IBD2 requires inheriting the same DNA from both the mother and father.

Genetic Maps

A genetic map provides recombination rates across the genome, allowing lineage to convert physical distances (base pairs) into genetic distances (centiMorgans).

lineage supports two sources of genetic maps:

HapMap Phase II

The International HapMap Project mapped genetic variation across human populations. This is the default genetic map in lineage.

• Covers all autosomes (chromosomes 1–22) and the X chromosome

• Good general-purpose choice for most analyses

1000 Genomes Project

The 1000 Genomes Project provides population-specific genetic maps with more detailed recombination data.

Available populations include:

• CEU: Utah residents with Northern/Western European ancestry

• YRI: Yoruba in Ibadan, Nigeria

• CHB: Han Chinese in Beijing, China

• And many others

Note

The 1000 Genomes maps do not include the X chromosome, so shared DNA on chromosome X will not be computed when using these maps.

Using a population-specific map can provide more accurate centiMorgan calculations if you know the ancestral background of the individuals being compared:

>>> results = l.find_shared_dna([ind1, ind2], genetic_map="CEU")

Understanding Thresholds

lineage uses two thresholds to filter out noise and reduce false positives when detecting shared DNA segments:

• cM_threshold (default: 0.75 cM): Minimum genetic length of a shared segment

• snp_threshold (default: 1100 SNPs): Minimum number of SNPs in a shared segment

These conservative defaults help ensure that detected segments represent true shared DNA rather than matching by chance. You can adjust these thresholds:

>>> # More permissive — reveals smaller segments but may include false positives
>>> results = l.find_shared_dna([ind1, ind2], cM_threshold=0.5, snp_threshold=500)

>>> # More conservative — only shows larger, high-confidence segments
>>> results = l.find_shared_dna([ind1, ind2], cM_threshold=1.0, snp_threshold=1500)

Shared Genes

Beyond identifying shared DNA segments, lineage can identify shared genes — genes located within those shared segments where individuals have the same genetic variations.

The Science

The Central Dogma of Molecular Biology describes how genetic information flows:

1. DNA is transcribed into mRNA

2. mRNA is translated into proteins

When two individuals share a DNA segment containing a gene, and they have identical genetic variations in that region, they are likely producing the same protein from that gene.

Using Shared Genes

Enable shared gene analysis with the shared_genes parameter:

>>> results = l.find_shared_dna([ind1, ind2], shared_genes=True)

This outputs additional CSV files listing genes in IBD1 and IBD2 regions, including:

• Gene names and symbols

• Chromosomal locations

• RefSeq and protein IDs

• Gene descriptions

Note

Shared DNA segments should theoretically produce the same proteins, but biological complexities like copy number variation (CNV) and gene expression differences mean this is an approximation.

Discordant SNPs

Discordant SNPs are genetic variations that don’t follow expected inheritance patterns (Mendelian inheritance). Finding discordant SNPs between a parent and child can indicate:

• Genotyping errors in the raw data

• Mutations that occurred during DNA replication

• Data quality issues from the testing company

A small number of discordant SNPs is normal. A large number may indicate data quality problems or that the individuals may not have the expected biological relationship.

>>> discordant = l.find_discordant_snps(parent, child)
>>> len(discordant)
42  # A small number is typical

Interpreting Results

The find_shared_dna method returns a dictionary with these keys:

Key	Description
one_chrom_shared_dna	IBD1 segments (DNA shared on one chromosome)
two_chrom_shared_dna	IBD2 segments (DNA shared on both chromosomes)
one_chrom_shared_genes	Genes in IBD1 segments (if requested)
two_chrom_shared_genes	Genes in IBD2 segments (if requested)
one_chrom_discrepant_snps	SNPs with unexpected patterns in IBD1 regions
two_chrom_discrepant_snps	SNPs with unexpected patterns in IBD2 regions

Tips for Analysis

1. Start with defaults — The default thresholds work well for most analyses

2. Compare total centiMorgans — Sum the cMs column to get total shared DNA and compare with expected values for the suspected relationship

3. Look at the plot first — The visualization quickly shows the pattern of sharing across all chromosomes

4. Consider the relationship type:

   • Parent-child: Expect IBD1 sharing across the entire genome

   • Full siblings: Expect a mix of IBD1 and IBD2 sharing

   • Half siblings: Expect IBD1 sharing only (no IBD2), with gaps showing IBD0 regions

   • More distant relatives: Expect smaller, scattered IBD1 segments

5. Check for data quality — Large gaps in the plot may indicate regions where SNPs weren’t tested rather than regions of no sharing