L28 Human Population Genetics 1 Flashcards
How many ancestors do you have?
see onenote slides
Can one person be multiple ancestors at once?
How many ancestors are you genetically related to?
see onenote
How much did each of them contribute to your genome?
From grandparents we don’t get a clean cut 25% of their genome from them, can be around 23% or 27%
After parents, there is uncertainty of how much of our ancestor’s genome we inherit (inherit 50% from mum and 50% from dad)
Humans are not infinite
see onenote
ancestors at generation
n=2^n
Who does your genome come from?
see onenote slides
% genome inherited from each
parent: 50%
gparent: 25%
ggparent: 12.5%
gggparent: 6.75%
Human population genetics
All humans are related due to our evolutionary history
but the specifics are trickier:
- when?
- where?
- how much?
- what does it mean for you?
- what does it mean for the species?
population genetics: the extension of Mendelian genetics to evolving populations
How do we talk about pop gen?
see onenote
need a formal means to describe processes such as:
- heterozyosity
- relatedness
- diversity
- mutations
- etc.
to synthesise these observations into coherent knowledge
Multiple ways of thinking about a population
see onenote
- full genealogy
- ancestry of extant lineages
- ancestry of sampled lineages
- coalescent of sampled lineages
Our most basic tool - a single segregating locus
see onenote
single diploid locus 2 alleles: A and a p = A freq. q = a freq. p + q = 1
Hardy-Weinberg equilibrium
see onenote
relationship between genotype and allele frequencies knows as h-w equilibrium
for it to hold, multiple assumptions must be true:
- random mating
- no selection
- no migration
- no mutation
- no genetic drift (infinite population size)
Beyond loci, to populations: the wright-fisher model
see onenote slides
still only one locus but now we explicitly model the passing of time
- population size is constant (not infinite)
- generations are discrete and non-overlapping
- all individuals are equally fit
- no mutation
- haploid individuals
- when extended to diploid mutations
- non-assortative mating
- no recombination
Wright-Fisher model, models genetic drift
- Binomial sampling with some extra parameters
Can be extended to incorporate things like selection
WF provides a robust null hypothesis for everything else we discuss
Fundamental properties of the WF model
see onenote
each individual at generation t has 2N opportunities to become an ancestor to generation t+1
the probability of being ancestor to a specific individual is 1/2N
binomial distribution
n = 2N
p = 1/2N
WF from the gene’s perspective
see onenote
there can be multiple copies of a gene in a generation
probability of being transmitted to generation t+1 now depends on number of copies of gene at t
still a random binomial process
n = 2N
p = x/2N
Extending the model: WF with mutation
see onenote
introduce mutation into the population with probability “mu” per site per generation
in humans, “mu”~1 x 10^-8 per site per generation
A window into the past: the coalescent
see onenote slides
can use assumptions of the WF model to go backwards and reconstruct the past from the present
Can answer questions such as:
- When did two individuals last share a common ancestor? When did they coalesce?
- Coalescent lets us date relatedness in a way that we can track
Can extrapolate current data back into the past to figure out relatedness between individuals
Coalescent lets us date relatedness and divergence between sequences
see onenote
- given a DNA phylogeny, it allows us to find the time to most recent common ancestor (TMRCA) of sequences in the phylogeny
- coalescent modelling can be expanded to incorporate selection, expansion etc.
- gives us the framework against which to test predictions and observations
But real populations are far more complicated
WF model is unrealistic but it gives us a mathematical language to describe expectations under neutrality, as well as mutation, selection, population growth etc.
does the parameter of the model resemble observed data?
WF and coalescent modelling empowers us to make robust judgements about actual populations and their genetic data
Describing population structure
Population
- Randomly reproducing group of individuals
random mating = random relative to genetics
- meaning there is no underling population structure or subpopulations
but humans violate these assumptions:
- geographic structure/isolation by distance
- assortative mating
- inbreedinge etc.
F statistics for relationships between populations
see onenote
H statistics = measurements of heterozygosity and diversity
- Hi = heterozygosity within an individual
- Hs = heterozygosity within a subpopulation
- Ht = heterozygosity within the total population
F statistics = measure of population structure and differentiation
- Fxy = correlation between gametes drawn from x relative to y
- Fis = describes correlation (or diversity) between gametes from a single individual relative to what is seen in the subpopulation
F and H statistics are hierarchical
Fst is the most widely used F statistic in population genetics
- it describe the proportion of the variance due to between-population rather than within-population differences
The international HapMap project
see onenote slides
aimed to generate a haplotype map of the human genome to be used in mapping genes associated with any disease
use of trios allowed for phasing of data and identification of haplotypes
Haplotype
a block of SNPs physically on the same chromosome, inherited together due to LD
haplotypes are used to identify recombination break point, linkage blocks and tag SNPs
tag SNP = representation SNP for an entire haplotype
HGDP-CEPH and 1000 genomes
see onenote
Human genome diversity project
- for ethnic and evolutionary studies
1000 genomes
- for broadly medical studies
HGDP-CEPH - treasure trove or ethics quagmire?
see onenote
aim of the project was studies of human history and evolution - not medical research but all project data is publicly available so…
sought to collect DNA from isolated populations “at risk of becoming extinct” without working with the communities to prevent it
biocolonialism has resulted in many indigenous groups worldwide refusing to participate in genetic research when the distribution of benefits is inequitable
