5.3 Genome wide association studies and human population structure

Exam 2

Manhattan Plots

• Significance values for each SNP across the genome

• Each spot represents the P-value of 1 SNP

• will see a Gaussian (bell shaped) distribution around one SNP with the highest likelihood of being linked to the trait

• many SNPs form a ‘peak’ because recombination breaks up haplotypes in the region (linked variants)

GWAS for mapping traits Pros:

• samples are generally easy to collect

• can use the same samples (controls) for multiple phenotypes

• resolution can be much higher

GWAS for mapping traits Cons:

• lots of samples are required

• expensive to collect all of the genotypes

• false positives are common different causative alleles in “cases” it will lower statistical power to detect associations

Some challenges in QTL analysis

• Most complex traits are affected by many different —- (alleles)

• Effects of the —- (alleles) on the trait can be strongly influenced by environment and other —-

  • A lot of —- (alleles) are in genes that have no known function

• The majority of —- (alleles) affecting common traits account for a tiny fraction of total phenotypic variance

Subgroup

are part of the larger haplogroup

Ancient human migrations

• Each sub-population had the opportunity to gain unique mutations and form new haplogroups and subgroups

• With ‘recent’ technology, humans began trading and mating with other groups, exchanging haplotypes

Independent Haplogroups

Because autosomal, mitochondrial, and Y-chromosome DNA are inherited separately

Ancestry Tracking

Y and mitochondrial groups are most often used to track ancestry because they do not experience recombination, making it easier to track how groups evolve

Autosomal Recombination

Autosomal haplogroups do not have official names because recombination makes them difficult to study, requiring more complex analysis

The haplogroups can only identify extant (living) lineages

at points in the past there may have been more genetic diversity for these sequences

Assigning Y-Haplogroups to Females

Since women do not have a Y haplogroup that can be measured directly, a father's or brother's DNA can be sequenced to assign a female to a Y haplogroup.

Mitochondrial ‘Eve’ vs Y-chromosome ‘Adam’
“Y-chromosome Adam” and “Mitochondrial Eve” are terms we use to describe the oldest haplogroups on the Y-chromosome and mitochondrial genome. These are not the first humans, merely the oldest groups we are able to identify. Mitochondrial Eve is ~155,000 years old, while Y-chromosome Adam is ~200,000 years old. How would you expect the haplotype size between Y-chromosome Adam and a more recent ~12,000 year old South American Y haplotype called Q3 to compare?
A) Y-chromosome Adam is longer
B) Q3 is longer
C) There is no difference
D) There is not information to tell if there will be a difference

A) Y-chromosome Adam is longer

You are studying the Y chromosome haplogroups from a population of South American Amazon natives that have yet to be sequenced. The SNPs on their Y chromosomes mostly match those that define the Q3 haplogroup, however there are a few truly unique mutations found on no other Y hromosomes. You name this new haplogroup, Q3b. Q3 and its subgroup are part of the larger haplogroup Q. Do you expect Q3b to have the defining SNPs of haplogroup Q?
A) Yes
B) No
C) No way to tell with this information

A) Yes

Interpreting GWAS relatedness

• The first eigenvector or Principal Component (PC1) is the variance caused by the combination of SNPs that contribute most to the differences between individuals, typically < 1%

  • the x-axis

• PC2 is the combination of SNPs that control the second most variance, here those that separate Bantu from Mandinka, again typically <1%

  • y-axis

GWAS shows that haplotypes segregate mostly based on geography

• Sampling more individuals allows for more understanding of population structures

• The largest PCs tend to be geography, indicating more likelihood to mate with nearby individuals

• underlying this analysis are measurements of allele frequencies and the like

The HapMap Project

focused on understanding and cataloging all of the haplogroups and subgroups in Homo sapiens

“Race is not genetic”

classifications of race do not align with our measurements of genetics aka The lack of exclusivity of variants