MOLECULAR GENETICS 2005
Tuesday November 29:
Human Genetics, part I
Liisa Kauppi (Keeney lab)
RRL-1129
Phone 639 5180
Email kauppiL@mskcc.org
Web resources:
For BLAST searches, gene queries etc.:
http://www.ensembl.org/Homo_sapiens/index.html
For comprehensive listing and information on genetic diseases:
Online Mendelian Inheritance in Man (OMIM)
http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=OMIM
SNP database:
http://www.ncbi.nlm.nih.gov/SNP/index.html
HapMap project:
http://www.hapmap.org/index.html.en
Textbook: Human molecular genetics (Strachan and Read), chapters 11 and 12 in version
2 of the book
See any recent issue of American Journal of Human Genetics for applications of the
methods described in this lecture (at www.ajhg.org).
Human genetics can be studied in pedigrees or populations. There are limitations,
however: unlike with model organisms such as mice or flies, in human genetics you
cannot generate mutants, you cannot perform controlled matings, place your subjects in a
controlled environment etc. We have to rely on “natural” mutants, i.e. patients with a
phenotype. For a trait to be amenable to genetic analysis, it has to show some degree of
heritability. This can be assessed in pedigrees (first degree relatives, twins).
There are two basic types of genetic diseases: simple Mendelian with complete
correlation between genotype and phenotype - if you have the mutant gene, you'll get the
disease, and complex or multifactorial (risk of getting the disease is modified by
individual's genotype; other factors like other genes and environment, also influence
disease risk).
Mapping disease genes in humans is done by using polymorphic DNA markers (single
nucleotide polymorphisms [SNPs] and microsatellites). Terminology for SNPs is defined
by allele frequency. A single base change, occurring in a population at a frequency of
>1% is termed a SNP. When a single base change occurs at <1% it is considered to be a
mutation.
Expected genotype frequencies at any bi-allelic locus can be calculated using the HardyWeinberg equation. It has the following assumptions: large randomly mating population,
no mutations, no migration between populations, no selection (all genotypes reproduce
with equal success).
Basic relations are: two alleles at a locus (B and b)
frequency of the B allele = p, frequency of the b allele = q
and p + q = 1
Genotype frequencies are given by the equation: p2 (BB) + 2pq (Bb) + q2 (bb) = 1
Any individual is the result of the union of two gametes which have the probability of
containing a specific allele (equal to the allele frequency). This can be diagrammed as a
Punnett square:
p (B)
q (b)
p (B)
p2 (BB)
pq (Bb)
q (b)
pq (Bb)
q2 (bb)
HWE explains how recessive traits are maintained in a population, and allows
calculations of carrier frequencies for recessive traits (with some caution). Departure
from HWE suggests that some assumptions are not met, for example there may be recent
migration or selection influencing the genotype frequencies.
Family studies
In a simple Mendelian disease, polymorphisms are studied in family members for genetic
linkage. If the marker is close to the disease gene on the chromosome there is a low
chance of recombination at meiosis and linkage is observed; if the marker and disease
gene are far apart (or on different chromosomes), linkage is not observed. To calculate
linkage, the number of recombinant (R) and non-recombinant (NR) offspring are counted
for each sibship in the family. If the recombination fraction RF (), calculated as
R/(R+NR), is significantly less than 0.5, there is evidence of linkage.
Numbers of offspring in human families are small. In order to get statistically significant
evidence for linkage, it is necessary to combine evidence from many families, and to
make probabilistic guesses for families where not all members are available for study (all
done on a computer). The lod (logarithm of odds) score Z is a statistic that describes the
strength of evidence for linkage, at any chosen value of the RF, given the family data
available. It is calculated as follows:
Z= log [Likelihood of loci being linked/Likelihood of loci not being linked]
A lod score of 3 or more is considered good evidence for linkage and a lod score of -2 or
less is evidence against linkage. Values between -2 and 3 are inconclusive and more data
must be obtained.
A genome scan consists of genotyping a collection of families with the genetic disease
using hundreds markers across the genome. By using many markers there is a good
chance that any unknown gene will be close enough to one or two of them to show
genetic linkage. The aim is to find linkage with two markers, one of which is on each side
of the disease gene; then the disease gene must be between the two markers (usually a
few Mb apart).
Recognizing recombinants
does the disease segregate with this marker?
1
I
25
16
II
6
21
34
III
31
32
41
NR
NR
NR
41
NR
42
32
NR
R
Recombination fraction  is 1/6=0.167
Which marker is the disease locus closest to?
Multi-point lod scores
chr 3p12-14
Waardenburg syndrome type 2
Hughes et al. (1994) Nature Genet 7, 509-512
“Shared segment” analyses
When there is no simple Mendelian inheritance pattern, we can analyze affected people
only. When you only look at affecteds, there is no need to specify penetrance of the
disease. In affected sib pair analysis, the aim is to identify genomic segments that are
shared between the affected sibs more often than expected by chance.
Association studies in populations
In populations, cases (patients) and controls can be collected. Then you look for alleles
that are more prevalent in the patient cohort than in the general population (controls).
However, typing many markers in such large numbers of samples would be prohibitively
expensive.
Luckily, we can make use of a phenomenon called linkage disequilibrium (LD).
This refers to the correlation between two alleles on a chromosomal segment. LD is a
result of this chromosomal segment (haplotype) not having been broken up by meiotic
recombination in the population. A haplotype is the set of alleles at more than one locus
that is inherited by an individual from one of its parents. For two loci with two alleles
each (A and a, B and b) there are four possible haplotypes: AB, Ab, aB, ab. A diploid
individual’s genotype could be for example AB on one homologous chromosome and Ab
on the other homolog. In other words, the individual has two haplotypes, one inherited
from each parent, just as a one-locus genotype contains two alleles from the two parents.
The opposite of LD is linkage equilibrium (or free association), which can be thought of
as the two-locus version of the Hardy-Weinberg ratio, but it is a property of haplotypes,
not genotypes. At linkage equilibrium, all four haplotypes are found in the population, at
frequencies that are expected based on the genotype frequencies of the individual alleles.
Linkage disequilibrium (LD) measures
association between two alleles
Mutation creates new variants
A
G
A
A
A
G
T
A
Initially, the new allele is in LD with
nearby alleles
LD value = 1
Recombination reshuffles existing variation
A
G
A
G
X
T
A
A
T
LD diminishes
If enough crossovers take place, the
loci are in Тfree associationУ
Commonly used LD measures: D
Хand r2
The utility of LD for gene mapping is the fact that if there are extended
chromosomal segments where alleles are inherited as “a package” (haplotype blocks),
then there is no need to type every SNP in such a segment. It should be sufficient to
select a few “tag SNPs” for a haplotype block and only genotype those. Recently, the
HapMap project has characterized the extent of haplotype blocks in four human
populations (Nigerians, Whites in the US, Japan, China), and identified SNPs for tagging
these blocks.
LD-based methods work best when there is a single susceptibility allele at any
given disease locus, and generally perform very poorly if there is substantial allelic
heterogeneity. The Common-Disease Common-Variant (CDCV) hypothesis states that
disease-predisposing variants exist at relatively high frequency (i.e. >1%) in the
population. According to the CDCV hypothesis, common diseases are caused by ancient
alleles occurring on specific haplotypes; these haplotypes should be detectable in a casecontrol study using tagging SNPs.
The alternative (and more pessimistic) hypothesis states that disease-predisposing
alleles are sporadic new mutations, perhaps around the same genes, on different
haplotypes. Different families with history of the same disease would then owe their
condition to different mutations events.
There are examples to show that for at least some common diseases the CDCV
hypothesis is true (Alzheimer’s etc.). It is likely, however, that some diseases have
underlying allelic heterogeneity. For any gene, there are a number of ways in which SNPs
can alter its expression (nonsynonymous, modify splicing, alter promoter function etc.).
If a pathway leading to the disease is complex (consider schizophrenia for example), the
number of genes involved is higher, and the likelihood of several SNPs affecting gene
function increases.
Finally, recently it has been discovered that all humans carry several low copy number
polymorphisms in their genomes (see Mike Wigler’s President’s seminar talk on Dec 14).
It is likely that these too will influence susceptibility to common disease. There is also
evidence that microsatellite and insertion/deletion (indel) polymorphisms can influence
phenotypes:
- Spinocerebellar ataxia Type10 (SCA10) (OMIM:+603516) is caused by the largest
tandem repeat seen in human genome. Normal population has 10-22 repeats of the
pentanucleotide ATTCT in intron 9 of SCA10 gene; SCA10 patients have 800-4500
repeat units that cause the disease allele to be up to 22.5 kb larger than the normal one.
- Association between coronary heart disease and a 287-bp indel polymorphism located
in intron 16 of the angiotensin converting enzyme (ACE) has been reported (OMIM
106180). This indel, known as ACE/ID is responsible for 50% of inter-individual
variability of plasma ACE concentration.