Transcript Stature in adolescent twins - UCSD Genetics Training Program

Gene tics of comple x human dis e ase traits.
Daniel T. OÕC
onnor, M.D.
Department of Medicine.
Nopm-252. First year curriculum in human genetics.
Wed Apr 1, 2009.
CMME-2047.
PURPOSE.
In the next two hours, we plan to cover the role of heredity and genes in
very common, non-Mendelian traits that are frequently seen by primary care
physic ians. We w ill illustrate how we establish the role of heredity on any trait,
and then methods to position the particular genes that influence such a trait.
WHAT IS A ÒCOMPLEX TRAITÓ?
Trait = phenotype.
Disease causation/etiology/origin:
The old conundrum of: ÒNature (heredity) versus nurture (environment)Ó.
How to solv e this riddle: Family/pedigree or tw in studies (see below ).
Frequency.
Most (>95%) of the disease encountered in internal medicine, f amily
medicine, pediatrics, neurology, or psychiatry is complex, and its origin is not well
understood. Not clearly completely hereditary (Mendelian) or environmental.
Read: Hy pertension, coronary artery disease, arrhythmia, stroke, aneurysm,
asthma, COPD, diabetes, obesity, schizophrenia, bipolar disorder . . .
Multifactorial: Genes, environment, gene-by-environment interactions.
Non-Me nde lian.
Mendelian: Gene  Trait (1:1; high penetrance ~100%).
Non-Mendelian: Only partial penetrance. Some people with the gene do not
get the trait. Some people who do not have the gene still get the trait.
Bimodality:
Hallmark of a major gene effect on a quantitative trait.
Bimodality:
Hallmark of a major gene effect on a quantitative trait.
Polygenic Traits
1 Gene
2 Genes
3 Genes
4 Genes
 3 Genotypes
 3 Phenotypes
 9 Genotypes
 5 Phenotypes
 27 Genotypes
 7 Phenotypes
 81 Genotypes
 9 Phenotypes
3
3
2
2
1
1
0
0
7
6
5
4
3
2
1
0
20
15
10
5
0
Complex Trait Model
Linkage
Marker
Gene1
Linkage
disequilibrium
Linkage
Association
Mode of
inheritance
Gene2
Disease
Phenotype
Individual
environment
Common
environment
Gene3
Polygenic
background
APPROACHES TO COMPLEX TRAITS.
(Genetic) epide m iology.
Demographics (age, sex, ethnicity, geography, fam hx).
ÒRis kÓ (s usceptibility) factors (see above).
Relative risk (RR): Given a risk f actor, what is the increase in trait
prevalence?
Es timator of RR: Odds ratio (OR). Given a risk f actor, OR = (have trait/do
not have trait).
OR +/- confidence interval (+/-95% CI) versus ref erence (no risk) =1.
No risk: RR or OR = 1.
OR +/- CI >>1  Risk (susceptibility) factor.
OR +/- CI <<1  Protective factor.
Test by 2 (2x2 contingency table).
CHGA genetic variation: Risk factor for hypertensive ESRD in blacks
Estimator of RR: Odds ratio (OR).
Given a risk factor, OR = (have trait/do not have trait).
J Am Soc Nephrol. 2008 Mar;19(3):600-14.
Chromogranin A polymorphisms are associated with hypertensive renal disease.
Salem RM, Cadman PE, Chen Y, Rao F, Wen G, Hamilton BA, Rana BK, Smith DW, Stridsberg M, Ward HJ, Mahata
M, Mahata SK, Bowden DW, Hicks PJ, Freedman BI, Schork NJ, O'Connor DT.
ÒInterm ediateÓ(risk) traits (phenotypes).
Intermediate in time and mechanism.
Bridging genotype and ultimate, late disease trait.
Ideally: Greater h2, earlier penetrance.
If biochemical assays: ÒBiomarkersÓ.
Gene
“Intermediate” in
time and causality
Cardiorenal disease trait
(later life)
Mechanism
Twins
(fixed at conception)
Twins: window into heritability (h2) of any phenotype
Monozygotic (MZ, identical) twins: Billy and Benny . . .
VP = VG + VE
h2 = VG/VP = 2(RMZ - RDZ)
Source: Guinness Book of World Records.
Total mole count for MZ and DZ twins
DZ twins - 199 pairs, r = 0.60
400
400
300
300
Twin 1
Twin 1
MZ twins - 153 pairs, r = 0.94
200
200
100
100
0
0
0
100
200
300
Twin 2
400
0
100
200
300
Twin 2
400
2
Heritability (h ) of traits in human twin pairs
100
87
+/-2
91
+/-1
Physiological
Biochemical
71
+/-4
60
70
+/-4
67
+/-4
59
+/-6
2
40
47
+/-7
50
+/-6
43
+/-7
Trait
Plasma epi
Plasma norepi
Baroreflex down
Cardiac output
DBP
SBP
Weight
Height
0
Baroreflex up
33
+/-9
20
SVR
Heritability (h ), mean +/- SEM
80
Physical
Family studies (twin pairs, pedigrees).
Fam hx as a risk factor.
In 1st degree relatives: Parents, siblings.
Heritability (h2): Fraction of trait variance accounted for by genetic variance.
VP = VG + VE
h2 = VG/VP
Estimate h2 from twin pair (or pedigree) studies:
Type of twin pair
Allele sharing across the genome.
MZ = monozygotic = identical.
100%
DZ = dizygotic = fraternal
~50% (on average), like any sib pair
Quick-and-dirty algorithm:
h2 = VG/VP = 2(RMZ – RDZ)
Family history as a risk factor for complex traits.
Family
history
West J Med. 1984 Dec;141(6):799-806. Understanding genetic and environmental risk factors in susceptible persons. Williams RR.
(Genetic) Òlink ageÓ: Co-s egregation of mark er and trait.
Versus in dependent segregation: Dif f erent chromosome, or far apart on same
chromosome.
Thus, marker and trait loci are w ti hin ~50 cM of Ò
geneticÓdistance.
cM: Just count the meiotic recombinants versus non-recombinants
cM = (Recombinant meioses / Total meioses)*(100)
Lower cM means closer (marker and trait loci)
Calibration. cM (genetic/meiotic distance) vs Mb (physic al distance):
1 cM = ~1 Mb (actually ~0.5-2.0).
Markers to span the genome f or linkage:
3000 cM/50 cM = 60 fully inf ormative (heterozygosity) markers in theory
In practice:
~400-800 highly inf ormative markers (multiallelic microsatellites)
~2000-10,000 less inf ormative markers (biallelic SNPs)
Linkage = Meiotic co-segregation
A3A4
A1A2
A1A3
A1A2
A1A4
A2A4
A3A4
A2A3
A3A2
Marker allele A1
cosegregates with
dominant disease
Linkage Markers…
Thomas Hunt Morgan – discoverer of linkage
Idiosyncratic features of genetic linkage (= meiotic co-segregation).
Units.
Metric = meiotic recombination (~50 meioses/generation).
Units of genetic distance = recombination during meiosis (cM).
cM = (recombinant meioses/total meioses)*100
E.g.: [8/(8+86)]*100 =(8/94)*100
= 8.5 cM
1 cM ~ 1 Mb
Range ~0.5-2.0
Varies by species, sex, chromosomal region (meiotic “hot spots”)
Significance.
“LOD” scores.
LOD = Log10 of the odds ratio for linkage
Odds ratio: Co-segregation (marker and trait)
Not
Significant: LOD >3.0 (i.e., odds ratio > 1000/1)
Why 3.0?
~50 “linkage groups” (meiotic breaks/generation), target =0.05. 1/50*1/20=1/1000.
Genetic linkage: Meiotic recombination distance in cM
cM = (recombinant meioses/total meioses)*100
[8/(8+86)]*100 =(8/94)*100
= 8.5 cM
1 cM ~ 1 Mb
Range ~0.5-2.0
Varies by species, sex, chromosomal
region (meiotic “hot spots”)
Mahata SK, Kozak CA, Szpirer J, Szpirer C, Modi WS, Gerdes HH, Huttner WB, O'Connor DT.
Dispersion of chromogranin/secretogranin secretory protein family loci in mammalian genomes.
Genomics. 1996 Apr 1;33(1):135-9.
Mouse SBP crosses: “Genome scan”  linkage.
Wright FA, O'Connor DT, Roberts E, Kutey G, Berry CC, Yoneda LU, Timberlake D, Schlager G. Genome scan for blood pressure loci
in mice. Hypertension. 1999 Oct;34(4 Pt 1):625-30.
LOD score: Log10 of the OR for linkage (marker, trait).
Log10 (non-recombinants / total meioses).
ÒSignificantÓ LOD > 3 (i.e., OR > 1000).
Why? ~50 linkage groups (meiotic breaks/generation), target =0.05.
1/50*1/20=1/1000.
Quantitative trait, non-parametric:
Regression. Plot:
Y = (Trait difference)2 w ithin each sib pair of DZ tw in pair.
X = Alleles shared IB D (identical by descent), 012.
Linkage is about loci, not particular alleles!
Terminology: ÒQTLÓ(Quantitative Trait Locus). LOD >3 peak for that trait.
Problems:
Need families based on probands (but relatives may not be available).
Ideal for Mendelian disorders, but limited success for complex traits.
Meiosis yields very large chromosomal sharing blocks by first-degree
relatives, but this can give rise to low spatial resolution of involved genes (very
broad LOD peaks).
Genetic linkage: What the data (marker, trait) look like.
J Clin Invest. 1996 May 1;97(9):2111-8. Quantitative trait locus mapping of human blood pressure to a genetic region at or near the
lipoprotein lipase gene locus on chromosome 8p22. Wu DA, Bu X, Warden CH, Shen DD, Jeng CY, Sheu WH, Fuh MM, Katsuya T, Dzau VJ,
Reaven GM, Lusis AJ, Rotter JI, Chen YD.
Genetic linkage: What the data (marker, trait) look like.
J Clin Invest. 1996 May 1;97(9):2111-8. Quantitative trait locus mapping of human blood pressure to a genetic region at or near the
lipoprotein lipase gene locus on chromosome 8p22. Wu DA, Bu X, Warden CH, Shen DD, Jeng CY, Sheu WH, Fuh MM, Katsuya T, Dzau VJ,
Reaven GM, Lusis AJ, Rotter JI, Chen YD.
(Allelic) as sociation: M ark er  Trait.
 ÒCandidateÓgene : Specific prior hypothesis for one gene (e.g.,
hemoglobin  thalass emia).
First, systematic polymorphism discovery (by resequencing).
Define haplotype/LD (linkage disequilibrium) blocks.
Blocks ~3-50 kbp in unrelated individuals. Origin is ancestral meiotic
recombination. Vary in length by ethnicity.
Assay SNPs ( or SNP haplotypes) in phenotyped individuals.
Haplotypes: Infer from individual diploid genotypes by probability.
Dichotomous trait: Disease cases versus controls. Analyze by 2 on
3x2 contingency table: 3 (diploid genotype classes; e.g., A/A, A/G, G/G) x 2
(case/control) contingency tables.
Continuous trait: Analyze by ANOVA, w ti h genotype (diploid genotype
classes; e.g., A/A, A/G, G/G) as independent variable.
Association derives eff ects of particular alleles, not just loci.
Advantage: Unrelated individuals (do not need families).
Problems:
Population stratification (artif actual association as a result of allele
f requency differences across populations).
The catecholamine biosynthetic pathway.
T Flatmark. Regulation of catecholamine biosynthesis. Acta Physiol Scand 168:1-17, 2000.
Tyrosine hydroxylase promoter haplotype 2: Pleiotropy.
Coordinate effects on both catecholamine excretion
and stress blood pressure response in twins
18
2
Norepinephrine h =49.6+/-6.7%, p=0.0001*
Haploty pe 2 on norepinephrine:p=0.0125*,
4.06% v ariation explained
Change in DBP during cold stress, mmHg
2
16
DBP h =32+/-8%, p=0.0003*
Haploty pe 2 onDBP: p=0.0004*,
3.73% v ariation explained
Pleiotropy : biv ariate likelihood ratio test
2
 =14.2, p=0.0002*
Haplotype 2
n=2 copie s
(n=32 individuals)
14
12
Haplotype 2
n=1 copy
(n=164 individuals)
10
8
Haplotype 2
n=0 copie s
(n=131 individuals)
6
4
2.4 10
4
2.6 10
4
2.8 10
4
3 10
4
3.2 10
4
3.4 10
4
3.6 10
Norpine phrine e xcre tion, ng/gm
Figure 8: TH haplotypes in vivo
Tyrosine hydroxylase regulatory polymorphism:
Interaction of genotype and sex to affect DBP
100
95
90
2-w ay ANOVA:
(Covariates: age, BMI)
Overall F=12.4, p<0.001*
Genotype F=1.30, p=0.273
Sex F=31.6, p<0.001*
Genotype * Sex F=3.14, p=0.044*
C-824T explains 3.4% of DBP variance
Alleles: C=61%, T=39%
Male DBP
Female DBP
Males alone:
Genotype F=3.12, p=0.045*
Females alone:
Genotype F=0.015, p=0.985
2
DBP, mmHg
(mean +/- SEM)
HWE:  =0.54, p=0.46
85
80
75
70
85.3
+/-2.6
(n=75)
82.8
+/-1.3
(n=285)
78.3
+/-1.5
(n=234)
74.9
+/-1.5
(n=233)
74.9
+/-1.2
(n=329)
73.2
+/-2.1
(n=110)
65
60
C/C
C/T
T/T
Tyrosine hydroxylas e (TH) prom ote r C-824T diploid genotype
Disease trait
Mechanism
Gene

Biochemical trait

Physiological trait
Time
Hypertension: “Intermediate” phenotypes and candidate genes.
Tyrosine hydroxylase C-824T

 Catecholamines

 Baroreceptor function
 Stress blood pressure

 Hypertension
Figure 7: Intermediate phenotypes
 GWAS (Genome Wide As s ociation Study). Hypothe s is: Com mon
dis e ase/Common Variant.
Markers to span the genome for association:
~3.3 Gbp (1 Gbp = 109 bp) genome / 500K SNPs = ~ 6000 bp (~6 kbp).
The only variants spaced this closely (i.e., the common) are SNPs .
Spacing based on HapMap <www.hapmap.org> and LD (li nkage
disequilibrium) blocks.
HapMap: 270 people world-w di e typed at ~4 million SNPs across the
genome.
Within an LD block, SNPs are highly correlated (r2 ~0.6-1.0).
Try to ÒtagÓeach LD block across the genome.
Advantage: Unrelated individuals (do not need families).
Problems:
Population stratification (artif actual association as a result of allele
f requency differences across populations).
Statistical challenges to GWAS: Many LD blocks tested, modified
target p=5x10-7.
Solution: Replication in an independent sample, for joint (multiplicative,
) probability.
Haplotype:
Ordered array of alleles along a single chromosome.
Biallelic SNPs (single nucleotide polymorphisms).
Typically “transitions”:
Purine  Purine (G  A)
Pyrimidine  Pyrimidine (C  T)
Chromosome
T
C
A
G
C
T
G
A
Paternal
Maternal
pter  qter
5’  3’
“Linkage disequilibrium” (LD):
Local, marker-on-marker locus
Equilibrium = randomness (no correlation, r2=0)
Disequilibrium = non-random (correlated, r2>0)
Marker-on-trait locus: Mapping tool
0.0
0.9
Paternal
Maternal
0.5
0.5
0.9
0.9 0.0 0.9 0.9
r2
0.9
T
A
C
G
T
A
C
G
C
G
T
C
C
G
T
A
5’  3’
Ancestral (shared)
Meiotic recombination
Biallelic
SNPs
Linkage disequilibrium (LD).
Marker  trait
Marker  marker
In population genetics, linkage disequilibrium is the non-random
association of alleles at two or more loci.
Linkage disequilibrium describes a situation in which some
combinations of alleles or genetic markers occur more or less frequently
in a population than would be expected from a random formation of
haplotypes from alleles based on their frequencies.
Non-random associations between polymorphisms at different loci
are measured by the degree of linkage disequilibrium (LD).
The level of linkage disequilibrium is influenced by a number of factors
including the rate of meiotic recombination (crossovers) and the rate
of mutation.
HapMap: View variation patterns
Triangle plot shows LD
values using r2 or
D’/LOD scores in one or
more HapMap population
The International HapMap Project
(Identification of SNPs that ‘tag’ haplotypes within blocks)
Daly, M.J., Rioux, J.D., Schaffner, S.F., Hudson, T.J. and Lander, E.S. (2001). High-resolution haplotype
structure in the human genome. Nature Genet. 29: 229-232.
Linkage disequilibrium (LD) “blocks” on human chromosome 14q32
100 kbp displayed, from <www.HapMap.org>
3 shortrange
(~30 kbp)
LD blocks
No long-range
(~100 kbp) LD
Gene-by-Environment (GxE) interaction probed by MZ twin intra-pair trait differences:
HDL-cholesterol effect of T-cadherin (CDH13, novel adiponectin receptor)
genetic variation revealed by dense, genome-wide profiling in 1662 MZ pairs
p=8.5x10-8

QuickTime™ and a
TIFF (LZW) decompressor
are needed to see this picture.
100 kbp
Region of the genome around around SNP rs9941339 in CDH13 (T-cadherin = novel adiponectin receptor) on 16q24 associated with intra
MZ pair differences in HDL cholesterol (GWAS in n=1662 MZ pairs). Black points represent SNPs genotyped in the study and gray points
represent SNPs whose genotypes were imputed. In middle panel, red line shows the fine-scale recombination rate (centimorgans per Mb)
estimated from Phase II HapMap and the black line shows the cumulative genetic distance (in cM). Association p=8.5x10^-8.
 Com mon diseas e / Rare variant hypothe s is.
Or, accumulation of excess rare variants: Non-synonymous (amino acid
replacement) cSNPs .
Technology: Extensive re-sequencing in large numbers of cases vs
controls. Typically resequence a ÒpathwayÓin population trait extreme individuals
(boost statistical power).
Analyses: Summed 2, cases versus controls. Computational assessment
of amino acid change f unctionality (SIF T, PolyPhen). Ultimately functional
studies.
Accumulation of deleterious rare amino acid substitution
variants at extremes of human body mass index (BMI)
Ahituv N, Kavaslar N, Schackwitz W, Ustaszewska A, Martin J, Hebert S, Doelle H, Ersoy
B, Kryukov G, Schmidt S, Yosef N, Ruppin E, Sharan R, Vaisse C, Sunyaev S, Dent R,
Cohen J, McPherson R, Pennacchio LA. Medical sequencing at the extremes of human body
mass. Am J Hum Genet. 2007 Apr;80(4):779-91.
 ÒDrilling dow n to the ÒQTNÓ (Quantitative Trait Nucleotide)Ó.
Problem: Even after successful allelic association, the lower limit of
resolution is the LD block (~3-50 kbp). So where, within that LD block, is the
causative variant?
Solution: Studies of the putative responsible variant in a system outside of
the human organism: in vitro (test-tube enzymology), in cella (transfection into
cultured cells), or in vivo (transgenic mice).
Drilling down to the “QTN”
(“Quantitative Trait Nucleotide”)
• Haplotype “block” is the lower limit of
resolution of marker-on-trait mapping.
• Switch to studies of associated variants:
– In cella. E.g., transfected/expressed variants.
– In vitro. E.g., kinetic properties of variants.
– In vivo: transgenic mice (BAC haplotype
variant expression on knockout background).
Positional candidate genetic loci.
“Positional candidate” locus
Wong C, Mahapatra NR, Chitbangonsyn S, Mahboubi P, Mahata M, Mahata SK, O'Connor DT. The angiotensin II receptor (Agtr1a):
functional regulatory polymorphisms in a locus genetically linked to blood pressure variation in the mouse. Physiol Genomics. 2003 Jun
24;14(1):83-93.
“Positional candidate” locus
Wong C, Mahapatra NR, Chitbangonsyn S, Mahboubi P, Mahata M, Mahata SK, O'Connor DT. The angiotensin II receptor (Agtr1a):
functional regulatory polymorphisms in a locus genetically linked to blood pressure variation in the mouse. Physiol Genomics. 2003 Jun
24;14(1):83-93.
“Positional candidate” locus
Wong C, Mahapatra NR, Chitbangonsyn S, Mahboubi P, Mahata M, Mahata SK, O'Connor DT. The angiotensin II receptor (Agtr1a):
functional regulatory polymorphisms in a locus genetically linked to blood pressure variation in the mouse. Physiol Genomics. 2003 Jun
24;14(1):83-93.
Promoter variant characterization
Transcription
Transcription
Promoter/reporter
plasmid (pGL3-Basic)
Promoter/reporter
plasmid (pGL3-Basic)
Transfection
Luciferase
transcription
Luciferase
translation
Nucleus
Cytosol
Chromaffin cell
Cell
lysis
Firefly luciferase
enzymatic activity assay
“Positional candidate” locus
Wong C, Mahapatra NR, Chitbangonsyn S, Mahboubi P, Mahata M, Mahata SK, O'Connor DT. The angiotensin II receptor (Agtr1a):
functional regulatory polymorphisms in a locus genetically linked to blood pressure variation in the mouse. Physiol Genomics. 2003 Jun
24;14(1):83-93.
 ÒRis kÓalle le (gene) vers us Òmodifie rÓ alle le (gene ).
Risk allele: Allele that increases risk/susceptibility f or disease, in
longitudinal studies.
Example: CFTR (Cl - channel) F508  Cystic fibrosis.
Modifier allele: Allele that influences course of the disease, once that
disease has occurred.
Example: KCNMB1 Glu65Lys  Rapid progression of renal
dysf unction.
Genomics in Dz risk:
Two kinds of Gene-by-Environment interactions.
Gene:
Susceptibility
Gene:
Modifier
Stable
Dz
Exposure
Switch
Rapid decline
Switch
Not
Initiation
(case/control study)
Time (decades)
Drug
Outcome
(longitudinal study)
KCNMB1 Glu65Lys effect on rate of GFR decline
in hypertensive nephrosclerosis (NIDDK AASK)
1
Overall: p<0.001
KCNMB1: p=0.030
Covariate: Pro_cr p<0.001; Mb_GFR p<0.001;
BP goal p=0.196; Drug p=0.065
Permutation test: p=0.017
Alleles: E=95%, K=5%. HWE: p=0.552
0
-1
-1.88+/-0.08
(704)
-2.46+/-0.25
(72)
-2
-3
E/E1
2
E/K+K/K
KCNMB1 E65K genotype
Figure 3A
KCNMB1 Glu65Lys predicts long-term loss of renal function
in hypertensive nephrosclerosis (NIDDK AASK)
Cumulative survival
Log rank p=0.0190
Event:
ESRD requiring dialysis or doubling of serum creatinine
E/E
E/K+K/K
E/K+K/K censored
E/K+K/K
E/E
E/E censored
Months after enrollment
Figure 3B
KCNMB1 Glu65Lys: Hypothesis for effect on GFR
VOCC
K+
Ca2+
K+
Ca2+
VOCC
BK
BK
Wild-type
Variant
b 1 subunit
(65Lys)
b 1 subunit
(Glu65)


1 subunit
K+
K+
Contracted mesangial
or smooth muscle cell
Higher flux
Lower flux
1 subunit
Relaxed mesangial
or smooth muscle cell
: Ca2+ ions
 : Inhibition
Figure 4
WHAT HAVE WE LEARN ED?
¥ ÒComplexÓ rait
t definition.
¥ Risk (susceptibility) f actors.
¥ ÒIntermediateÓ(risk) traits (phenotypes).
¥ Family/twin studies and heritability.
¥ Genetic linkage: Co-segregation of marker and trait.
¥ Allelic association: Marker  trait.
¥ ÒCandidateÓgene.
¥ GWAS (Genome Wide Association Study). Hypothesis: Common disease/Common Variant.
¥ Common disease / Rare variant hypothesis.
¥ ÒDrilli ng down to the ÒQTNÓ Q
( uantitative Trait Nucleotide)Ó.
¥ ÒRisk Ó allele (gene) versus ÒmodifierÓallele (gene).
SAMPLE QUESTIONS.
The idea has been to develop a f eeling for concepts, rather than details. The
responses I w ould make are indicated by Ò*Óor ÒÓ.
Which of these phenotyped datasets would allow you to establish heritability (h2)
of a trait? Pick all that are correct.
Tw n
i pairs *
Nuclear f amilies (parents, siblings, children) *
Ex tended pedigrees (including second degree relatives) *
Random sample of the US urban population taken from Chicago, Illinois.
Probands from a case/control study of asthma.
Which of the follow n
i g data on f amilies and disease would allow estimation of
heritability? Pick one.
Street and city address w tih zip code.
Trait measurements in MZ and DZ tw n
i pairs. *
Match the gene-finding method w ith the type of inf ormation discovered.
Method
Information
Linkage

Loci
Association

Alleles
What biological process gives rise to haplotype (LD) blocks? (Pick one).
Meiotic recombination *
X-irradiation
Cosmic rays
Site-directed mutagenesis
Which of the follow ing epidemiologic parameters can help to estimate whether heredity influences
a trait? Pick one.
Familial relative risk, estimated by odds ratio *
Family income
Federal and state income tax returns
College tuition payments
If you w ished to scroll through the entire genome searching f or an allelic association to a complex
trait in cases versus controls, how many SNP genotypes would you need to type, in order to
capture the correlated LD blocks across the genome? Pick one.
5K
50K
500K *
5 million