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The beauty of admixture

March 2005
Nature Genetics 37(2):118-9

March 2005
37(2):118-9

DOI:10.1038/ng0205-118

Source
PubMed

Authors:

Ariel Darvasi

Hebrew University of Jerusalem

Sagiv Shifman

Hebrew University of Jerusalem

Admixture mapping is an old concept that has only now been applied with markers across the entire genome. Such a study scanning an African American population identified two chromosomal regions affecting susceptibility to hypertension.

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NEWS AND VIEWS

118 VOLUME 37

NUMBER 2

FEBRUARY 2005

NATURE GENETICS

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The beauty of admixture

Ariel Darvasi & Sagiv Shifman

Admixture mapping is an old concept that has only now been applied with markers across the entire genome.

Such a study scanning an African American population identified two chromosomal regions affecting

susceptibility to hypertension.

Anecdotally, children of parents of mixed

ethnicities are exotically beautiful. More sci-

entifically established is the merit of admixed

populations for gene mapping purposes. The

potential value of admixed populations was

suggested more than half a century ago

Substantial theoretical and practical aspects

have been developed since then (reviewed

by McKeigue

). A genome scan to identify

genes affecting a complex trait is now pre-

sented for the first time to our knowledge

by Xiaofeng Zhu and colleagues on page 177

of this issue

The admixed population

The concept behind admixture mapping is

simple (Fig. 1). In essence, admixture map-

ping is most similar to linkage analysis in

experimental crosses with inbred strains,

with specific similarity to advanced inter-

cross lines

. An advanced intercross line is a

population derived from two inbred strains

that were randomly intercrossed for several

generations. An advanced intercross line

constitutes the ideal admixed population: all

variations can be identified in one of the two

progenitors, the mean ancestral composition

is 50% for each progenitor, allele frequencies

in the progenitor populations are either 1 or

0, and random mating is followed after a sin-

gle generation of intercrossing the progeni-

tors. In a human admixed population, these

ideal conditions will never be met, resulting

in decreased power for mapping purposes.

Except for gene effect, which has a strong

influence on power, the parameter that mostly

affects power, specifically in admixture map-

ping, is the extent of difference in allele fre-

quency between the ancestral populations

Ariel Darvasi is in The Life Sciences Institute,

The Hebrew University, Jerusalem 91904, Israel.

Sagiv Shifman is in the Wellcome Trust Centre

for Human Genetics, Oxford OX3 7BN, UK. e-

mail: arield@cc.huji.ac.il, sagiv@well.ox.ac.uk

Case Control

Population 1

Population 2

Disease gene location

Figure 1 Schematic of one chromosome pair from each of several individuals in an admixed population.

A group of cases (for a given disease) and a group of controls are separately presented at the bottom left

and the bottom right, respectively. For one of the control individuals (arrow), a schematic presentation of

all its ancestors in the last four generations is shown in the upper part of the figure. Admixture mapping

can be ideally applied if population 1 (blue) and population 2 (red) carry a different allele at the disease

locus (dashed line). Whole-genome scanning under the admixture mapping strategy consists of scanning

the genome and identifying the regions with an excess of ‘red’ ancestry in the cases versus the controls,

assuming that the ‘red’ population carries the predisposition allele. The size of the blocks from different

ancestors will depend on the number of generations since the populations were mixed.

NEWS AND VIEWS

NATURE GENETICS

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FEBRUARY 2005 119

For example, in the extreme case where the

allele affecting a disease has the same fre-

quency in both ancestral populations, admix-

ture mapping cannot be efficiently applied. In

contrast, the power of admixture mapping

will be only mildly affected by the percentage

contributed by each population to the admix-

ture, as long as that proportion is between

20% and 80% (ref. 5).

Genome scan for hypertension

In an effort to identify chromosomal regions

affecting hypertension, Zhu et al.

carried out

a genome scan with 269 microsatellite mark-

ers and a total of 737 cases (hypertensives)

and 573 controls (normotensives). Cases

and controls were selected from the African

American population. African Americans are

an admixed population with ∼75% African

ancestry and ∼25% European ancestry

and

are thus appropriate for admixture mapping.

All individuals were sampled from three net-

works (GenNet, GENOA and HyperGEN) in

geographically distinct locations participat-

ing in the Family Blood Pressure Program.

Zhu et al.

initially explored hyperten-

sive cases only, independently in the three

networks, and found an excess of African

ancestry in more than one network on chro-

mosomes 4, 6 and 21. In particular, two

markers around 6q24 showed an excess of

African ancestry in all three populations.

To validate the significance of these results,

they compared the excess of African ances-

try found in the cases with that found in

controls. The excess of African ancestry was

shifted upwards in cases relative to controls.

The entire shift can be attributed to two chro-

mosomal regions at 6q24 and 21q21 where

the excess of African ancestry was signifi-

cant in cases but not in controls. Therefore,

these findings suggest that the chromosomal

regions 6q24 and 21q21 contain genes affect-

ing predisposition to hypertension. Support

for the chromosome 6q24 findings can be

drawn from previous linkage studies that

found evidence for linkage between this

chromosomal region and hypertension

or related traits

7,8

. The large size of this

chromosomal region (37 cM, including all

markers with Z score >2.5) may suggest that

more than one gene affecting hypertension is

present. This is not unexpected, as cis-acting

linked genes will behave as a single gene with

a larger effect (the combined effect of the two

genes) in an admixture mapping experiment,

hence having greater power of being picked

up in a genome scan. The 21q21 region needs

further replication to establish its validity, as

this region has not previously been suggested

to be associated with hypertension.

A complementary approach

Two main approaches have been used to

search for genes affecting complex traits:

linkage analysis and association analysis

Linkage analysis has two key disadvantages:

relatively low statistical power for detecting

modest effects

, and low mapping resolution,

which prevents gene identification even after

a region has been detected

. Association anal-

ysis also has two key disadvantages. Because

this approach is based on linkage disequilib-

rium or on testing the potential functional

polymorphisms, the number of polymor-

phisms that need to be scanned in the entire

genome is painfully high (>100,000)

. The

second disadvantage is the diminishing power

that occurs with high genetic heterogene-

ity

. Admixture mapping is a strategy that

falls between linkage analysis and association

analysis in many respects (Table 1).

Although admixture mapping has a sub-

stantially lower mapping resolution than

association analysis, as long as genotyping

costs are a limiting factor, admixture map-

ping will be a good approach for the initial

genome scan. Admixture mapping is particu-

larly appropriate for traits for which there is a

large difference in the phenotypic prevalence

in the ancestral populations of the admix-

ture. Nevertheless, admixture mapping is

not limited to those traits and will still work

if the allele frequencies of the disease locus

are different in the ancestors of the admixed

population. This is more likely to occur when

the disease prevalence varies in the ancestral

populations.

Given the advantages of admixture map-

ping, it is notable that this experiment has

only now been done. One reason for this

might be the notion (which might be cor-

rect) that more markers are required for an

adequate whole-genome scan with admix-

ture mapping

than were used in the current

experiment. In addition, admixture mapping

is efficient only if the allele frequencies of

the markers are substantially different in the

ancestral populations. In that respect, it now

seems that microsatellite panels might be

more informative than originally thought

Consequently, a standard panel of markers,

normally used in linkage experiments, suc-

cessfully served Zhu et al.

in their admix-

ture mapping study. A word of caution is

appropriate, though. The unexpected success

might be due to the specific constellations

particular to the current experiment, includ-

ing chance. Therefore, the study of Zhu et

al.

, which applied admixture mapping to

hypertension and concluded with success-

ful and robust results, will still require some

replications in other traits and with other

samples before its generality can be estab-

lished. The current results, however, are

undoubtedly promising enough to encour-

age the scientific community to carry out

these essential replications.

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Table 1 Main characteristics of mapping strategies

Linkage analysis Admixture mapping Association analysis

Statistical power Low High

High

Number of SNPs required for whole

genome scan

Low Low High

Sensitivity to genetic heterogeneity Low Moderate High

Mapping resolution Poor Intermediate Good

*Power diminishes to zero with equal allele frequencies in the ancestral population.

Analyse du polymorphisme moléculaire de gènes de composantes de la qualité des fruits dans les ressources génétiques sauvages et cultivées de tomate ; recherche d’associations gènes/QTL

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A panel of 32 AIMs suitable for population stratification correction and global ancestry estimation in Mexican mestizos

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Background Association studies are useful to unravel the genetic basis of common human diseases. However, the presence of undetected population structure can lead to both false positive results and failures to detect genuine associations. Even when most of the approaches to deal with population stratification require genome-wide data, the use of a well-selected panel of ancestry informative markers (AIMs) may appropriately correct for population stratification. Few panels of AIMs have been developed for Latino populations and most contain a high number of markers (> 100 AIMs). For some association studies such as candidate gene approaches, it may be unfeasible to genotype a numerous set of markers to avoid false positive results. In such cases, methods that use fewer AIMs may be appropriate. Results We validated an accurate and cost-effective panel of AIMs, for use in population stratification correction of association studies and global ancestry estimation in Mexicans, as well as in populations having large proportions of both European and Native American ancestries. Based on genome-wide data from 1953 Mexican individuals, we performed a PCA and SNP weights were calculated to select subsets of unlinked AIMs within percentiles 0.10 and 0.90, ensuring that all chromosomes were represented. Correlations between PC1 calculated using genome-wide data versus each subset of AIMs (16, 32, 48 and 64) were r² = 0.923, 0.959, 0.972 and 0.978, respectively. When evaluating PCs performance as population stratification adjustment covariates, no correlation was found between P values obtained from uncorrected and genome-wide corrected association analyses (r² = 0.141), highlighting that population stratification correction is compulsory for association analyses in admixed populations. In contrast, high correlations were found when adjusting for both PC1 and PC2 for either subset of AIMs (r² > 0.900). After multiple validations, including an independent sample, we selected a minimal panel of 32 AIMs, which are highly informative of the major ancestral components of Mexican mestizos, namely European and Native American ancestries. Finally, the correlation between the global ancestry proportions calculated using genome-wide data and our panel of 32 AIMs was r² = 0.972. Conclusions Our panel of 32 AIMs accurately estimated global ancestry and corrected for population stratification in association studies in Mexican individuals. Electronic supplementary material The online version of this article (10.1186/s12863-018-0707-7) contains supplementary material, which is available to authorized users.

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Human genetics is now at a critical juncture. The molecular methods used successfully to identify the genes underlying rare mendelian syndromes are failing to find the numerous genes causing more common, familial, non-mendelian diseases. With the human genome sequence nearing completion, new opportunities are being presented for unravelling the complex genetic basis of non-mendelian disorders based on large-scale genome-wide studies. Considerable debate has arisen regarding the best approach to take. In this review I discuss these issues, together with suggestions for optimal post-genome strategies.

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