QTLrevTIPS

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Believe it or not, QTLs are accurate!Adam H. Price

School of Biological Sciences, University of Aberdeen, Aberdeen, UK AB24 3UU

It is generally believed that mapping quantitative trait

loci (QTLs) does not accurately position genes under-

lying polygenic traits on the genome, which limits the

application of QTL analysis in marker-assisted selection

and gene discovery. However, now that a few plant

QTLs have been cloned or accurately tagged, it appears

that they might be accurate to within 2 cM or less. This

means that there will be circumstances when map-

based cloning using only original mapping data would

be a realistic option that avoids time-consuming and

expensive fine mapping. Acceptance of this view would

enhance the value of past and future mapping exper-iments, particularly those revealing small and environ-

mentally sensitive QTLs that are often considered

intractable at the molecular level.

Map-based cloning and the accuracy of QTLs

The genes responsible for genetic variation of quantitat-

ively variable traits constitute the vast majority of the

functional genetic diversity of the biosphere and probably

represent the main sites at which selection influences

evolutionary heredity. In the early days of genetics, major

genes were the focus of the first inheritance studies and

only later did the focus widen to include characterization

of the biometrics of more complex traits [1]. Likewise, the

technology of gene cloning, which originally targetedmajor genes, is increasingly including those genes

responsible for quantitative, multigenic traits [2]. Identi-

fying the genes behind these quantitative trait loci (QTLs)

has been described as the greatest challenge for geneti-

cists this century [3]. A powerful way to characterize these

genes (in terms of numbers and relative contribution) is to

use a mapping population to identify QTLs: there has been

a tenfold increase in the number of QTL studies published

annually over the past 10 years. Once QTLs have been

identified, the next challenge is to identify the genes. One

of the most promising ways to do this is positional cloning,

where the QTL is linked to the physical sequence of the

genome via the sequence of large insert clones [e.g.bacterial artificial chromosomes (BACs)] [2]. For those

species that have been sequenced, there should be no need

to generate the large insert clones because gene order is

already known. All that is required is to locate a QTL on

the sequence and then look for candidate genes. However,

a big obstacle exists in attempting to link a QTL on a

genetic map of a primary mapping population to a position

on a sequence map. Theory suggests that the positioning

of a QTL in a primary population is not accurate, covering

a region up to and over 20 cM depending on the type of

population, the number of individuals scored, and the

quality of the data [35]. The 1 likelihood of odds (LOD)

support interval with which QTLs are commonly reported

is often a large region covering 10 to 30 cM [5]. This region

spans the location on the genome where the statistical

support for the QTL is within one order of magnitude of

the peak statistic (Figure 1). In most species, this covers in

the order of one hundredth of the entire genetic map and

could include up to 2000 genes. Such a large number of

genes cannot be tested for candidacy so it is accepted that

to identify candidate genes based on map position, the

mapping must be done more accurately. This involvesMendelianizing the QTL in a near isogenic pair that is

used to make a secondary population for fine mapping. In

this approach, many individuals (sometimes O1000) are

genotyped for markers around the QTL. Those that show

recombination in the region are phenotyped for the trait,

allowing a much more accurate QTL localization, nor-

mally to !1 cM. This distance can represent from 50

genes to as few as 1 gene. However, this fine mapping

requires considerable expense and is practically difficult if

the QTL effect is small because the small genetic effect

limits the ability to assess phenotypic differences accu-

rately between allelic variants in the fine mapping

population. Most small QTLs will not be tractable using

fine mapping. But perhaps the original QTL mapping ismore accurate than was previously supposed.

Tagged or cloned genes are near their original QTL

position

A growing number of natural allelic variations in plants

have now been successfully characterized to the gene or

individual sequence polymorphism [4], and this includes a

few examples of cloning genes for QTLs. A recent article in

Trends in Plant Science also highlights the success of

cloning QTLs [2]. These first plant QTLs are now being

isolated and QTLs are being tagged using the fine

mapping approach and, therefore, the precision of QTL

analysis can now be evaluated (Table 1). The concept isillustrated in Figure 1. However, it is important to

distinguish between major QTLs, where a single locus

explains a large proportion of the genetic variation, and

the small QTLs exemplified in Figure 1. This is because

the precision of QTL location is considered to be

proportional to its contribution to the heritability of the

trait [5]. For example, it has been shown theoretically that

if five QTLs of equal size (effect) each control a trait of

heritability of 50% and, therefore, each have a heritability

of 10%, they can be placed with 95% confidence into a

region of 30 cM when tested on 300 F2 plants [5]. Accuracy

of gene position is theoretically increased by choosing onlyCorresponding author: Price, A.H. ([email protected] ).

Available online 17 April 2006

Opinion TRENDS in Plant Science Vol.11 No.5 May 2006

www.sciencedirect.com 1360-1385/$ - see front matter Q 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.tplants.2006.03.006
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to study QTLs with a relatively large contribution to the

trait in question (i.e. major QTLs) or by increasing the

heritability of the small QTL (by reducing environmental

variation, by having more replicates or by combining

analysis of several traits that the gene affects pleiotropi-

cally (e.g. [8]). Here I attempt to assess the position of

cloned and a few tagged genes relative to the original

mapping position where the data published are suffi-

ciently detailed to allow it (usually it is not). Adifferentiation is made between major and small QTLs

using the arbitrary criteria whereby a small QTL is one

where its contribution to overall variation is in the order of

25% or less.

Accuracy of major QTLs

A fruit size QTL of tomato called fw2.2 was one of the first

tagged QTL in plants and explained 3047% of the

phenotypic variation in a population of 264 BC1s [9];

subsequently, this QTL has been cloned [10] and was

found to be within 1.6 cM of its original QTL peak. Ovate

is another major QTL affecting tomato fruit shape andexplained 4767% of variation [11]; this has now been

characterized [12] and was found to be at the marker

originally identified. In Arabidopsis, the FLOWERING1

(FLW1) QTL, which explained 27% and 62% of variation in

flowering time of long and short days, respectively, has

been detected at a marker for the underlying gene [13].

Another Arabidopsis flowering time gene (Cry2) has been

shown to be responsible [14] for a QTL explaining between

20% and 55% of variation in leaf number [15] and was

between 0.8 and 1.6 cM from the QTL peak for this trait,

with the mean QTL position being only 0.1 cM from the

gene. Another QTL in Arabidopsis that now has a

molecular explanation is the transpiration efficiency

QTL on chromosome 1, which explained 2164% of

variation in carbon isotope discrimination, and was

found to be within 1 cM of the ERECTA gene, which is

responsible [16]. In wheat, a cold-regulated transcription

factor, Cbf3, has been identified as a candidate gene

located at 2.8 and 1.6 cM from QTL peaks for frost

tolerance assessed in different years and less than

0.5 cM from the mean position of those screens [17]. In

this example, the QTLs explained between 40% and 48%

of the variation. A major QTL in wheat explaining 66% ofthe variation for grain protein content was first mapped to

marker Xmwg79 in a population derived from a chromo-

some substitution line that was therefore segregating in

only one chromosome [18]. The peak has proved to be

within 0.2 cM of the tagged gene [19]. The gene Ppd-H1,

which regulates photoperiod response, has been identified

[20] after fine mapping [21] and is located 1.9 cM from the

original position of a QTL described as having a highly

significant effect on flowering time and identified in 94

double haploid populations [22]. In soybean, a QTL (FT1)

explaining 62% of the variation in flowering time

originally mapped to marker satt365 [23], which has

subsequently been found to be 0.1 cM from the gene afterfine mapping [24]. A gene controlling flowering time has

been isolated from Brassica [25] that was within 1 cM of

the peak LOD of a QTL explaining 45% of the variation

[26]. Another example from Brassica is provided by Johan

Pelemanet al. [27]: a QTL explaining 43% of the variation

for euric acid content was mapped to 11.3 cM in 184 F 2plants, and subsequent fine mapping revealed its accurate

location to be 12.3 cM.

Accuracy of small QTLs (low heritability)

QTLs for several traits centred on the teosinte branched 1

(tb1) locus in two maize ! teosinte crosses have been

reported [28]. Despite being a major gene for branching,

TRENDS in Plant Science

0

2

4

6

8

10

100 120 140 160 180 200 220

sd1

Position on chromosome 1 (cM)

One LOD supportinterval for each trait

DroughtControlAverageLow nitrogenLow light

LOD

Figure 1. The concept of QTL accuracy. QTL scans for plant height from 160 recombinant inbred lines (RILs) of the Bala!Azucena mapping population of rice [6] are shown,

together with the position of the sd1 (semi-dwarfing) locus. The gene is a gibberellin oxidase [7] and Bala has the 383 bp deletion mutant allele. It maps to 176 cM in this

population.In different environments, plant height QTLsexplain 7.8to 14.6% of the variation and peaks occurat 166,171, 173and 183 cM witha meanposition of 173 cM. The

1 LOD confidence intervals range from 10-18 cM. The position of the QTL obtained by combining all data across all environments (orange) is 174 cM, only 2 cM from the

strong candidate gene. For the drought treatment (blue), the blue broken lines indicate the generation of the 1 LOD support interval.


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this locus displays pleiotropy and QTLs for several traits

mapped to the locus. The location of QTLs explaining

1225% of the variation [29] ranged up to 9 cM either side

of the tb1 locus, but the mean position of all 14 QTLs was

less than 1 cM from the gene that was subsequently

identified [30].

In rice, Masahiro Yanos group have tagged or isolated

the genes for five heading date (Hd) QTLs [31,32]. Genes

Hd1Hd5 were found to be 0.5, 0.3, 0.0, 2.6 and 1.2 cM,

respectively, from the position originally identified by a

single mapping experiment [33]. Although the contri-bution of each QTL to the overall variation was not given,

because five QTLs were detected at least three must be

small QTLs by the definition used here. Yanos group has

also tagged a gene for phosphate uptake [34] within 1 cM

of the QTL peaks for phosphate uptake and three other

related traits that explained 1928% of the variation in a

population of 98 backcross inbred lines [35]. Tagging of an

environmentally sensitive grain weight QTL explaining

1017% of variation in an advanced backcross population

of258 BC2F2s, which was originally mapped to the nearest

marker RZ452 [36], revealed the fine map location to be

1.6 cM from that marker [37]. In the out-breeding crop

potato, an invertase gene is an exceptionally strong

candidate for a sugar content QTL [38] that explained

5.714.5% of the phenotypic variation [39] and mapped

less than 3 cM from it although the position of the peak is

not given, just the nearest marker CP137.

Map-based cloning without fine mapping

From the data presented in Table 1, the position of genes

underlying major QTLs ranges from 0.0 to 1.9 cM and the

mean is less than 0.7 cM, whereas for small QTLs the

range is 0 to !3 cM and the mean is !1.2 cM. Although

the list given in Table 1 might not be exhaustive, an

attempt was made to find as many examples as possible. It

is possible that the literature itself has a bias because

examples where QTL are distant from the underlying

gene are not represented or because they have proved

more difficult to clone or to tag. None the less, these data

indicate that the position of a QTL obtained from a

primary mapping population can be an order of magnitude

more accurate than is often stated. This appears to be true

even for small QTLs, particularly where accuracy can be

improved by averaging peak positions from different

screens or by combining multiple data sets to increase

the heritability of the trait. This implies that a successfulapproach to identifying candidate genes for small QTLs

for which fine mapping is likely to be problematic but

where multiple data sets are available to improve

accuracy, is to test the candidacy of genes within 12 cM

either side of the mean QTL position detected in the

primary mapping population. The genes around the QTL

can be tested to see if: (i) they are expressed in the

conditions in which the QTL is detected; (ii) if they have

allelic diversity in expression; or (iii) if they show amino

acid sequence polymorphism between the parents of the

mapping population (or populations) displaying the QTL.

It has been demonstrated that expression arrays

represent a powerful way to address points (i) and (ii) inplants and animals [13,40]. Proof of function can then be

strengthened by: characterizing mutants of the candidate

gene; association mapping (e.g. [25,41,42]); and conduct-

ing gene complementation (replacing one allele by

another) either by crossing (e.g. [43]) or by transgenics

(e.g. [16]). Positional cloning of small QTL without fine

mapping appears to be a realistic possibility for species

such as rice and Arabidopsis that have been sequenced

and, although the probability of success will inevitably

depend on the quality of the mapping and trait data, this

realization should greatly increase the potential value of

past and future QTL mapping experiments.

Table 1. The distance between original QTL peak position and subsequently tagged or cloned genes in plant species

Species Trait Gene or tagged locus Mapping populationa Distance to original LOD

peak (cM)

Refs

Major QTLs

Tomato Fruit size fw2.2 264 BC1s !1.6 [9,10]

Tomato Fruit shape Ovate 82 F2s 0.0 [11,12]

Arabidopsis Flowering time FLW1 98 RILs 0.0 [13]

Arabidopsis Flowering time CRY2 162 RILs 0.1b [14,15]

Arabidopsis Transpiration ERECTA 100 RILs !1.0 [16]

Wheat Frost tolerance Cbf3 74 RILs 0.1b

[17]Wheat Grain protein GPC 85 RICLs 0.2 [18,19]

Barley Photoperiod response Ppd-H1 94 DH 1.9 [2022]

Soybean Flowering time FT1 156 RILs 0.4 [23,24]

Brassica Flowering time COL1 88 BC1s 1.0 [25,26]

Brassica Euric acid content E1 184 F2s 1.0 [27]

Small QTLs

Maize! teosinte Shoot morphology tb1 290 F2s 0.6b [2830]

Rice Heading date Hd1 186 F2s 0.5 [31,33]





Rice P uptake Pup1 98 BILs 1.0 [34,35]

Rice Grain weight gw3.1 258 BC2F2s !1.6 [36,37]

Potato Sugar content inv/GE 146 F1s !3.0 [38,39]a

Abbreviations: BC1, backcross 1; BC2F2, selfed backcross 2; BIL, backcross inbred lines; DH, double haploids;RICL, recombinant inbred chromosome lines; RIL, recombinantinbred lines. Note, because potato is inbreeding, an F 1 is a segregating population.bPosition based on mean position of multiple traits or trait screens.

Opinion TRENDS in Plant Science Vol.11 No.5 May 2006 215


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AcknowledgementsThe data presented in Figure 1 was gathered by Keith MacMillan in a

project funded by the BBSRC (project no. P13058).

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