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Integrating genotypic and expression data in a segregating mouse population to identify 5-lipoxygenase as a susceptibility gene for obesity and bone traits Margarete Mehrabian1, Hooman Allayee2, Jirina Stockton1, Pek Yee Lum3, Thomas A Drake4, Lawrence W Castellani1, Michael Suh1, Christopher Armour3, Stephen Edwards3, John Lamb3, Aldons J Lusis1,5–7 & Eric E Schadt3 Forward genetic approaches to identify genes involved in complex traits such as common human diseases have met with limited success. Fine mapping of linkage regions and validation of positional candidates are time-consuming and not always successful. Here we detail a hybrid procedure to map loci involved in complex traits that leverages the strengths of forward and reverse genetic approaches. By integrating genotypic and expression data in a segregating mouse population, we show how clusters of expression quantitative trait loci linking to regions of the genome accurately reflect the underlying perturbation to the transcriptional network induced by DNA variations in genes that control the complex traits. By matching patterns of gene expression in a segregating population with expression responses induced by single-gene perturbation experiments, we show how genes controlling clusters of expression and clinical quantitative trait loci can be mapped directly. We demonstrate the utility of this approach by identifying 5-lipoxygenase as underlying previously identified quantitative trait loci in an F2 cross between strains C57BL/6J and DBA/2J and showing that it has pleiotropic effects on body fat, lipid levels and bone density.
One of the primary goals of forward and reverse genetic approaches is to uncover genes that control biological traits of interest. In forward genetic approaches, human or animal populations are studied to identify genomic regions that cosegregate with a traits of interest. Identified regions are finely mapped by genotyping additional markers in larger populations until, ideally, the region is narrowed to a single gene. On the other hand, reverse genetic approaches characterize a gene functionally by perturbing it in an experimental system, thus elucidating its effect on the traits of interest. Forward genetic approaches have been successful for identifying genes underlying mendelian traits, but less so for analyzing common human diseases involving multiple genes and interactions with environmental factors. Reverse genetic approaches provide a straightforward way to assess gene function, but such investigations typically take place in an oversimplified context where interactions between a gene of interest and genetic background are eliminated and where compensatory changes that occur through development can confound the interpretation of results. Furthermore, functional consequences of changes in gene activity are often asymmetric. For example, knockout mice lacking glutathione peroxidase 1 have no obesity- or diabetes-
associated phenotype compared with controls, whereas transgenic mice overexpressing glutathione peroxidase 1 do1. Several groups recently proposed a combined genetics–gene expression approach to elucidate the genetics of complex traits2–12. Molecular profiling of traits that are genetically controlled by loci that also control complex traits (e.g., disease) and that are significantly correlated with such traits can begin to elucidate the portion of the transcriptional network that underlies the phenotypic trait. Patterns of gene expression can be used to infer gene function, given the coregulation of genes of unknown function with genes of known function13–17. By matching patterns of gene expression in a segregating population with the expression signature in single-gene perturbation experiments, we show how genes responsible for clusters of expression and clinically relevant quantitative trait loci (QTLs) can be mapped directly. To illustrate this approach, we focus on mapping a gene underlying QTLs underlying various traits related to cardiovascular and metabolic diseases in a previously reported BXD intercross9,18,19. Specifically, we identified a locus on mouse chromosome 6 with pleiotropic effects on adiposity, plasma lipoprotein levels and bone density18. Using an
1Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California 90095-1679, USA. 2Department of Preventive Medicine and Institute for Genetic Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California 90089-9075, USA. 3Rosetta Inpharmatics, 401 Terry Ave. North, Seattle, Washington 98109, USA. 4Departments of Pathology and Laboratory Medicine, 5Microbiology, Immunology and Molecular Genetics and 6Human Genetics and 7Molecular Biology Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California 90095-1679, USA. Correspondence should be addressed to E.E.S. (
[email protected]).
Received 15 February; accepted 21 June; published online 2 October 2005; doi:10.1038/ng1619
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integrative genomics approach involving sequence comparisons, the genetics of gene expression, pattern matching between single-gene perturbation experiments and expression QTL (eQTL) data, and the clinical characterization of mouse models, we show that the pleiotropic metabolic effects of the chromosome 6 locus in the BXD cross can be attributed, at least in part, to the gene encoding 5-lipoxygenase (5-LO; Alox5). RESULTS Examination of trait and genetic variation in the BXD cross In the BXD cross, female F2 mice homozygous with respect to the DBA/2J (DBA) allele at the Alox5 locus had fat mass, leptin levels, bone density and very-low-density/low-density lipoprotein (VLDL/ LDL) levels compared with mice homozygous with respect to the C57BL/6J (B6) allele (Table 1), demonstrating the underlying genetic basis for linkage to this locus on chromosome 6 (Supplementary Fig. 1 online). Because precise positioning of QTLs in a moderately sized cross such as ours is problematic20, the interval supporting the linkage is large, extending from 85 Mb to 125 Mb on chromosome 6, and contains 331 known genes21. We treated gene expression levels from livers of BXD mice as quantitative traits in a standard QTL analysis, as described previously9. Of the 23,574 genes on the BXD microarrays used in this analysis, 20,107 genes had eQTLs dispersed throughout the genome with log lod scores 42 (P E 0.01). Notably, 1,991 (nearly 10%) of these genes had eQTLs over the 40-Mb linkage region on chromosome 6 described above (Supplementary Table 1 online), whereas only 236 (B1%) would have been expected by chance. This large number of eQTLs at this location, even after normalizing for the number of genes in the region, is extremely significant (P o 1.0 10–16), suggesting that this locus is a hot spot for gene expression activity, as others have noted12. We excluded the possibility that this eQTL cluster was a response to the obesity state by constructing a diet-induced obesity (DIO) gene expression signature and showing that the 1,991 genes linked to the chromosome 6 locus were not enriched for genes in this DIO signature (Supplementary Methods online). The genes linked to chromosome 6 were enriched for genes correlated with the metabolic phenotypes that also linked to this locus (e.g., the omental fat mass (OFM) trait; Fig. 1). With respect to the 23,574 genes represented on the microarray, 28% had expression levels correlated with OFM at the 0.05 significance level (Fig. 1b). To estimate empirically the null distribution for these correlations, we permuted the set of 23,574 genes 100 times so that the correlation structure among these genes was preserved. The false discovery rate for
correlation between the permuted genes and OFM was 9% (Fig. 1a), indicating that the liver transcriptional network in this case was associated with fat mass traits. Of the 1,991 genes with eQTLs over the chromosome 6 locus, however, 1,177 (59%) were correlated with OFM (Fig. 1c), a significant increase compared with the 28% detected over the entire set of genes (Fisher’s exact test, P o 1.0 10–16). Using the Gene Ontology Biological Process categories, we next tested whether there was enrichment in the set of 1,991 genes linked to the chromosome 6 locus for biological pathways known to be associated with obesity-related traits. Table 2 lists the top 11 pathways over-represented in the set of 1,991 genes. Two of these pathways were fatty acid and amino acid metabolism, processes that are central to energy storage and expenditure. These results represent a degree of pathway coherence that directly implicates a substantial subset of the 1,991 genes linking to chromosome 6 in obesity-related processes. Refining positional candidates in the chromosome 6 locus The cluster of eQTL and clinical QTL (cQTL) activity on chromosome 6 suggests that one or more DNA differences in this region between B6 and DBA are the ultimate cause of the QTLs. Because B6 and DBA shared a common ancestor, they will share regions of the genome that are largely identical by descent (IBD) or, more precisely, identical subspecies by descent. Given the availability of B6 and DBA genomic sequences22,23, it is possible to define precisely the IBD regions between B6 and DBA by examining SNP frequencies. Experimental support that low-frequency SNP regions between these two strains are IBD comes from a recent report showing that nearly 97% of genes with cis-acting eQTLs detected in the BXD cross fell in non-IBD regions between B6 and DBA (P r 10–300 for enrichment)24. This observation supports the ideas that most common variation between strains originates in non-IBD regions and that the SNP activity observed in IBD regions may only rarely give rise to trait variation. We determined the IBD status of the 40-Mb interval on chromosome 6 using previously described methods24 (Fig. 2). Of the 331 known genes in this interval, 172 fell in non-IBD regions and 159 were in IBD regions. Genes falling in IBD regions are not considered strong positional candidates for the QTLs. For example, peroxisome proliferator activated receptor gamma (Pparg) falls in an IBD region (Fig. 2). This transcription factor is involved in adipocyte differentiation and insulin sensitivity and is the target of pharmaceutical agonists, such as rosiglitazone, that are used to treat type 2 diabetes. Without knowledge of the IBD status, this gene would have been favored as a positional candidate for the BXD metabolic trait QTLs.
Table 1 Comparison of clinical traits between BXD F2 and Alox5–/– mice BXD F2 cross DBA homozygotes Trait
Alox5 mice Alox5–/–
B6 homozygotes
Alox5+/+
Mean
N
Mean
N
P
Mean
N
Mean
N
P
Total fat pad mass (g) Omental fat pad mass (g)
3.90 ± 0.50* 0.40 ± 0.04*
26 26
1.59 ± 0.32* 0.21 ± 0.04*
19 18
0.000036 0.00057
3.29 ± 0.59* 0.44 ± 0.06*
5 5
0.86 ± 0.12* 0.19 ± 0.02*
7 7
0.0001 0.0001
Plasma leptin levels (pg/ml)/percent body fat Bone mineral density (mg/ccm)
12.5/0.6 300 ± 7.2*
26 39
9.0/0.8 275 ± 7.5*
19 36
0.0021 0.0021
50.1/10.3 580 ± 7.1*
5 5
11.0/8.7 560 ± 6.5*
7 7
0.007 0.03
VLDL/LDL cholesterol (mg/dL) HDL cholesterol (mg/dL)
13.3 ± 1.1* 63.3 ± 1.5*
37 30
10.6 ± 0.9* 54.8 ± 1.5*
35 34
0.033 0.0005
39 ± 2.5* 86.8 ± 4.5*
5 5
33 ± 1.9* 64.7 ± 2.1*
7 7
0.008 0.0006
*Mean ± s.e.m. All mice phenotyped were female.
P values represent the significance of the t statistic under the null hypothesis that the difference in mean trait values between the two groups is equal to 0.
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a
b 5,000
3,000
c
9%
28%
d
250
59%
60
58%
4,000 200
2,000
150
2,000
1,000
100
1,000
500 0 0.2
0.4
0.6
0.8
Absolute value of the correlation for permuted data sets
20
50
0 0.0
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0 0.0
0.2
0.4
0.6
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0.0
0.2
0.4
0.6
0.8
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0.4
0.6
0.8
Absolute value of the correlation for Alox5 –/– signature genes
e
Figure 1 Histograms of correlation coefficients computed between OFM and gene expression levels in five different gene sets. The Pearson correlation coefficient was computed between OFM and every gene expression trait in the set of 23,574 genes on the BXD microarray. After taking the absolute value of the correlation coefficients computed for each set, the histograms were plotted with the number of genes on the y axis and the correlation coefficients on the x axis. Highlighted in each plot is the percentage of genes in the given set that had significant correlation coefficients at the 0.05 level. The different distributions represented in each panel were obtained from (a) the permutation procedure (represents the null distribution); (b) the set of 23,574 gene expression traits represented in the BXD data set; (c) the set of 1,991 genes whose eQTLs give lod scores 42 and map to the 18-cM Alox5 interval; (d) the set of 444 genes in the Alox5–/– liver expression signature; and (e) the set of 104 genes from the Alox5–/– liver expression signature whose eQTLs give lod scores 42 in the BXD cross and map to the 18-cM Alox5 interval.
Using DNA sequence data from multiple sources22,23, we estimated that 82.2% of the B6 sequence in the chromosome 6 locus was supported by DBA sequence and that 90.7% of all SNPs identified in transcribed regions of protein-coding genes (coding sequence, putative untranslated regions and acceptor and donor sites) were similarly supported by DBA sequence (Supplementary Methods). Of the 172 genes identified as falling in non-IBD regions, 154 (90%) were represented on the microarray. This set of 154 candidate genes can be further narrowed by requiring the gene to be expressed, to be correlated with the metabolic traits of interest and to have cis-acting eQTL behavior in the BXD cross, as previously proposed by others2,5,7–11,25,26. Of the 154 genes, 44 were expressed and gave rise to cis-acting eQTLs with lod scores 42.0. These 44 genes represent the highest-confidence set of candidate gene expression traits for the metabolic phenotypes in this data set, where variations in the gene itself could mediate changes in transcript abundances, which in turn mediate changes in the metabolic traits. Of these 44 candidate genes, only 3 were correlated with the metabolic phenotypes at the 0.01 significance level: Ogg1, Anxa4 and Anubl1. Ogg1 and Anxa4 are supported by two strong cis-acting eQTLs in the chromosome 6 locus (lod scores of 37.4 and 27.4, respectively). But the genetic and gene expression data strongly support the idea that the correlation between the metabolic traits and expression traits for these two genes is an artifact of two closely linked QTLs being in gametic phase disequilibrium, as discussed previously24 and supported by application of a statistical test to assess such relationships26. The third gene, Anubl1, had transcript abundances that were not correlated with the chromosome 6 QTL genotypes, conditional on the metabolic phenotypes, and the correlation between the metabolic phenotypes and QTL genotypes conditional on Anubl1 expression levels was markedly different from zero, supporting the possibility that Anubl1 was reactive to the metabolic phenotypes26. Therefore, application of a QTLmapping strategy2,8–12 did not identify any causal candidate genes with liver expression associated with cis-acting eQTLs in the chromosome 6 locus. Failure to identify genes using this approach could be due to a number of factors, including polymorphisms in the gene(s) underlying the QTL that affect protein function but not expression
0.0
Absolute value of the correlation for genes linked to Alox5 locus 20
84% 15 10 5 0 0.0 0.2 0.4 0.6 0.8 Absolute value of the correlation for –/– Alox5 genes lined to Alox5 locus
and polymorphisms in the relevant genes that affect expression but not in liver tissue. Therefore, we focused on the 32 genes located in non-IBD regions of the chromosome 6 locus that had SNPs leading to codon changes. We searched for SNPs in these 32 genes known to result in amino acid changes and found that a missense mutation in Alox5 altered enzymatic activity (Supplementary Fig. 2 online). Alox5 is also physically centered at the peak of the joint lod score curve for the composite trait18, making this gene a good positional candidate (Supplementary Fig. 1). In total, 66 SNPs were polymorphic between B6 and DBA in the 51-kb region encompassing Alox5, including 63 intronic SNPs, 1 missense mutation and 2 SNPs in the 3¢ untranslated region. The single Alox5 missense mutation in DBA is identical to a V646I substitution that was also identified in strain CAST/Ei27 and, in recent studies, markedly decreased levels and activity of 5-LO28. This suggests that DBA is similar to CAST/Ei (and Alox5–/– mice) in this phenotypic regard. Our search did not identify any other SNPs with known effects on protein function in the other 31 genes. Associating Alox5 and metabolic traits by pattern matching By profiling livers from Alox5 knockout (Alox5–/–) mice, we tested whether perturbations of the transcriptional network induced by complete inactivation of Alox5 overlapped with expression traits linked to the chromosome 6 locus. Overlap of these patterns of expression would support the possibility that Alox5 is a susceptibility gene for the metabolic traits (Fig. 3). We identified 444 genes whose expression was different between Alox5–/– and control B6 mice (Supplementary Table 2 online). Of these 444 genes, 104 (23.4%) had eQTLs directly over Alox5 in the BXD cross (Fig. 2 and Supplementary Table 3 online). Because only 44 (B10%) genes in the Alox5–/– signature would be expected to give eQTLs over Alox5 by chance, the B2.5-fold enrichment is highly significant (P ¼ 3.3 10–17). Next we examined the correlation of OFM with the 444 genes in the Alox5–/– signature and found that 58% were correlated (Fig. 1d), a considerable increase over the 28% expected by chance. Of the 104 genes that fell in the Alox5–/– signature set and were linked to Alox5 in the BXD cross, however, 84% (87 genes) were correlated with OFM (Fig. 1e). Overall, only 1,177 (5.9%) of the genes in the BXD
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BXD F2 cross Bonferroni-
© 2005 Nature Publishing Group http://www.nature.com/naturegenetics
Category
Rank
Rosiglitazone-treated mice Bonferroni-
P
corrected P
Rank
P
corrected P
BonferroniRank
P
corrected P
Electron transport
1
3.04 10–12
5.56 10–9
1
o10–15
o10–15
4
o10–15
o10–15
Amino acid metabolism Amine metabolism
2 3
1.91 10–11 5.70 10–11
3.48 10–8 1.04 10–7
3 5
o10–15 4.08 10–11
o10–15 2.40 10–8
18 7
1.50 10–6 2.24 10–9
0.0028 4.21 10–6
Amino acid and derivative metabolism Energy derivation by oxidation of
4 5
7.22 10–11 7.51 10–11
1.32 10–7 1.37 10–7
2 20
o10–15 5.28 10–7
o10–15 0.00097
15 17
9.18 10–7 1.49 10–6
0.0017 0.0028
Fatty acid metabolism Energy pathways
6 7
8.04 10–11 8.76 10–11
1.47 10–7 1.6 10–7
4 17
1.31 10–11 1.71 10–7
2.40 10–8 0.00031
2 5
1.41 10–11 2.56 10–10
2.65 10–8 4.80 10–7
Main pathways of carbohydrate metabolism Antiapoptosis
8 9
9.80 10–11 1.03 10–8
1.79 10–7 1.87 10–5
25 10
7.36 10–6 6.14 10–9
0.013 1.12 10–5
9 NS
6.47 10–8 NA
0.00012 NA
Negative regulation of apoptosis Negative regulation of programmed cell death
10 11
2.57 10–8 2.79 10–8
4.70 10–5 5.10 10–5
8 9
2.01 10–9 2.08 10–9
3.68 10–6 3.80 10–6
NS NS
NA NA
NA NA
Amine catabolism Amino acid catabolism
NS NS
NA NA
NA NA
6 7
8.75 10–10 1.89 10–9
1.60 10–6 3.46 10–6
NS NS
NA NA
NA NA
Cofactor metabolism Fatty acid b-oxidation
29 NS
7.34 10–6 NA
0.013 NA
11 NS
7.65 10–9 NA
3.46 10–6 NA
11 1
1.47 10–7 o10–15
0.00028 o10–15
Fatty acid oxidation Lipid biosynthesis
NS 17
NA 2.21 10–6
NA 0.0041
NS NS
NA NA
NA NA
3 6
4.43 10–11 1.49 10–9
8.29 10–8 2.68 10–6
Alcohol metabolism VLC fatty acid metabolism
13 NS
4.05 10–7 NA
0.00076 NA
24 NS
2.50 10–6 NA
0.0047 NA
8 10
2.62 10–8 1.40 10–7
4.92 10–5 0.00026
organic compounds
Of the 11 most significant Gene Ontology Biological Process categories represented in the set of genes with eQTLs that link to Alox5 in the BXD cross, 8 are represented from the expression signature of Alox5–/– mice and 5 from that of the rosiglitazone-treated mice. P values represent the significance of the Fisher’s exact test statistic under the null hypothesis that the frequency of Gene Ontology Biological Process genes for the functional category indicated is the same between a reference set of 30,102 genes and the set of genes comprising the perturbation signature. NA, not applicable; NS, not significant.
data set were linked to the chromosome 6 locus and correlated with OFM, compared with 87 (19.6%) genes in the Alox5–/– signature (Fig. 2). This nearly fourfold enrichment is highly significant (P ¼ 7.3 10–24). We obtained similar results for total fat mass, VLDL/LDL cholesterol, leptin levels and measures of bone density (data not shown). These results provide compelling evidence that Alox5 is one of the genes underlying the metabolic QTLs in the BXD cross. We further tested whether the same level of pathway coherence observed in the set of genes linked to the chromosome 6 locus was also present in the Alox5–/– signature. Table 2 lists the top 11 Gene Ontology Biological Process categories over-represented in the set of genes in the Alox5–/– signature, of which 8 are the same as those over-represented in the chromosome 6 eQTL cluster. Therefore, pathways affected by complete inactivation of 5-LO are the same pathways affected by the ‘soft’ perturbation induced by the chromosome 6 QTL, which includes a mutation in Alox5 that decreases enzymatic activity. 129 sequence in Alox5–/– mice cannot explain the QTL effects Alox5–/– mice were originally generated using 129 embryonic stem cells injected into B6 blastocysts, resulting in B6/129 chimeric mice that were then backcrossed to B6 for more than 15 generations. We identified B14 Mb of 129 genomic sequence flanking the Alox5 locus in Alox5–/– mice (Supplementary Fig. 3 online). Part of this 129 sequence was also IBD with the DBA sequence (Supplementary Fig. 3), potentially confounding interpretation of the phenotypic and gene expression similarities between Alox5–/–and BXD mice with respect to the Alox5 locus. For such confusion to occur, three conditions must hold at a given locus of interest: (i) 129 and DBA must be IBD; (ii) 129 and B6 must not be IBD; and (iii) B6 and DBA
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must not be IBD. Less than 3.3 Mb of the 14-Mb 129 sequence flanking the Alox5 locus met all three conditions (Supplementary Fig. 3). Of the 331 genes in the 40-Mb chromosome 6 interval, 32 overlapped the 129 regions (Supplementary Fig. 3). None of the corresponding gene expression traits in this set gave rise to cis-acting eQTLs that correlated with the metabolic traits in BXD mice. Of the eight genes in this set that carry SNPs leading to amino acid changes, only those in Alox5 are known to lead to changes in protein activity. This bioinformatic analysis cannot absolutely exclude the possibility that genes falling in the 129 regions flanking the Alox5 locus contribute to the Alox5–/– liver expression signature or the chromosome 6 eQTL hot spot. To confirm that Alox5 is at least partially responsible for the Alox5–/– expression signature and chromosome 6 QTL, we artificially perturbed the 5-LO pathway in primary human monocytes (Supplementary Methods). 5-LO catalyzes one of the initial steps in the production of leukotrienes from arachidonic acid. LTB4, a powerful chemoattractant, is one of the primary products of this 5-LO–catalyzed reaction29. Therefore, we cultured primary human monocytes in medium containing LTB4 to perturb the transcriptional network specific to the 5-LO pathway. Genes whose expression was significantly different between perturbed and control cells comprised the LTB4 expression signature. These genes were significantly enriched in the Alox5–/– liver gene expression signature (P ¼ 0.0000078) and in the set of genes with liver expression values in BXD mice linked to the Alox5 locus and correlated with the metabolic traits (P ¼ 0.00011). This overlap confirms that the Alox5–/– liver gene expression signature and the set of genes linked to the chromosome 6 locus in BXD mice are an important component of the 5-LO pathway that is associated with the metabolic traits.
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Pparg (2 SNPs)
SNP count (smoothed 25-kb windows)
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Alox5 (66 SNPs)
40 20 0 116.0
116.5
117.0
117.5 118.0 118.5 Chromosome 6 (116–120 Mb)
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80 60 40 20 0 80
85
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105
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130
135
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Chromosome 6 (85–125 Mb)
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444 genes in the Alox5 signature
104 (87) genes overlapping enrichment P = 3.3 × 10–17 (7.3 × 10–24) 56 genes overlapping enrichment P = 0.68
595 genes in the rosiglitazone signature
Figure 2 Enrichment of genes linked to the Alox5 locus in the BXD set for genes in the Alox5–/– perturbation signature. The upper panels represent a frequency plot in the genomic region supporting the Alox5 locus for SNPs that are polymorphic between B6 and DBA. The horizontal line represents a threshold of five SNPs per 25-kb region. Regions with five or more SNPs in a 25-kb interval were designated IBD between B6 and DBA, as previously described24. Highlighted in the upper panel are Pparg and Alox5 residing in IBD and non-IBD regions, respectively. Shown in the lower panel are overlaps between genes linked to the Alox5 locus and the Alox5 and Pparg perturbation signatures. Of the 20,107 genes with eQTLs with lod scores 42, 1,991 (9.9%) were in an 18-cM window encompassing Alox5. Restricting attention to the 444 genes from the Alox5–/– signature, 104 (23.1%) genes have eQTLs with lod scores 42.0 in the 18-cM interval. The Alox5–/– signature is more enriched for genes linked to the Alox5 locus in the BXD set and correlated with OFM (shown in parentheses). Of the 20,107 genes with eQTLs with lod scores 42.0, 1,177 (5.9%) are correlated with OFM at the 0.05 significance level and map to the 18-cM Alox5 interval. Restricting attention to the 444 genes from the Alox5–/– signature, 87 genes (19.6%) have eQTLs with lod scores 42.0 that map to the interval and correlate with OFM at the 0.05 significance level.
Chromosome 6 eQTLs not enriched for Pparg-responsive genes Pparg is transcribed in diverse tissues and was expressed in B6 and DBA livers (Supplementary Fig. 4 online). Pparg expression in liver was approximately twofold higher in DBA mice than in B6 mice. Given previous eQTL results on the BXD cross9, Pparg has a complicated eQTL signature, consisting of four eQTLs with lod scores Z3.0 and nine eQTLs with lod scores Z1.5, with the most significant eQTL mapping to chromosome 10 at 24 cM with a lod score of 4.6. Despite this strong eQTL signature for Pparg, none of the variation in Pparg expression in the BXD cross can be explained by the chromosome 6 locus, indicating that the expression variation observed in this cross can be attributed to trans-acting genetic factors and other nongenetic factors. We also note there are only two informative SNPs (both in introns) between B6 and DBA in the 130,212-bp genomic sequence containing the Pparg gene in the Celera Mouse Genome Database. Between a full-length cDNA clone for the adiposespecific isoform of Pparg derived from DBA and the complete coverage of the two most 5¢ exons of the liver-specific isoform of Pparg in DBA sequence in the Celera Mouse Genome Database, there is 100% coverage of the coding sequence of Pparg by DBA sequence,
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which perfectly matches the corresponding B6 sequence (from build 33 of the National Center for Biotechnology Information mouse genome assembly). To assess further whether Pparg could partially explain this linkage of the metabolic phenotypes to chromosome 6, we identified liverspecific gene expression signatures in three independent Pparg perturbation experiments: wild-type B6 mice treated with rosiglitazone (595 genes; signature set E1), Ppara–/– B6 mice treated with rosiglitazone (457 genes; signature set E2) and Ppara-null B6 mice overexpressing Pparg in the liver30 (313 genes; signature set E3; Supplementary Table 4 online). Genes in set E1 significantly overlapped genes in set E2 (P ¼ 4.97 10–90), indicating that a robust, Pparg-specific expression signature in the liver was induced by treatment with rosiglitazone. In addition, both signature sets E1 and E2 significantly overlapped set E3 (P ¼ 3.60 10–21 and 1.04 10–18, respectively). Taken together, these data indicate that a robust Pparg-specific signature is induced in the livers of mice treated with rosiglitazone. Despite overlaps among the three independent Pparg perturbation signatures, none of these signatures was enriched for genes with
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Identify genes with eQTL in linkage region
AACGC GTT
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AAC A GTT
a
Profile tissue
D6Mit200
X
D6Mit198 D6Mit25 Gene G1 D6Mit44 D6Mit149
F1 D6Mit102
D6Mit16
Map perturbation signature to eQTL signature and assess significance of overlap
D6Mit50
b
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Obtain knockout for gene of interest
G1
Identify perturbation signature
Profile tissue
Obtain wild-type mouse for reference
G1
Figure 3 Intersecting perturbation signatures in gene expression data to map genes for complex traits. (a) QTLs for complex traits in an F2 population are mapped. A genetic locus at G1 is highlighted as linked to a trait of interest in the F2 population. A SNP in G1 is highlighted as the causal variant underlying the complex trait: the green allele leads to decreased G1 activity, whereas the red allele leads to increased G1 activity. Tissues relevant to the complex trait are monitored using microarrays. Expression traits found to be genetically linked to the G1 locus are determined. The network to the left highlights expression traits linked to the G1 locus (blue nodes), in addition to expression traits interacting with genes linked to the G1 locus (white nodes), with G1 denoted by the red node. (b) Tissues from mice genetically modified with respect to G1 are profiled. Genes that are differentially regulated between the perturbed and unperturbed system are identified. Highlighted to the left is the portion of the transcriptional network that is observed to change when gene G1 is perturbed (knocked out, in this case). This perturbation signature is then compared with the eQTL signature defined in a. If expression traits controlled by the G1 locus are enriched for expression traits that are differentially regulated as described in b (blue nodes), then this matched pattern of expression provides direct experimental support that G1 is the gene underlying the linkage to the complex trait in the F2 population.
eQTLs that linked to the Alox5 locus. For example, using the same type of analysis described for the Alox5–/– expression signature, only 56 of the 595 genes represented in signature set E1 had expression values linked to the chromosome 6 locus, which is not significantly different from the 59 expected by chance (Fisher’s exact test, P value ¼ 0.68). The P values for enrichment for signature sets E2 and E3 were 0.67 and 0.99, respectively. These data, combined with the fact that Pparg resides in an IBD region of chromosome 6, do not support the idea that Pparg is the gene underlying the chromosome 6 QTLs in the BXD cross. Characterization of Alox5–/– mice for QTL traits in the BXD cross The expression data provide strong evidence that Alox5, at least in part, explains the variation in the metabolic phenotypes linked to the chromosome 6 locus. If this is the case, Alox5–/– mice should have greater fat mass, bone density, leptin levels and cholesterol levels than
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wild-type mice. On a chow diet, Alox5–/– females had 32% body fat, as measured by nuclear magnetic resonance, compared with 16% for wildtype mice (Table 1). Notably, this difference was not isolated to one fat pad, as all four depots had greater mass. We observed similar differences on the HFC diet (data not shown). With respect to leptin levels, Alox5–/– mice had greater plasma levels than wild-type mice, and this difference was much greater than that observed in mice from the BXD cross (Table 1). To determine whether these differences resulted from behavioral differences, we measured daily food intake over a 6-d period. Food consumption was not different between Alox5–/– and wild-type mice (4.0 ± 0.27 g d–1 versus 4.3 ± 0.05 g d–1, respectively), and the mice had similar activity levels when observed during the light cycle. Given the QTLs for plasma lipids and bone density in the BXD cross, we phenotyped Alox5–/– mice for these traits as well. On a chow diet, Alox5–/– mice had elevated total, high-density lipoprotein (HDL) and LDL/VLDL cholesterol levels compared with wild-type mice
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Figure 4 Genetic subnetwork of genes in the Alox5–/– signature and linked to the Alox5 locus in the BXD cross. Subnetwork derived from the BXD liver gene network previously described31. The light purple nodes represent genes that are in the Alox5–/– liver signature and linked to the Alox5 locus in the BXD cross. The white nodes represent genes in the BXD liver network31 that were within a path length of 1 of the light purple nodes. Two subclusters in the subnetwork containing the genes Spp1 and Mt2, which affect bone density and obesity traits, respectively, are highlighted.
(Table 1). Similarly, both femoral bone mineral density and bone mineral content were higher in Alox5–/– mice than in wild-type mice (Table 1). For each of these traits in the Alox5–/– mice, the differences were perfectly consistent with the trends observed in the BXD cross, where F2 mice homozygous with respect to the DBA allele at the Alox5 locus had greater body fat, leptin levels, lipid levels and bone density than did B6 homozygotes (Table 1). Pparg falls in a region that is IBD between the B6 and 129 strains of mice, where there is only a single informative SNP between these strains in the 130-kb region spanning the Pparg gene. Furthermore, between a full-length Pparg cDNA sequence derived from 129S1/ SvImJ and the 129S1/SvImJ sequence data represented in the Celera Mouse Genome Database, the coding sequence of the liver-specific isoform of Pparg is completely supported by 129 sequence, and there are no sequence differences between this sequence and the B6 sequence (National Center for Biotechnology Information build 33 of the mouse genome). This observation is important, given that the Alox5–/– mouse retains a sizeable stretch of 129 sequence flanking the Alox5 gene, which includes Pparg. If Pparg activity varied between B6
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and 129 and this variation was due to the cis effects of polymorphisms in Pparg, this could potentially explain part of the phenotypic variation noted in the Alox5–/– mice, given the role of Pparg in obesity and diabetes phenotypes. Reconstructing gene networks We recently developed a new gene network reconstruction algorithm that was applied to a set of 1,088 of the most transcriptionally active genes in the BXD liver data31. The resulting network could predict causal relationships among genes in the network31. Of the 104 genes in the Alox5–/– signature and linking to the Alox5 locus in the BXD cross, 31 intersected with the set of 1,088 genes used to reconstruct the BXD liver gene network, and 25 of these 31 genes were connected to many different nodes in the network (i.e., not singleton nodes or isolated clusters). Figure 4 highlights the portion of the BXD liver gene network31 that contains these 25 genes, in addition to genes in the network within a path length of 1 of any of these 25 genes. All genes in this subnetwork are within a path length of 4 of one another. Therefore, of the 1,088 genes comprising the BXD liver gene
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ARTICLES network31, the set of 25 genes central to the Alox5–/–signature genes and linked to the Alox5 locus in the BXD cross falls in the same region of the network, and the path between any two of these genes consists of no more than four links. Several groupings of the genes in this portion of the network are highlighted in Figure 4. The first is a cluster of genes influenced by the gene osteopontin (Spp1). Spp1 knockout mice are resistant to ovariectomy-induced bone resorption, which is a model for postmenopausal osteoporosis32. The second gene cluster contains metallothionein 2 (Mt2) as a key component of the network substructure. Mt2-null mice are more sensitive to oxidative stress and have greater fat mass and plasma leptin levels than wild-type controls33. The Alox5–/– gene expression signature shows that Spp1 and Mt2 are both downregulated in Alox5–/– mice relative to wild-type controls. In agreement with these single-gene perturbation experiments, the eQTLs for Spp1 and Mt2 at the Alox5 locus in the BXD cross show downregulation of both genes in mice that are homozygous with respect to the DBA allele of Alox5 relative to mice that are homozygous with respect to the B6 allele. These data provide direct experimental evidence that perturbations to Alox5 (i.e., those reducing 5-LO activity) lead to variations in other genes that have a causal role in the same metabolic phenotypes that are associated with reduced 5-LO activity, namely bone density and obesity-related measures. Furthermore, the pattern of transcriptional downregulation of these genes induced by reducing 5-LO activity is consistent with the phenotypes achieved in the corresponding knockout mice. Zfp90, a gene we recently identified for involvement with obesity26, is also represented in the network in Figure 4. DISCUSSION Using an integrative genomics approach involving genotypic, gene expression and clinical trait data in segregating mouse populations and genetically targeted mice, we show that 5-LO influences various metabolic parameters, such as adiposity, lipoprotein levels and bone density. Multiple lines of experimental evidence support these conclusions. First, QTLs for these traits map to Alox5 in a cross between B6 and DBA. Second, the Alox5 locus is enriched for eQTLs with corresponding expression levels that are correlated and enriched for pathways associated with the metabolic traits. Third, the DBA allele of the Alox5 gene carries a mutation that reduces enzymatic activity. Fourth, there is enrichment and overlap between the liver gene expression signature of Alox5–/– mice with those genes that have eQTLs over Alox5 in the BXD cross. Fifth, genes in the Alox5–/– liver signature that link to the Alox5 locus in the BXD cross were correlated and enriched for pathways associated with the metabolic traits. Sixth, Alox5–/– mice have the same differences in QTL traits as F2 mice homozygous with respect to the DBA allele of Alox5, which substantially decreases 5-LO levels and activity. Seventh, an independent 5-LO perturbation in human monocytes produced an expression signature that overlapped with the Alox5–/– liver gene expression signature and the set of genes linked to the chromosome 6 locus, indicating that changes in 5-LO activity and not allelic differences between B6 and 129 accounted for the overlap between the Alox5–/– liver gene expression signature and the set of genes linked to the chromosome 6 locus. Eighth, there was no comparative significant enrichment in the expression signature obtained by overexpression and pharmacological manipulation of Pparg, a favored positional candidate gene that influences metabolic processes including adiposity, insulin resistance and bone density34. Finally, genetic networks constructed from the genes in the Alox5 signature that also link to the Alox5 locus in the BXD cross involve those known to affect the metabolic traits of
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interest. Taken together, these data provide compelling evidence that Alox5 and its role in inflammatory processes influence a variety of physiological pathways related to human metabolic disorders. Previous studies used microarray approaches in mice and humans to identify gene expression patterns that redefine a complex disease trait, to identify subtypes of a given disease, to elucidate the complex genetic networks of causal and reactive expression changes and to enhance the ability to identify the key drivers of disease9,31,35,36. In this study, we proposed an important variation to this approach. Given that patterns of expression underlie biological processes associated with complex traits13–17,37, we propose mapping susceptibility genes by comparing the perturbed transcriptional network in segregating populations with that in single-gene perturbation experiments of positional candidate genes (Fig. 3). This method addresses the weaknesses of the forward and reverse genetic approaches by leveraging their complementary strengths. Specifically, the segregating population is used to study the complex traits that manifest themselves in this setting and that are associated with complex networks of naturally occurring variation. Single-gene perturbation experiments are used to define the effects on the transcriptional network, where achieving physiological relevance is not required, but instead the perturbation signature is mapped back into the segregating population and interpreted in the context where the complex traits had initially been observed. Our results provide an example for this paradigm by identifying Alox5 as controlling various metabolic phenotypes. The most plausible explanation for this conclusion is that variations in Alox5 in the BXD cross gives rise to similar transcriptional network perturbations as that associated with 5-LO deficiency. Because strain DBA has an amino acid substitution in Alox5 that decreases enzyme levels and activity, the results of the liver microarray experiments in the Alox5–/– and BXD mice are entirely consistent. Therefore, even without directly observing changes in 5-LO expression in the BXD cross (a valid probe for Alox5 was not present on the microarray), the expression signature that results from perturbations to this gene is enough to map Alox5 as at least one of the genes underlying the expression signatures and clinical traits linked to the Alox5 locus. Supplementary Figure 4 shows that Alox5 is expressed in a number of tissues and is expressed in liver and adipose tissues in the B6 strain (similar expression patterns were observed in DBA; data not shown). At the same time, we were able to exclude Pparg as a strong causal candidate in this particular cross, given that this gene is IBD between B6 and DBA and that multiple, independent direct perturbations of this gene did not result in an expression response that overlapped with the set of genes controlled by the Alox5 locus in the BXD cross. Although the gold standard for mapping genes for QTLs remains the construction of genetically modified animals carrying the strain-specific allele associated with the trait of interest, this time-consuming method can be potentially circumvented by the more rapid matching of perturbations signatures with eQTL or cQTL hot spots. Furthermore, the construction of BAC transgenics or other modified animals carrying the strain-specific allele causing the clinical traits is still not definitive, given that the strain-specific allele may cause regulatory changes in flanking genes as opposed to changes in the gene in which the mutation occurs. With the method presented here, the connection to the putative causal gene is potentially stronger, given that the connection is based on hundreds or even thousands of molecular traits that underlie the clinical trait of interest and that vary with perturbations to the putative causal gene. We recently identified C3ar1 as a causal gene for OFM in the BXD cross used here to map Alox5 (ref. 26). Using the approach described herein, however, C3ar1 was not identified as a fat mass susceptibility gene in the chromosome 6 linkage region for several reasons. First, as
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ARTICLES with Pparg, C3ar1 falls in a region that is IBD between the B6 and DBA strains, with no polymorphic SNPs identified in the Celera Mouse Genome Database between these two strains. In addition, a genomic sequence derived from 129/SvJ containing the entire C3ar1 coding sequence matched the B6 genomic sequence 100%. Given that 129/SvJ is predicted to carry the same haplotype as B6 and DBA in this region, we can conclude there are no sequence variants in this gene between B6 and DBA. Second, C3ar1 is slightly more than 7 Mb distal to Alox5 and is not supported in the 99% confidence interval for the chromosome 6 fat mass linkage. Finally, we detected C3ar1 as causal in the BXD cross because the liver expression of this gene was controlled by multiple genetic loci (trans-acting eQTL), where DNA variants in other genes lead to changes in expression of C3ar1, and where we predicted and later validated that changes in C3ar1 expression lead to changes in fat mass. The power of this approach to prioritize and identify QTL genes in a more direct fashion can be extrapolated to other complex diseases. The data and methods presented here also suggest that screening of positional candidate genes using more accessible cell-based systems, such as RNA interference, may lead to accelerated identification of QTL genes. While investigators might still need to construct a congenic, knockout or transgenic animal to validate the phenotypic association directly, this approach could greatly accelerate identification of the primary drivers of disease beyond what has been achieved to date. In addition, these data fit very naturally into methods currently under development that integrate genotypic, expression and clinical data to reconstruct more reliable genetic networks underlying complex disease traits26,31. METHODS Animal models. We bred Alox5–/– mice on a B6 background in-house from homozygous parental breeders, which were backcrossed to B6 for more than ten generations. We either bred control B6 mice in-house or purchased them from the Jackson Laboratories. We housed mice four to a cage at 25 1C on a 10-h dark/14-h light cycle and maintained them on either a chow diet (Purina) or a HFC containing 15% fat, 1.25% cholesterol and 0.5% cholic acid (Harlan-Teklad). The Alox5–/– mice used in the experiments were of both sexes and age-matched between 4 and 7 months of age. We housed Ppara–/– mice on a B6 background and wild-type B6 mice used in the rosiglitazone treatment and DIO experiments in microisolater cages (Labproducts) on a 12-h light/12-h dark cycle. To establish an obesity expression signature in liver, we housed 20 8-week-old male B6 mice individually and divided them into lean and DIO groups. Lean mice were fed regular mouse chow (13% kcal from fat, 3.41 kcal g–1; Harlan Teklad). We maintained DIO mice for 6 weeks on a high fat diet (59.4% kcal from fat, 24.5% kcal from carbohydrate, 16.2% kcal from protein, 5.29 kcal g–1; BioServ). We collected liver tissues for gene expression profiling after the 6-week high-fat feeding cycle. The BXD F2 mouse population and associated liver gene expression data used in this study were previously described9,18. An F2 population consisting of 111 mice was constructed from two inbred strains of mice, B6 and DBA. Only female mice were maintained in this population. Mice were on a rodent chow diet up to 12 months of age and then switched to an atherogenic high-fat, high-cholesterol diet for another 4 months. At 16 months of age, the mice were killed and their livers extracted for gene expression profiling. The mice were genotyped for 139 microsatellite markers uniformly distributed over the mouse genome to allow for the genetic mapping of the gene expression and disease traits. All procedures were done in accordance with the current National Research Council Guide for the Care and Use of Laboratory Animals and were approved by the University of California Los Angeles Animal Research Committee (for the BXD and Alox5–/– mice) and by the Merck Research LaboratoriesRahway Institutional Animal Care and Use (for the rosiglitazone-treated and DIO mice).
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Treatment of mice with rosiglitazone. We treated male B6 mice (n ¼ 9 per treatment group, 9–11 weeks old) daily with either rosiglitazone (100 mg per kg body weight) or vehicle (0.25% methylcellulose) by oral gavage for 7 d. We fed mice standard rodent chow (Harlan Teklad) for at least 1 week before study initiation and weighed them daily during treatment. Mice were killed 6 h after the last treatment, and livers were removed for RNA isolation and microarray analysis. We used the same experimental paradigm for male Ppara–/– mice (n ¼ 4 per treatment group) treated with either rosiglitazone (100 mg per kg body weight) or vehicle (0.25% methylcellulose) by oral gavage for 7 d. Probe selection for mouse gene expression arrays. The mouse microarray used for the BXD cross was previously described9. The mouse microarray used here is an updated version, containing 23,574 noncontrol oligonucleotide probes for mouse genes and 2,186 control oligos. We extracted full-length mouse sequences from Unigene clusters (build 168, February 2004) and combined them with RefSeq mouse sequences from Release 3 (January 2004) and RIKEN full-length sequences (version fantom1.01). We clustered this collection of full-length sequences and selected one representative sequence per cluster. To complete the array, we selected 3¢ expressed-sequence tags from Unigene clusters that did not cluster with any full-length sequence from Unigene, RefSeq or RIKEN. To select a probe for each gene sequence, we used a series of filtering steps, taking into account repeat sequences, binding energies, base composition, distance from the 3¢ end, sequence complexity and potential cross-hybridization interactions38. For each gene, every potential 60-bp sequence was examined and the 60-bp oligonucleotide that best satisfied the criteria was printed on the microarray. All microarrays used in this study were manufactured by Agilent Technologies, Inc. Preparation of labeled cDNA and hybridizations to microarrays. After killing mice, we removed livers from Alox5–/–, rosiglitazone-treated B6, control and DIO mice, immediately flash-froze them in liquid nitrogen and stored them at –80 1C. We purified total RNA from 25-mg portions using an RNeasy Mini kit in accordance with the manufacturer’s instructions (Qiagen). We prepared liver cDNA in the same fashion as for the F2 mice in BXD cross, as described previously9. We hybridized fluorescently labeled cRNA (5 mg) from each Alox5–/–, rosiglitazone-treated B6, control and DIO mouse against different pools of RNAs. There were RNA pools specific to each experiment set (the Alox5–/– set, the wild-type rosiglitazone treatment set, the Ppara–/– rosiglitazone treatment set and the DIO set) constructed from equal aliquots of RNA from the control mice in each experiment set. We constructed five fluor-reverse pairs for the Alox5–/– experiment set, with RNA from five individual Alox5–/– mice hybridized against the pool of five wild-type mice. We constructed three fluorreverse pairs for the wild-type rosiglitazone experiment set, with RNA from three pools of three rosiglitazone-treated mice hybridized against the pool of RNA from nine vehicle-treated mice. We constructed four fluor-reverse pairs for the Ppara–/– rosiglitazone-treated set, with RNA from four Ppara–/– rosiglitazone-treated mice hybridized against the pool of RNA from four vehicle-treated Ppara–/– mice. We constructed ten fluor-reverse pairs for the DIO set, with RNA from ten B6 mice on a high-fat diet competitively hybridized against a pool of RNA from ten B6 mice on a chow diet. We removed livers from the BXD mice for expression profiling and treated them as described above. RNAs from each BXD mouse were hybridized against a pool of RNAs constructed from equal aliquots of RNA from each mouse. Analysis of expression data. We processed array images as previously described to obtain background noise, single-channel intensity and associated measurement error estimates9. We quantified expression changes between two samples as log10 (expression ratio), where the expression ratio was taken to be the ratio between normalized, background-corrected intensity values for the two channels (red and green) for each spot on the array. We applied an error model for the log ratio as previously described to quantify the significance of expression changes between two samples39. Plasma measurements. Mice were fasted overnight and bled retro-orbitally 2–3 h into the light cycle under isoflurane anesthesia. We carried out enzymatic assays for total cholesterol, HDL cholesterol and triglycerides as described previously40 and analyzed leptin levels using a murine leptin ELISA kit (R&D
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ARTICLES Systems). All measurements were done in duplicate or triplicate and in accordance with the manufacturers’ instructions.
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Body composition. We determined whole body fat, fluids and lean tissue mass of isoflurane-anesthetized mice using a Bruker Optics Minispec NMR analyzer in accordance with the manufacturer’s recommendations. After killing the mice, we dissected out individual fat depots (retroperitoneal, epididymal, subcutaneous and omental) and weighed them separately. Food intake. We caged Alox5–/– and wild-type B6 mice individually in minimal bedding and fed them 6 g of chow per day. Every 24 h for 6 d, we carefully removed and weight the remaining uneaten food to determine differences in food intake. Bone density. For the Alox5–/– and wild-type controls, we determined bone density of individual mouse femurs (right side) by a peripheral quantitative computed tomographic small bone scanner, using a STRATEC XCT 960M unit (Norland Medical Instruments). Femurs were scanned full-length at 2-mm intervals with a resolution of 0.100 mm per voxel, yielding eight 1-mm-thick cross-sections representing eight axial levels of the femur. We selected the center-most scan (based on image morphology) or the mean of two scans sharing the center position for data analyses41. For BXD mice, we determined bone density measures as previously described18. Statistical analyses. We carried out QTL analysis of clinical and expression data in the BXD cross as described previously9. We predicted genotypes in BXD F2 mice at the Alox5 locus by imputing the genotype probability distribution using markers flanking this locus and selecting the genotype that provided the largest contribution to the lod score at the Alox5 location for the OFM trait. We determined differences in measured variables between F2 mice predicted to be homozygous with respect to the DBA allele and F2 mice predicted to be homozygous with respect to the B6 allele using a standard t-test (SPLUS 6.1). Similarly, we determined differences in measured variables between Alox5–/– and control mice, between B6 wild-type rosiglitazone-treated mice and controls, between B6 Ppara–/– rosiglitazone-treated mice and controls, and between B6 mice on a high-fat diet and B6 mice on a chow diet using a standard t-test (SPLUS 6.1). Accession codes. GenBank: adipose-specific isoform of Pparg derived from DBA, AY208184; full-length Pparg cDNA sequence derived from 129S1/ SvImJ, AY243585; genomic sequence derived from 129/SvJ containing the entire C3ar1 coding sequence, U77461. GEO: probe-related data and gene expression data, GSE2008. Note: Supplementary information is available on the Nature Genetics website. ACKNOWLEDGMENTS We thank the Rosetta Gene Expression Lab for microarray work; J. Berger, K. Wong, J. Thompson, E. Tan and E. Muise for sharing the Pparg expression data; J.G. Menke for sharing the LTB4 data; and J. Zhu for discussion on network analysis. This work was supported in part by grants from the US National Institutes of Health (A.J.L.). COMPETING INTERESTS STATEMENT The authors declare that they have no competing financial interests. Published online at http://www.nature.com/naturegenetics/ Reprints and permissions information is available online at http://npg.nature.com/ reprintsandpermissions/
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