systems biology with high-throughput sequencing...

DOI: 10.1161/CIRCGENETICS.114.000520

1

Systems Biology with High-Throughput Sequencing Reveals

Genetic Mechanisms Underlying the Metabolic Syndrome in the

Lyon Hypertensive Rat

Running title: Wang et al.; Systems genetics of metabolic syndrome in LH rats

Jinkai Wang, PhD1,4*; Man Chun John Ma, PhD2*; Amanda K. Mennie, BS2*;

Janette M. Pettus, BS1,2; Yang Xu, MS1,2; Lan Lin, PhD1,4; Matthew G. Traxler, BS2;

Jessica Jakoubek2; Santosh S. Atanur, PhD5; Timothy J. Aitman, PhD5; Yi Xing, PhD1,4,

Anne E. Kwitek, PhD1,2,3

1Department of Internal Medicine, 2Department of Pharmacology, 3Iowa Institute of Human Genetics, University of Iowa, Iowa City, IA; 4Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, Los Angeles, CA; 5Physiological Genomics and Medicine Group,

Medical Research Council Clinical Sciences Centre, Faculty of Medicine, Imperial College London, Hammersmith Hospital, London W12 0NN, United Kingdom

*contributed equally

Correspondence:

Anne E. Kwitek, PhD Yi Xing, PhD

Associate Professor, Associate Director, Associate Professor

Iowa Institute of Human Genetics Dept of Microbiology, Immunology,

Dept. of Pharmacology, Internal Medicine, and Molecular Genetics

Molecular Physiology & Biophysics University of California, Los Angeles

University of Iowa CHS 33-228,

2-252 BSB MERF 650 Charles E. Young Drive South

Iowa City, IA 52242 Los Angeles, CA 90095-7278

Tel: (319) 384-2934 Tel: (310) 825-6806

Fax: (319) 384-3082 Fax: (310) 206-3663

E-mail: [email protected] E-mail: [email protected]

Journal Subject Codes: [112] Lipids, [113] Obesity, [130] Animal models of human disease

PhD ; Yi Xing, PhhDDDD

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Abstract:

Background - The metabolic syndrome (MetS) is a collection of co-occurring complex disorders

including obesity, hypertension, dyslipidemia, and insulin resistance. The Lyon Hypertensive

(LH) and Lyon Normotensive (LN) rats are models of MetS sensitivity and resistance,

respectively. To identify genetic determinants and mechanisms underlying MetS, an F2

intercross between LH and LN was comprehensively studied.

Methods and Results - Multi-dimensional data were obtained including genotypes of 1536

SNPs, 23 physiological traits and more than 150 billion nucleotides of RNA-seq reads from the

livers of F2 intercross offspring and parental rats. Phenotypic and expression QTL were mapped.

Application of systems biology methods identified 17 candidate MetS genes. Several putative

causal cis-eQTL were identified corresponding with pQTL loci. We found an eQTL hotspot on

rat chromosome 17 that is causally associated with multiple MetS-related traits, and found

RGD1562963, a gene regulated in cis by this eQTL hotspot, as the most likely eQTL driver gene

directly affected by genetic variation between LH and LN rats.

Conclusions - Our study sheds light on the intricate pathogenesis of MetS and demonstrates that

systems biology with high-throughput sequencing is a powerful method to study the etiology of

complex genetic diseases.

Key words: metabolic syndrome, genetics, rat/mouse, genome-wide analysis, transcriptome, systems biology

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Background

The metabolic syndrome (MetS), characterized by hypertension, central obesity, dyslipidemia,

and insulin resistance, causes increased mortality due to cardiovascular and renal disease.1, 2 The

etiology of the metabolic syndrome is complex, and is a collection of multifactorial traits each

involving environmental and genetic interactions. While twin studies, familial segregation, and

inter-correlation analyses have all supported the existence of strong genetic influences on the

metabolic syndrome,3-5 the majority of the genetic determinants of the metabolic syndrome

remain to be identified.

Identification of the genetic contribution to complex disease is aided by integrated studies

involving genetic variation, transcriptional regulation, and animal models. The Lyon

Hypertensive (LH) rat was selectively bred for high blood pressure;6 however it has several

features common to human MetS including high body weight, cholesterol, and triglycerides,

increased insulin and insulin/glucose ratio, and high blood pressure.7 The Lyon normotensive

(LN) control strain, concurrently bred for normal blood pressure from the same Sprague Dawley

(SD) colony, is genetically similar to the LH; however this strain is lean, has normal plasma

lipids, and is normotensive. Consequently, the Lyon Hypertensive and Normotensive strains are

really to be considered as the Lyon MetS sensitive and Lyon MetS resistant rat strains,

respectively, and represent a simplified multigenic model for better understanding the

pathological links between MetS and its associated risk for cardiovascular disease.

Previous studies identified quantitative trait loci (QTL) related to MetS phenotypes in the

LH rat, including blood pressure, body weight, plasma lipids, glucose, and insulin, among

others.8 While mapping QTL provide valuable information, integrated systems genetics

approaches can complement positional cloning approaches to identify novel genes causing

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complex traits (reviewed in 9). In this study we used a systems genetics approach to identify

genes involved in traits defining MetS in the LH rat. In a single F2 intercross between the LH

and LN rat strains, we determined MetS phenotypes as well as liver mRNA levels by RNA-seq.

Combined mapping of phenotypic QTL (pQTL) and expression QTL (eQTL) followed by co-

expression network analyses led to strong candidate genes underlying the metabolic dysfunction

in the LH rat.

Materials and methods

Expanded methods are available in the Data Supplement, as indicated below.

Animals

LH/MRrrcAek, LN/MRrrcAek, and their F1 and F2 progeny were bred and maintained in an

approved animal facility on a 12-hour light-dark cycle at the University of Iowa. The rats were

provided chow (Teklad 7913) and water ad libidum unless otherwise noted as part of the

experimental protocol. LH males were bred to LN females to produce F1 offspring, which were

brother-sister mated to generate 169 F2 male progeny to be used in this study. All animal

protocols were approved by the IACUC at the University of Iowa.

SNP genotyping

DNA was isolated from tail or spleen samples from each F2 offspring and parental rats (DNEasy

Blood and Tissue kit, Valencia, CA). SNP genotyping was determined using a custom 1536

Illumina SNP chip, enriched with 453 SNPs selected to tag all haplotypes differing between the

LH and LN genomes (Table I and Methods in the Data Supplement).10 Genotyping was

performed according to manufacturer specifications at GeneSeek (©Neogen Corporation). Only

SNP calls that were polymorphic between LH and LN strains, and with GenCall scores

below.

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exceeding 0.4 were accepted for analysis.

Phenotyping

Beginning at three weeks of age, parental and F2 rats entered a 12-week phenotyping protocol

(Table II and Methods in the Data Supplement). Briefly, body weight, blood pressure (by

telemetry), and plasma measures of lipids, glucose and leptin were determined. Ear and tail snips

were taken for DNA isolation, and tissues were harvested at the end of the protocol. For each

phenotype, differences between the F2 offspring and parental strains were determined using

ANOVA. Phenotypic (p)QTL were mapped to the genome using R/qtl and the SNP genotypes

determined in the F2 rats.11 Multiple testing error was corrected for by permutation testing (see

Methods in the Data Supplement).

RNA-seq

Total RNA from liver tissue obtained after completing the 12 week phenotyping protocol from

LH (N = 6), LN (N = 6), and F2 (N = 36) rats was sequenced on an Illumina HiSeq 2000 to

produce approximately 30 million 51-bp paired-end reads per sample (Table III in the Data

Supplement). The sequenced F2 rats were selected to be enriched for the extremes of the

phenotype distributions. Sequences were aligned to the rat genome using Tophat (Figure I in the

Data Supplement),12 and for mapped reads, FPKM were determined using Cufflinks.13

Differentially expressed genes (DEGs) between LH and LN were determined using edgeR

(version 3.0.0)14 and corrected for multiple testing using an FDR < 0.05. eQTL mapping was

performed in the genotyped F2 offspring using FPKM data as normalized gene expression and

the R/qtl and R/eqtl packages, with permutation testing to correct for multiple testing error.15

Gene Ontology enrichment analyses were performed using DAVID.16 Detailed methods can be

found in the Data Supplement.

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Causality tests

To infer causal relationship between gene expression and phenotypes (or another gene

expression), we used the single.marker.analysis function from the Network Edge Orienting

(NEO) package17 implemented in R (see Methods in the Data Supplement). In this study, the

eQTL peak or mQTL (module QTL) SNPs were used as anchor SNPs. Causality tests were

conducted only when traits show correlation with expression value (Pearson R 0.3). Causality

was indicated when the RMSEA (Root Mean Square Error of Approximation) value was < 0.05,

which indicate good model fit, and the causality score was > 0.3,18-20 representing 2 times higher

probability of causal model than any other four models. For causality tests with a large number

of genes, we focused on global patterns rather than specific genes to avoid the pitfalls of multiple

testing that may inflate the false positive rate.

Network construction and analyses

Unsigned weighted gene co-expression network based on the gene expression of 36 F2 rats was

constructed using WGCNA package in R,21 including 10,088 genes with mean FPKMs >1 across

the cohort. Network modules were determined with at least 30 genes and minimum height for

merging modules at 0.25 to obtain moderately large modules (see Figure II and Methods in the

Data Supplement). Gene ontology enrichment analysis of each module was conducted using

DAVID.16 Module eigengenes were summarized by the first principal component of the module

expression profiles. To determine mQTLs, clusters of genes that were regulated by the same

eQTL hotspot were selected and tested for enrichment in particular modules using Fisher exact

tests.

qPCR validation

Quantitative RT-PCR (qPCR) was performed on liver RNA from all available F2 rats (N=144)

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and parental controls for the cis-eQTL genes RGD1562963, Aqp11, Prcp, and Mapk9 (see

Methods in the Data Supplement). Gene expression (2- ) differences between groups were

determined by ANOVA followed by post-hoc tests for pairwise significance compared to the LH

parental control. Correlations between gene expression and phenotypes were determined using

Spearman correlation, with nominal P values of < 0.05.

Genome sequencing and analysis

Genome sequencing and identification of variants was performed as previously described.22

Sequence data is available at the EBI Sequence Read Archive under accession number

ERP002160. Sequence variants are available at the Rat Genome Database

(http://rgd.mcw.edu).The Alibaba223 program was used to predict transcription factor binding

sites using binding sites from TRANSFAC database (http://www.biobase-

international.com/product/transcription-factor-binding-sites).

Data access

RNA-seq raw data and gene expression values of each sample have been deposited in the

Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/geo/) under the accession

number GSE50027.

Results

pQTL (Phenotypic Quantitative Trait Locus) mapping

To identify loci and genes contributing to the phenotypes defining MetS, we phenotyped 169

male offspring from an F2 intercross between LH and LN rats, as well as male parental LH (N =

13) and LN rats (N = 20), for traits including blood pressure, plasma lipid, glucose, and leptin

levels, and body weight and growth (Table IV and V in the Data Supplement). For all blood

pressure, body weight and growth measures, and plasma leptin levels the F2 cohort had an

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intermediate phenotype, significantly differing from both LH and LN strains (P < 0.01). At 18

weeks of age plasma triglyceride levels in the F2 rats were more similar to LN rats, both with

significantly lower levels than LH rats, whereas plasma cholesterol levels at 12 and 16 weeks of

age were similar or higher in the F2s than LH.

453 SNPs were used to tag all haplotypes differing between LH and LN across the

genomes of the two strains10, 24 (Table I in the Data Supplement). pQTLs of 23 phenotypes were

calculated based on the F2 cohort and analyzed using R/qtl11. Seventeen pQTLs exceeding the

5% genome-wide threshold for significance (defined by permutation testing) were identified for

plasma cholesterol, plasma leptin, body weight, growth curve, heart rate, and blood pressure on

chromosomes 1, 2, 10, 15, and 17 (Figure 1 and Table VI in the Data Supplement). Overlapping

QTLs for plasma cholesterol and body weight were identified on RNO10, while overlapping

QTLs for plasma leptin and blood pressure were identified on RNO17. All other QTLs were non-

overlapping.

eQTL (Expression Quantitative Trait Locus) mapping and parental rat DEG

(Differentially Expressed Gene) analyses

Liver mRNA from parental (N=6) and F2 (N=36 with extremes of the phenotypes) rats

were sequenced to produce ~30 million paired-end reads (51 bp) per sample. FPKM (fragments

per kilobase of transcript per million fragments mapped)13 was used as a normalized quantitative

measure of gene expression and eQTL mapping was performed using the R/eqtl package.15 We

identified 1264 suggestive eQTLs (Lod > 3.82) and 276 significant eQTLs (Lod > 4.84). The

chromosomal locations of the regulated genes were plotted against the chromosomal locations of

their associated SNPs (Figure 2) to visualize both cis-eQTLs (diagonal) and trans-eQTL hotspots

(vertical). For example, we identified a single SNP (ENSRNOSNP962219), on RNO17 as an

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eQTL hotspot. This SNP is associated with 66 trans-eQTLs and one cis-eQTL at 29.9 Mb on

RNO17 that exceed 5% genome-wide significance. The cis-regulated gene, RGD1562963, will

be discussed in detail below. The genes sharing this eQTL hotspot have significant GO (gene

ontology) enrichment in genes involved in mitochondrial function including oxidative

phosphorylation (FDR = 0.017), and comprise 22.4% of the 67 genes. Other trans-eQTL hotspots

were identified on chromosomes 7, 8, and 14.

In liver from LH and LN rats, 610 DEGs were identified with a false discovery rate

(FDR) of 5% (Figure III in the Data Supplement). The numbers of genes upregulated and

downregulated in the LH rats were similar (322 and 288, respectively). Genes upregulated in the

LH rat were significantly enriched for immune response and inflammatory processes while

downregulated genes were enriched for fatty acid metabolism (Figure III in the Data

Supplement).

Integrate pQTLs, cis-eQTLs and DEGs to identify candidate causal genes.

Integration of the liver gene expression with SNPs and phenotypes was used to discover

candidate genes responsible for the etiology of MetS. Several criteria were set as filters to select

the most promising eQTL-gene pairs causing MetS in the LH rat. First, the genes must be

differentially expressed between LH and LN strains. Second, they must have at least one eQTL

where the peak SNP co-localizes within the pQTL confidence interval; third, the average F2

FPKM must be > 1, to avoid potential noise in RNA-seq of genes with very low expression.

Finally, to address whether the regulated genes are likely to cause the phenotypic outcomes, the

Network Edge Orienting (NEO) package17 was used to infer the causal relationships between

genes meeting the above criteria (N=25) and all measured phenotypes, using the eQTL peak

SNPs as anchors. Causality tests (LEO.NB.SingleMarker) were calculated between each of the

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genes and any correlated phenotypes (R 0.3). Genes causally affecting at least one phenotype

were ranked by their maximum scores, using a previously suggested causality score threshold of

0.3,18-20 equivalent to a 2-fold higher probability compared to any other model (for example, the

phenotype modifies the gene’s expression). This approach identified 17 genes (66 SNP-gene-

phenotype trios) (Table 1 and Table VII in the Data Supplement). Of these 17 genes, 6 are cis-

regulated and fall within pQTLs on RNO 1, 2, 10, and 17, while the remaining 11 are regulated

in trans. One gene (Aqp11) has a cis-eQTL that shares the same peak SNP with pQTLs, and the

average number of phenotypes causally affected by these 17 genes was 3.3.

Cis-eQTLs are strong candidates for causing pQTLs, when the cis-regulated genes fall

within the pQTL intervals. To further investigate causal cis-eQTL, we performed qPCR for four

of the six cis-regulated genes (Aqp11, Prcp, Mapk9, and RGD1562963) in the entire cohort of F2

rats completing the study (N = 144). Using the nearby SNP as a marker for the genotype for each

gene, all four genes were found to have genotype-specific expression in the F2 rats (Figure 3a-d),

with allelic expression similar to that found in liver from the parental LH and LN rats.

Aqp11 and Prcp both fall within QTL on RNO1 for measures of body weight (Figure 1a

and Table VI in the Data Supplement). Both RNA-seq and qPCR determined liver Aqp11

expression is downregulated in the LH compared to the LN allele (Table 1; Figure 3a).

Furthermore, expression of Aqp11 is negatively correlated with a co-localized pQTL trait – Adj

BW 16 wk (nominal P < 0.05; Figure 3e). Conversely, Prcp expression is significantly

upregulated in LH liver (Table 1; Figure 3b). Prcp expression is also significantly correlated

with all traits having co-localized pQTL (Adj BW 12 wk; Adj BW 16 wk; Growth AUC; Figure

3e). These data suggest that genetic variation causing concurrent downregulation of Aqp11 and

upregulation of Prcp cause obesity in the LH rat. Of note, both genes have been reported as

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MetS-related genes through mouse knock out studies (Table VII in the Data Supplement).25-27

Network construction and module QTL (mQTL) analyses

While the integrated eQTL/pQTL approach identified regulated genes with gene expression and

sequence variation between the LH and LN strains, as well as functional relevance to the mapped

traits, the phenotypic variation due to single genes in a multifactorial disease like MetS may be

limited. As such, the expression data generated from RNA-seq can also be used to unveil the

mechanisms of etiology of this disease at the network level. A weighted gene co-expression

network was constructed based on the gene expression measures of the 36 F2 individuals,

consisting of 10,088 genes with average FPKM > 1. Fifteen modules were obtained from the

network analysis. Figure 4a is a global view of the network. Together the top 5 modules contain

7781 genes, over 77% of all genes included in the dataset, and have strong gene ontology

enrichment (Figure 4b). Other modules, such as the cyan and pink modules, have strong gene

ontology enrichment in immune regulation and inflammatory response, similar to that of the

enrichment in the upregulated genes in the LH compared to LN rats (Figure III in the Data

Supplement).

Module eigengenes (ME) were calculated to represent the gene expression of each

module, and correlations between multiple module eigengenes and multiple MetS-related traits

were observed (Figure IV in the Data Supplement). For example, the grey module eigengene has

strong correlation with leptin, glucose, white adipose and multiple blood pressure related traits.

Module QTLs (mQTLs) were also determined, defined as an eQTL-peak SNP of multiple genes

that show significant module enrichment based on the Fisher exact test (see Methods in the Data

Supplement). In this analysis, we relaxed the eQTL Lod score threshold to Lod > 3 in order to

increase the power of mQTL detection. As shown in Figure 4c, genes sharing the same eQTL

e 36 F2 individualslslsls,,,,

were obtbtbtbtaiiiinedddd fffrf ommmm tth

a o

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n h

in the preg lated genes in the LH compared to LN rats (Fig re III in the Data

alysysysisisisi . FiFiFiF gguruuu e 4444a a is a global view of the nnnetetwork. Togetherr ttttheh top 5 modules co

, ovvvver 77% of ff aaall ggennnes ininini cludududu eddd iin thhhee daataseet,t,, ananananddd hhhavve sttroongngng genneeee ononontooologyggy d

(Figuruureee 444bbb). OtOtOOthehehh rr mom duleleleessss, suchhhh asasasas ttthhehe ccyayan n anananand dd pppinknknk mmododdduules, hahahave ssttrtronongg geg

nrichment in immune regugg lall tiiion andd iii fnffflllammatory yy resppponse,,, iisi iimiillal r to that of th

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peak SNP are enriched in a limited number of modules.

Of the mQTLs, ENSRNOSNP962219 is located in a pQTL region for blood pressure and

plasma leptin on RNO17 (Figure 1) and has the largest number of genes regulated by this eQTL

hotspot. Other mQTLs, including the strong trans-eQTL hotspots on RNO 7, 8, and 14, are not

located in pQTL regions and thus were not studied further. ENSRNOSNP962219 is an mQTL of

both the turquoise (P = 3.7×10-7, Fisher exact test) and blue modules (P = 2.0×10-42, Fisher exact

tests) (Figure 4c). These two modules are the top two largest modules and represent distinct

functional categories. The turquoise module is enriched for genes involved in transcriptional

regulation; the blue module is enriched for genes associated with translation, ribosomes,

mitochondria and oxidative phosphorylation (Figure 4b). As shown in Figure 5a, these two

modules also show significant correlations with multiple phenotypes, including body weight,

white adipose tissue (WAT), blood pressure, and plasma triglycerides. To test if these two

modules have causal relationship with phenotypes, we used ENSRNOSNP962219 as the anchor

SNP to calculate the causality scores of the module eigengenes to the correlated phenotypes. As

shown in Figure 5b, the blue module shows a causal relationship to white adipose, while the

turquoise module shows a causal relationship to multiple phenotypes including body weight,

white adipose and blood pressure. Therefore these two modules are predicted to causally affect

most of the correlated phenotypes.

To further study the role that mQTL ENSRNOSNP962219 plays on gene expression

regulation and MetS phenotypes, we focused on the 293 genes sharing the eQTL peak SNP

ENSRNOSNP962219 at a Lod > 3. Of these 293 genes, 279 (95.2%) fell into either the turquoise

(118 genes) or blue modules (161 genes) (Figure 4c). For each of these 279 genes, we conducted

causality tests to determine whether the gene’s expression causally affects phenotypes that are

olved in transcriptp ioioioionnann

slation, ribibibibosomesss,

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so show significant correlations with mult le phenotypes, including body we h

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c late the ca salit scores of the mod le eigengenes to the correlated phenot pe

ria aaandndndnd oooxixixidaddd tiiiveveve phosphorylation (Figurrrreeee 444b). As shown in n FiFFF gure 5a, these two

soooo sssshow signiffficccantt cccorrrrrelelele atioooonns wwithhh mmulltiipleee pppphehehennottyppes, innnclululuudingggg bbbboddyyy wwweigii h

ose titiitisssssssueueueue (((WAWAWAT)T)T)T), blblblooo d prprpresesesessure, anananand ddd lplpllasasmama tttririririglglglyyycerereriddidideses. TTTTo ttttesesesest tt iiiif theheh sese tttwowo

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correlated with this specific gene. We found 136 (84.5%) of the 161 genes in blue module

causally affect white adipose, and more than 80% of the 118 genes in turquoise causally affect

adjusted body weight and diastolic blood pressure. A number of genes show causal relationship

to other phenotypes (Figure 5c). These results are consistent with the causality tests using

eigengenes, suggesting these two modules are responsible for many MetS phenotypes in the LH

rat. Furthermore, it suggests the turquoise network has pleiotropic effects on common MetS

phenotypes.

RGD1562963 is a potential driver gene of the gene cluster sharing a common eQTL hotspot

within pQTL for MetS traits.

As described above, ENSRNOSNP962219 defines a trans-eQTL hotspot, suggesting genes in

the hotspot are affected, directly or indirectly, by a common driver gene. In principle, the driver

gene would be regulated in cis (i.e. a cis-eQTL) at ENSRNOSNP962219 and have causal

relationship to trans-regulated genes sharing the same eQTL peak. To find the driver gene, we

selected the 10 genes in the 95% eQTL hotspot confidence interval (RNO17:1-39 Mb) with an

eQTL Lod > 3 (relaxed criteria). Only one cis-eQTL gene, RGD1562963, exceeded the Lod 4.8

threshold for significance (Lod 6.8) (Figure 6a). Two other cis-eQTL genes had suggestive

linkage, Sfxn1 and Prelid1 (both with Lod 3.93). Furthermore, only RGD1562963 is

differentially expressed between the parental LH and LN strains, and shows allele specific

regulation in the F2 cohort (Table 1; Figure 3c).

If a gene is cis-regulated, it also must have sequence variation regulating its expression.

Genome sequencing of both the LH and LN strains identified approximately 643,000 SNVs and

327,000 indels between the strains.22 These variants cluster into haplotypes derived from

different ancestral chromosomes.10 In the 95% confidence interval of the eQTL hotspot on

a common eQTLLLL hhohh

e s

d

d

p

e 10 genes in the 95% eQTL hotspot confidence inter al (RNO17:1 39 Mb) ith

ed aaabobobob veveve, ENENEENSRSRSRRNOSNP962219 defines aaa ttrans-eQTL hotsspppot, suggesting genes

aaara eee e affected, dididirrectlyyyy orrr r iini diiiirerrectttlyy, bybby a cococommmonononn dddrivevver geennne. InInInn priiiincncncn ipppleee, thhhhee d

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ppp to trans-regggulatedddd gggenes shhhah ringgg the same eQQeQTLLL pppeak.kk TTTo fifififi ddndd thhehh driver gegg ne,,,

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RNO17, only three small haplotypes differ between LH and LN; one of these haplotypes (29.7 -

30.3 Mb) contains RGD1562963 (Figure 6b). There are 86 sequence variants between LH and

LN rats in the genomic interval encoding RGD1562963 and the 5 kb upstream. In comparison,

Sfxn1 has only one intronic indel and Prelid1 has no SNVs between the strains (Figure 6c).

Three non-synonymous variants (V36I, R135H, and C139Y) were identified in RGD1562963,

although functional variant prediction tools (Polyphen and SIFT28, 29) predict these variants to be

benign. We also identified 3 variants within relatively conserved regions shown as conservation

score peaks on UCSC conservation track based on comparisons of 9 vertebrates, and 19 variants

within putative transcription factor binding sites for PHO2, Oct-1 and GATA-1 (Alibaba223)

(Table VIII and Figure V in the Data Supplement). One variant (SNV1) in the predicted

promoter of RGD1562963 fell into both categories and is a strong candidate for the functional

variant regulating RGD1562963 expression.

The data implicate RGD1562963 as being cis-regulated in LH rats. However, to be the

driver gene of the trans-eQTL cluster, it must also affect downstream trans-eQTL genes.

Therefore, we performed causality testing between RGD1562963 and the 278 trans-eQTL genes

that are located on different chromosomes (Lod > 3). RGD1562963 is predicted to causally affect

100 of the 278 trans-eQTL genes (Figure 6d). These results support RGD1562963 as the likely

driver gene of the gene cluster sharing the ENSRNOSNP962219 eQTL hotspot. Together the

data indicate the genetic dysregulation of RGD1562963 directly affects the turquoise and blue

modules through the trans-eQTL genes, resulting in a cascade of dysregulation of the

downstream module genes, which in turn affect the MetS traits.

Discussion

The etiology of the Metabolic Syndrome is complex, as it is a collection of several underlying

ertebrates, and 19 vavavv r

GATATATATA 111-1 (A(A(A(Alilililibbbab babababa222222232

I

f

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of the trans eQTL cl ster it m st also affect do nstream trans eQTL genes

I annnddd d FiFiFiigugugurerere V V VV iniini the Data Supplement). OOOnne variant (SNV1V1V11) in the predicted

f RGRGRGR D15629663 fellll iiini toooo bbboth hhh ccatettegorrriees aannd iiiss a a a a sstss rrongnng canndddidadaattte forrrr tttheee ffffunnnctcttio

ulatiiiingngngg RGRGRGR D11156565662929292 6363633 exprprpresesesession.

e data imppplicate RGRGRGR D1DD 5656565 29292996363636 as bbbeinggg cisiii -regugg lated ddd iiini LLLH HH rats. HoHH wever,,, to be

fof thhe t QeQTLTL ll st iit t lal ffff t ddo st t QeQTLTL by guest on June 8, 2018http://circgenetics.ahajournals.org/

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multifactorial traits. To identify novel genes and pathways that underlie the individual

components characterizing MetS, as well those that could explain the co-occurrence of these

traits, we applied a systems genetics approach in LH and LN rats, inbred strains that show

genetic MetS susceptibility and resistance, respectively. Through integrating pQTL and eQTL

from a segregating F2 intercross between LH and LN, and parental gene expression differences,

followed by co-expression network analysis and tests for causality, we identified 17 genes

predicted to be causal for at least a subset of the MetS phenotypes. We identified several cis-

regulated genes that fall within pQTL for MetS traits which are strong candidates for the pQTL

(Table 1, Figure 3). Furthermore one of these cis-regulated genes (RGD1562963) also falls

within a trans-eQTL hotspot on RNO17 and is a strong candidate driver gene of downstream

gene regulation (Figure 6). The genes in the trans-eQTL hotspot fall within modules that are

causally related to the measured MetS traits, with function related to global transcriptional

regulation and mitochondrial oxidative phosphorylation, providing candidate mechanisms

underlying a genetic component of MetS in the LH rat model (Figure 5).

The study identified pQTL for MetS traits on RNO1 (body weight measures and plasma

cholesterol), 2 (growth, plasma cholesterol, heart rate), 10 (body weight measures and plasma

cholesterol), 15 (heart rate), and 17 (blood pressure measures, plasma leptin). Comparing these

results with our previous study,8 we found concordance of many of the pQTL identified in this

study. However, there are also differences. For example, we did not confirm the previously

mapped blood pressure QTL on RNO2 and RNO13. Furthermore, while body weight, plasma

lipids and blood pressure all mapped to RNO17 in the previous study, only the blood pressure

QTL were replicated. Some of the differences could be due to differences in cohort size, with the

previous study including over 300 animals. Furthermore, the phenotypes in the previous study

candidates for theeee ppppQ

D1566662929292963636363)))) allllso ffffalalalallllsls

a m

a r

l

a

a genetic component of MetS in the LH rat model (Fig re 5)

ans-e-e-eQTQTTQTL LLL hohohohotsspopopot on RNO17 and is a strororonng candidate drivevever gene of downstream

atiioi nnn n (Figure 66)6 ... Thhheee gennnneese in nn ttht eee trannnss-eQQTLTL hhhototototspsps ottt ffall wiwiwithhhinininn mododododuuulu eseses thaaat tt ar

latedddd tttoooo thththt e memeasasurur ddeded MetttSSS tttrtraits, wiwiwithththt ffffunun tctcttiooonnnn reeelalalateddd totototo ggllolobaballll trtrtrt aaansccririi tptptioonanal

and mitochondriiii lall oxidaddd tiiive ppphhoh sppphhoh ryyylllal itiion, ,, prpp oviddddiiini g gg canddddidididi ate mechanisms

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were measured at 29-31 weeks of age, compared to 12-18 weeks of age in the present study,

which could significantly influence the QTL detection. For instance, it is known that the blood

pressure in LH continues to increase between 18-29 weeks of age.7, 30 The fact that blood

pressure solely mapped to RNO17 at a younger age could suggest that the RNO17 locus initiates

hypertension, while loci on RNO2 and RNO13 involve disease progression evident at later ages.

In the pQTL for body weight and growth on RNO1 we identified two cis-eQTL genes of

note - Prcp (also known as angiotensinase C) and Aqp11 (aquaporin 11). Prcp encodes a

lysosomal prolylcarboxypeptidase, which regulates both the renin-angiotensin and kallikrein-

kinin systems.31 It has more recently been shown to be a key enzyme involved in the degradation

- -melanocyte-stimulating hormone) to an inactive form unable to inhibit food

intake.32 Deletion of Prcp in mice reduces body weight and attenuates the metabolic effects of

diet-induced obesity.25, 27 It is significantly upregulated in LH compared to LN liver (fold change

= 1.8; FDR = 9x10-9), and its expression is nominally correlated to the QTL traits, including

body weight and plasma cholesterol, making it a candidate causal gene for the QTL (Figure 3e).

Another cis-regulated gene in the RNO1 pQTL is Aqp11, a member of the aquaporin

major intrinsic protein family that facilitates the transport of water and small neutral solutes

across cell membranes. As shown in Figure 3e, Aqp11 expression is downregulated in the LH,

correlated with body weight, and causally affects leptin level and bodyweight. LH rats have a

variant in the 3’UTR (1g.154973845T>C) as well as in the 5’ upstream region

(1g.154985253A>T) of Aqp11 that are in positions with conserved sequence in rat, mouse,

human, dog, and opossum. Of note, the 5’ upstream variant is located in a predicted transcription

factor binding site for C/EBPalp, which has been reported to bind to the promoter and modulate

the expression of the gene encoding leptin, providing a possible causal variant for altered leptin

iotensin and kallikkkkrererer i

nvolvlll edddd iiiin ththththe deeegrgrgrgrad

e s

c

R n

ht and plasma cholesterol making it a candidate ca sal gene for the QTL (Fig re

-mememelalalaanononocycycytee d -s-s-stimulating hormone) to aaan inactive form unununabaaa le to inhibit food

ellllettttion of Prcpp iiin mmiiici e rererer ducecces bobbodyyy wweighght anand d d atata tenunnuatess tttheee mmmetababababolo icicc effffffffeects

d obebebbesisisisitytytyty.25252525, 27272727 ItItIt iiiss ssiiigignififificacacaanntly uuuprprpregegegeg lulullatatt dededd iiin nn LHLLHLH commmmpapareredddd to LLLLN NNN liveverr (((fofoff ldldld c

R = 9x10R -9),), and iiiits expppressioii n is nomiiinallllllly yy correlatedddd to thhhhe QTQTQTQ LLL traits, ,, includin

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levels.33 Interestingly, liver specific knockout mice showed increased HDL cholesterol as well as

periportal hepatic rough endoplasmic reticulum (RER) vacuolization associated with ER stress.26

The current and previous studies found QTL for multiple MetS traits on RNO17, which

were replicated in consomic rat models.8, 34 Therefore, we speculate there may be a gene or genes

having pleiotropic effects on the MetS spectrum. In this study, we identified a cis-regulated gene

on RNO17, RGD1562963, as a putative driver gene regulating multiple downstream genes. The

trans-eQTL genes fall within two co-expression modules which are causally related to body

weight and blood pressure measures. Therefore we speculate RGD1562963 dysregulation causes

a cascade of downstream gene dysregulation that ultimately impacts body weight and blood

pressure. However it must be noted that RGD1562963 expression alone was not significantly

correlated with these traits; it showed only a nominal correlation to plasma cholesterol levels

(Figure 3) which was not significant after correction for multiple testing of 23 traits. One

possibility for these results is that RGD1562963 may be a driver gene but unrelated to the LH

phenotypes. Another possibility is that the expression of MetS involves multiple pathways and

the variation in a single gene, without considering its downstream effectors, may be insufficient

to identify a causal relationship. The expression difference in RGD1562963 between the LH and

LN genotypes, while consistent across our studies, is relatively modest (Figure 3). There may not

be enough variation in RGD1562963 alone to detect significant correlation with the phenotypes,

but its dysregulation may induce a cascade of downstream gene dysregulation (ie the genes in the

trans-eQTL hotspot), and the conglomeration of these genes show causal relationships to the

MetS traits.

RGD1562963 has no functional annotation. Its ortholog in the human (C6Orf52) is also

only annotated as an open reading frame with no predicted function; orthologs have also been

62963 dysregulatioooonn n n c

ody w iiiei hghhhtttt anddd d blblblbloooood

H t

w e

w

f L

Another possibilit is that the e pression of MetS in ol es m ltiple path a s

Howeweweveveverrr ititit mmmusst tt t be noted that RGD1562999636363 expression alonononee e was not significant

wwwwithhh these traitts; it shhohoh wewewew d onnnllyl a nommminaal corrrreelalalatiiionnn tto plllasmamama chooooleleleestererrol lllleeve

whichhh wawawawas noottt iisisigngngniffifificicii ant tt affafaftttter cooorrrrrrecececectititionon fffoor mmmmuluulultititippple teeteteststtinini g g g offff 2222333 tttraiiitstst . OnOOnO ee

for these results iiiis thhhhat RGRGRGR D1D1D1D 56565662929296363633 may yy bbbeb a driivi er gggene bbbub t unrelated to the L

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identified in several other species. The closest sequence homology with RGD1562963 or its

orthologs is with TRNAU1AP, coding for an RNA-binding protein in a complex that is required

to generate selenoproteins, which are important antioxidants such as Glutathione peroxidases and

Thioredoxin reductase.35, 36 The RNA binding-domain characterizing TRNAU1AP (RRM

domain) also has a role in regulating transcript stability.37 Both functions directly relate to the

functional enrichment of the blue (mitochondrial oxidative phosphorylation) and turquoise

(transcriptional regulation) modules. However, the sequence similarity between RGD1562963

(and its orthologs) and TRNA1AP do not include the RNA binding domain or any known

functional domain or motif, thus predicting its function is premature and will be the focus of

future studies.

The expression of RGD1562963 is also strongly associated with the blue co-expression

module, including genes involved in mitochondria and increased oxidative phosphorylation. This

is evidenced by the mRNA upregulation of several NADH dehydrogenase genes found in the

blue co-regulation module. Interestingly, genes involving oxidative phosphorylation have also

been found to be upregulated in livers from diabetic obese patients as compared to diabetic lean

patients.38 While increased respiration is likely aimed at maintaining homeostasis, chronic

activation could eventually lead to opening of the mitochondrial permeability transition pore and

apoptosis,39 causing further defects in lipid metabolism in the liver. While the role of the

mitochondria is well established in most traits characterizing MetS, further studies are needed to

elucidate the mechanism of RGD1562963 and its downstream effects on the metabolic syndrome.

Transcriptome studies only capture a temporal and spatial snapshot of gene expression.

Some gene expression variation may be due to a consequence of the MetS state, rather than

driving MetS. Furthermore, cell types may differ in the groups being compared. For example,

main or any y known n n n

nd wiiiillllllll bbbbe ththththe fffof cucuuussss o

i

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cluding genes involved in mitochondria and increased oxidative phosphorylation

d t

lation mod le Interestingl genes in ol ing o idati e phosphor lation ha e a

ies.

e eeexpxppxpression offf RRRGD1D1D1566662922 6333 iiisi aallso sttronnggly asassosososocciatttedd wiiti hhh ththheee bluuueu co-o-o-exprppress

cluddddininiinggg g gegegeg nes s iininvovollvlvede in mimimimitochhhhononondrdrdrdriiia aa dndndd incncnccrrrreasasas dded oooo iixixidadad titititive pppphohohosphohoh ryrylalal tititiono

d by yy the mRNAAAA uprpp eggg llulatioii n fofff severallll NANANANADHDD dddd hehhhyddydrogegg nase geg nes found in t

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there is significant enrichment of DEGs between LH and LN rats involved in inflammatory

response; this may be due to immune cell infiltration into the liver rather than liver gene

expression differences. To address these issues, gene expression could be measured prior to

significant disease onset. Because the phenotypes are present by 5 weeks of age in the LH,40 a

temporal study at early ages may better define the time of MetS initiation and provide more

homogeneous cell types in the liver.

Systems genetics approaches are predictive and hypothesis generating, but require

experimental validation. From the F2 cohort of 169 male rats, a subset of 36 rats was selected for

the eQTL studies. While these numbers are in line with studies in RI strains, they are low for

many eQTL studies in F2 cohorts. While the rats were selected based upon phenotypic extremes,

power may be insufficient to detect some eQTL, leading to possible Type II error. However,

combining the eQTL mapping with systems biology approaches have led to both genes and

pathways that are strong candidates for functional validation. Additional experimental validation

is required to prove the role of RGD1562963 as a driver gene of the trans-eQTL hotspot and its

role in MetS in the LH rat. For example, siRNA knockdown of RGD1562963 in hepatocytes or

in vivo should alter the expression of genes in the trans-eQTL hotspot and affect liver

metabolism. These studies are ongoing. Studies in congenic or knockout strains involving

RGD1562963 will be necessary to prove a causal role for this novel gene in the MetS traits in the

LH rat.

This study uses a unique rat model of MetS (the LH and LN inbred rat strains) to

integrate genetic, transcriptomics, and systems genetics approaches in the identification of

candidate genes and mechanistic pathways causing the diverse phenotypes defining MetS.

Through our studies we identified cis-regulated genes that are strong causal candidates for

t of 36 rats was selelelelecctcc

trains, ththththey are lowowoww ffo

L r

be insufficient to detect some e L, leading to possible Type II error. Howeve

the eQTL mapping with systems biology approaches have led to both genes and

h d

to pro e the role of RGD1562963 as a dri er gene of the trans eQTL hotspot an

L stttudududu ieieieess s inininn F2 22 cccohorts. While the rats weweweree selected based dd uuupon phenotypic extr

bbeb insufficiennt to dddeeeteccccttt t somemme eQQTLLL, leadidingg tto o o o popopp ssibiible TTyyypeee IIIII errrrrororor. HHHoH wwwevve

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hat are stronggg candididididddad tes ffof r fuff nctiiion lall v lallidididdation. AAAAddddddd iiiti ioiii nalll l expepp rimental valid

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determining body weight and plasma lipid levels. One of these cis-regulated genes,

RGD1562963, is also a putative master regulator of downstream pathways involving global

transcriptional regulation and mitochondrial function. Future studies that validate and determine

the role of these genes in the LH rat facilitates not just the translational studies of MetS-

susceptibility genes in humans, but also generalized disease mechanisms that offer in-roads to

personalized therapies.

Acknowledgments: We thank Janet Beinhart for rat colony maintenance as well as Stephanie

Dunkel and James Stewart II for technical assistance.

Funding Sources: This work is supported by NIH grants R01HL089895 (AEK) and

R21DK089417 (AEK and YX), and the Fraternal Order of Eagles Diabetes Research Center at

the University of Iowa.

Conflict of Interest Disclosures: None

References:

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Beckckck AAAA, BBBihohoh rereauau MMMMT, BBBirririrnnnney E,EEE BBBBrororor kckckkllelebbabb nknknkk DDDD, ChCChChennenen YYYY, eet allll. SSSnp aandnddd hhhhapaplollor genetic analysysysysisss iiiinnn n thththheeee rararat.t.t.t NaNaNaNat t t t GeGeGeenenenen tt.. 20202008080808;4;4;400:0:0 56565660-0-0-0-56565666.6.6.6

KKK, Szabo o G,G,GG, RRRasasasasooo GMGMGGM, ,, MeMeMeMelililili RRR,,, DiDiDDianananano oo S.SS.S. DDDDeleee etetetioioioion nn ofofofof ppprorororolyllylyllll caccacarbrbbrboxoxooxypypypypepepeptidase theh et bab loliic ffff ts ff didiet ii dnd ded bbe isit AA JJ PPhh ii ll EE dd ii ll MM t bb by guest on June 8, 2018

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C, Dominguez C, Allain FH. The rna recognition motif, a plastic rna-binding plap

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Table 1: Candidate genes identified for MetS traits identified by integrated mapping and network analyses.

Gene Name eQTL Chr

eQTL LOD Maker name of peak Type Peak

position

LH mean

FPKM

LN mean

FPKMDEG FDR Gene

Chr Gene start Gene end # Anchor* # Traits† Causality score‡

Aqp11 1 7.15 ENSRNOSNP70864 cis 154627781 7.0 12.3 1.4E-04 1 154973798 154983989 2 2 0.68

Pigl 10 7.13 ENSRNOSNP2796578 cis 49338801 1.5 3.9 1.1E-09 10 48629286 48689381 1 1 0.7

Aqp11 1 7.11 ENSRNOSNP45308 trans 136552144 7.0 12.3 1.4E-04 1 154973798 154983989 2 2 0.68

RGD1562963 17 6.79 ENSRNOSNP962219 cis 29903973 1.7 1.1 4.4E-02 17 29733271 29746769 1 1 0.311

Iah1 17 6.34 ENSRNOSNP962219 trans 29903973 79.5 50.2 2.0E-06 6 41875020 41882235 1 4 1.1

Neurl4 10 6.04 ENSRNOSNP2796827 trans 75516973 3.8 7.6 1.6E-08 10 56745822 56757435 2 9 1.14

Pex11b 2 5.29 ENSRNOSNP2786744 cis 191277899 7.8 13.8 2.6E-04 2 191437355 191446244 1 1 0.705

Prcp 1 5.26 ENSRNOSNP2784200 cis 151110344 5.6 3.1 8.7E-09 1 149711289 149763868 2 1 0.588

LOC303140 10 5.07 ENSRNOSNP2796873 trans 80398271 0.4 1.4 7.5E-12 10 39407540 39417112 1 1 1.02

Mapk9 10 5.05 ENSRNOSNP2796424 cis 36183842 15.1 27.2 1.9E-06 10 35344672 35384319 2 9 1.48

Neurl4 10 4.98 ENSRNOSNP2796437 trans 37954559 3.8 7.6 1.6E-08 10 56745822 56757435 2 9 1.14

Phldb3 1 4.93 ENSRNOSNP2783493 trans 44878370 2.8 1.8 1.5E-04 1 79893158 79910727 1 1 0.35

Aspa 10 4.74 ENSRNOSNP2796902 trans 83698648 0.7 2.0 2.4E-09 10 60178512 60199209 1 2 0.352

Popdc2 2 4.59 ENSRNOSNP2785628 trans 51496838 2.2 0.7 1.2E-15 11 64154287 64170282 1 3 0.479

Prmt6 2 4.48 ENSRNOSNP2786609 trans 175912348 1.1 2.0 1.5E-03 2 205909919 205915148 1 1 0.837

Rtn4ip1 10 4.40 ENSRNOSNP313093 trans 47678382 8.0 2.4 5.1E-26 20 47818499 47857741 1 1 0.686

Mapk9 10 4.31 ENSRNOSNP2796840 trans 77352099 15.1 27.2 1.9E-06 10 35344672 35384319 2 9 1.48

Prpsap2 10 4.05 ENSRNOSNP2796516 cis 46015612 5.3 3.8 8.2E-03 10 47890410 47925430 1 1 0.886

Ivns1abp 17 3.96 ENSRNOSNP962219 trans 29903973 24.5 37.2 1.7E-02 13 66223323 66235533 1 6 0.956

Supt4h1 17 3.93 ENSRNOSNP962219 trans 29903973 25.2 18.7 2.4E-02 10 76030806 76036988 1 7 1.11

*Total number of anchors for causality test of the gene. †Number of unique phenotypes that show causal relationship. ‡ The maximum causality score to phenotypes. The eQTL hotspot in RNO17 (ENSRNOSNP962219) is highlighted in bold.

6666 41414141878787875050505020202020

10 56565656747474745858585822222222

4

0

7

2

44 iiiciss 191919191277899 7.8 13333.8.8.88 2.6E-0444 2 191437355

00000 cis 15151 111010100343434444 55.55 6 3.1 8..8..7E7EE-0009 11 1414141 99711111 2289999

73 trtrtrtrananananssss 808080039393939828282271777 00.00 4 1.1.1.4444 7.7.7.7 5E5E5E5E---12222 10110 399994040404075757554040404

24 cis 363636361818188383833 424222 15151515.1.1.11 2727272 .2.22 1.1.1.1.9E9E9E9E---060600 1010100 35344672

777 transsss 37373737959595954545454559999 3.3.3.8888 7.7.7.7 6666 1.1.1.6E6E6E6E----0808080 10101010 56565656745822


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Figure Legends:

Figure 1: Lod plots of representative pQTL. Descriptions of phenotypes are in Table V in the

Data Supplement. (a) Adj BW = length adjusted body weight at 16 weeks (wks) old, (b) Chol =

plasma cholesterol (mM) at 18 wks old, (c) SBP = systolic blood pressure (mmHg) at 14 wks

old, (d) Leptin = plasma leptin (ng/ml) at 18 wks old. Blue line denotes 5% genome-wide

significance level; red line denotes 1% genome-wide significance level.

Figure 2: Genome-genome plot of liver eQTL. The location of genes (y-axis) and their peak

eQTL SNPs (x-axis) are shown accordingly. Red dots denote eQTL above 1% genome-wide

significance; blue dots denote eQTL above 5% genome-wide significance. The red triangle

denotes the location of SNP ENSRNOSNP962219 as an example of eQTL hotspot on RNO17.

Figure 3: qPCR validation of cis-eQTLs and their correlation to MetS traits. (a) Aqp11 in F2 rats

expression grouped by ENSRNOSNP70864 genotype on RNO1. (b) Prcp expression in F2 rats

grouped by ENSRNOSNP2784200 genotype on RNO1. (c) Mapk9 expression in F2 rats grouped

by ENSRNOSNP2796424 genotype on RNO10. (d) RGD1562963 expression in F2 rats grouped

by ENSRNOSNP962219 genotype on RNO17. (e) Gene expression-phenotype correlations for

cis-regulated genes in the F2 rat cohort (N=144). All expression is relative to a standard SD

whole-rat universal reference sample.* P < 0.05.

Figure 4: Co-expression network studies of F2 liver. (a) Multidimensional scaling based on

distance matrix of the expression of all genes in the co-expression network. Genes are colored

(y-axiiiis)s))) andddd tttthehhh irrr ppppeea

d

e e

location of SNP ENSRNOSNP962219 as an example of eQTL hotspot on RNO

qPCR alidation of i eQTLs and their correlation to MetS traits (a) A 11 in F

s (xxxx-a-a-axixixix s)s)s)s aaarrre ssshohhh wn accordingly. Red dodododotstts denote eQTL aabobobobove 1% genome-wid

e;;; bbblue dots deennnotee eeeQTTTTL LLL abovoove 55% geenomome-wiwiwideded sigggnnificannncee.e Theeee rrrededed tttrriannnggle

locaatititionononon of SNSNSNPPPP ENENENNSRSS NONONOOSSSSNP999962262622121212 999 asas aan exxexexammmple ee ofofofof eeQTQTQTQTL LLL hohhohotttstspoottt onon RRRRNNNO

qPPPCRCR lalididatiio fof ii QTQTLLs dnd thheiir lla iti t MMetSS tr iaits ((a)) AA 1111 ii FF by guest on June 8, 2018http://circgenetics.ahajournals.org/

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according to their module color. (b) Gene ontology enrichments for the five largest modules

using all genes used in network as background. (c) Module QTLs (mQTLs). Each bar represents

the number of genes (Lod > 3) regulated by the eQTL hotspot (x-axis label) by their

corresponding module colors. Only mQTLs with at least 50 regulated genes are plotted. Red

arrow denotes mQTL co-localizing with a pQTL.

Figure 5: Turquoise and blue modules have causal relationship with phenotypes. Description of

phenotypes are in Table V in the Data Supplement. (a) Heatmap of correlation coefficients

between phenotypes and eigengenes of turquoise and blue modules. The correlation coefficient

and nominal P value (in bracket) are shown in each cell. P values 0.01 correspond to FDR <

0.1 based on Benjamini-Hochberg FDR correction. (b) Causality scores of blue and turquoise

eigengenes with their correlated phenotypes. Colors represent module colors. Vertical dashed

line denotes the cutoff to infer causal relationships. (c) Causality scores of the cluster of genes

sharing the ENSRNOSNP962219 eQTL hotspot with their correlated phenotypes. For each

phenotype, the causality scores of all eligible genes in one specific module to this phenotype are

shown as a box. Genes in turquoise and blue modules analyzed separately and denoted by color.

The number above each box denotes the number of genes eligible for causality tests (see

Methods in the Data Supplement), and only boxes with at least 20 eligible genes are shown. The

horizontal dotted line denotes the cutoff to infer causal relationships.

Figure 6: Determination of the driver gene for the ENSRNOSNP962219 eQTL from ten putative

cis genes. (a) Lod scores of putative cis-eQTL in the trans-eQTL interval. (b) Haplotypes and

genes located within the eQTL confidence interval. Orange box denotes the eQTL region, and

rrelation coefficientntntnts

he correlalll tititition coeeeeffffffffiiiic

a R

i

with their correlated phenotypes. Colors represent module colors. Vertical dash

s the cutoff to infer causal relationships. (c) Causality scores of the cluster of gen

ENSRNOSNP962219 eQTL hotspot ith their correlated phenot pes For each

al PPP vavavalulululuee (i(i(i(in brbrbrbracket) are shown in eachhhh cccele l. P values 0.0.0.001 correspond to FDR

nnn BBBenjamini-HoHHochhhbeeerg FFFDRRR ccorrrrrectioiion. (b(b) CaCaCaususususalllityyy sscorresss ooof fff blueeee aaanddd turquqquoia

with hhh thhththeieieie r coorrrr llelelatatt ddeded phenononootytytytypes.s CCCColololo orrss rereprpresessesenenenttt momoodudududulelel ccolo orrssss. VVVVerrtititicacal dadadd ssh

s the cutoff to inffffer causall l relatiiiion hshhhipipii s. (((c)c))) CCCCausallilitytyty scores ffoff thhehh cluster of gegg n

ENENSRSRNONOSNSNP9P96262212199 QeQTLTL hhot ot iithh hth iei lelat ded hhe t FF hh by guest on June 8, 2018http://circgenetics.ahajournals.org/

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vertical red dashed line denotes the eQTL peak. The haplotype blocks were generated through

whole genome sequencing of LH and LN strains.10 (c) The number of SNVs (including SNPs

and indels) for each candidate driver gene, including the 5 kb upstream genomic region of each

gene. The genes are ordered according to their genomic locations. (d) Number of regulated trans

genes in this gene cluster by each of the candidate driver genes based on causality tests.

Causality score cutoff of 1 are used to infer the causal relationship.


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KwitekMatthew G. Traxler, Jessica Jakoubek, Santosh S. Atanur, Timothy J. Aitman, Yi Xing and Anne E.

Jinkai Wang, Man Chun John Ma, Amanda K. Mennie, Janette M. Pettus, Yang Xu, Lan Lin,the Metabolic Syndrome in the Lyon Hypertensive Rat

Systems Biology with High-Throughput Sequencing Reveals Genetic Mechanisms Underlying

Print ISSN: 1942-325X. Online ISSN: 1942-3268 Copyright © 2015 American Heart Association, Inc. All rights reserved.

TX 75231is published by the American Heart Association, 7272 Greenville Avenue, Dallas,Circulation: Cardiovascular Genetics

published online January 8, 2015;Circ Cardiovasc Genet.

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1

SUPPLEMENTAL MATERIAL

Supplementary Methods:

Animal phenotyping protocol (See Supplementary Table II):

Body weight was determined weekly in unfasted animals at approximately the same

time of day (3:00 pm ± 1 hour). At the same time as body weight measures, body length

measures including nose-rump and nose-tail were determined weekly to eight weeks of

age, and then at 12, 16 and 18 weeks of age. At 12, 16, and 18 weeks of age, blood

was drawn into EDTA tubes for plasma measures after an overnight fast.

Total plasma triglyceride at 12, 16 and 18 weeks of age were assayed by

Triglyceride Quantification Colorimetric/Fluorometric Kit (BioVision, Milpitas, CA, USA)

and cholesterol was measured by Cholesterol/Cholesteryl Ester Quantitation

Colorimetric/Fluorometric Kit (BioVision, Milpitas, CA, USA). Glucose measures were

determined using a Bayer Contour glucose meter (Bayer Diabetes Care, Tarrytown,

New York, USA). Plasma insulin was measured in the LH and LN parental strains at 12,

16, and 18 weeks of age. However, as no significant variation was determined in the

parental strains (data not shown), insulin was not measured in the F2 offspring. All

biochemical assays were performed under manufacturer’s instructions.

At 12 weeks of age each rat underwent surgery in which a radio telemeter (TA11PA-

C40, Data Sciences International, St Paul, Minnesota, USA) was placed in a

subcutaneous pocket with its 8 cm catheter surgically implanted into the right femoral

artery under anesthesia (ketamine/xylazine). At 14 weeks of age, blood pressure

measures (systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean

pressure (MAP)) and heart rate were measured for 15-second intervals every 5 minutes

over a 72-hour period while the animal was freely moving in a cage placed over a

receiver pad. The rats were then given a 4% NaCl diet (Teklad 7913 modified with 4%

NaCl) for three weeks and the radiotelemetry measurements were repeated as above at

17 weeks of age.

At 18 weeks of age, the animals were fasted overnight, euthanized according to

approved guidelines using CO2 and tissues including liver, kidney, heart left ventricle,

perirenal and gonadal fat pads, brown fat, and skeletal muscle were harvested and

stored in RNAlater (Life Technologies, Grand Island, N.Y.) at -80°C for subsequent RNA

extractions. Wet weights were measured for white and brown fat pads. Spleen was

snap-frozen for future DNA isolation.

2

Phenotype data analysis

Blood pressure and heart rate measures were averaged hourly over the 72-hour

measurement period at either 14 or 17 wks of age. Because of an interest in how

phenotypes progress over time, and because experimental variables may change

across time points, each time point was considered an independent phenotype, with the

exception of determination of the area under the grown curve. Differences between

parental LH and LN and F2 rats were determined for each phenotype by ANOVA,

followed by Holm-Sidak post-hoc tests for all pairwise comparisons. If trait did not meet

normal distribution criteria, non-parametric ANOVA was performed (Kruskal-Wallis test)

followed by Dunn’s post-hoc tests. A P < 0.05 was considered significant.

Generation of a custom SNP chip

A custom, 1536-SNP Illumina Goldengate genotype chip was designed

representing a subset of over 20,000 SNPs whose genotypes were previously

determined in over 150 inbred rat strains, including the LH and LN strains,1 with an

average resolution of 2 Mb across the genome. 453 of these SNPs (N=453) were

specifically selected for the panel to tag each haplotype block determined to be

divergent between LH and LN strains2 (Supplementary Table I). The remaining SNPs

were selected to be polymorphic in other strains undergoing genetic studies in the

laboratory and to maintain a 2 Mb resolution across the genome.

Sample selection and preparation for RNA-seq

The selection of the 36 F2 offspring was based on phenotype, to enrich for animals

that were the most divergent for MetS traits. For each phenotype measured in the F2

offspring, a cohort mean was calculated. Subsequently, a Z-score was calculated for

each F2 rat for all phenotypes to identify animals that were at the extremes of the trait

distributions. Animals enriched for phenotype extremes across all traits were selected

for RNA-seq.

RNA was isolated using standard TRIzol methods.3 RNA quality was determined

via a (BioAnalyzer 2100, Agilent Technologies, Santa Clara, CA, USA), using an RIN

threshold of 7. mRNA-seq libraries were prepared with TruSeq RNA Sample

Preparation Kits v2 (Illumina, San Diego, CA) according to manufacturer’s instructions.

RNA sequencing was performed on an Illumina HiSeq 2000, with paired-end 51 bp

3

cycles, at the University of Iowa DNA sequencing facility. Each lane of 6 multiplexed

samples yielded approximately 180 million reads, or ~30 million reads per sample.

RNA-seq data analysis

Tophat (version 1.4.1)4 was used to map RNA-seq reads to rat Ensembl transcripts

(release 66) first allowing up to 1 mismatch, and the reads that were not fully mapped to

the transcriptome were mapped to rat rn4 Ensembl genome allowing up to 2

mismatches. About 65% of read pairs were uniquely mapped (Supplementary Figure I),

and subjected to downstream analyses. Gene expression was normalized by

determining FPKM values calculated using Cufflinks (version 2.0.0).5 edgeR (version

3.0.0)6 was used to identify differentially expressed genes (DEGs) between LH and LN

strain, and genes with Benjamini-Hochberg corrected FDRs smaller than< 0.05 were

considered as differentially expressed. Gene ontology studies of DEGs were conducted

using DAVID.7 Functional categories with Benjamini-Hochberg FDRs smaller than< 0.05

were considered significantly enriched.

QTL mapping

pQTL mapping was performed using R/qtl.8 The genetic locations of the markers

were estimated by Haldane’s mapping function. The normality of the quantitative traits

in pQTL was tested by Shapiro-Wilk test to determine the use of non-parametric (‘np’) or

parametric (EM) linkage analysis. For traits with P values < 0.05 in the Shapiro-Wilk

test, the “np” (non-parametric) model was used to perform genome scanning, while for

other traits with normal trait distribution the expectation-maximation (EM) algorithm

(parametric) was used to scan for genotype/phenotype relationships.9 Correction for

multiple testing, performed by permutation testing (x 1000 permutations), identified

genome-wide 5% (suggestive) and 1% (significant) Lod significant thresholds as

implemented in the R/qtl software package,10 with a 1.5 Lod-drop confidence interval

indicated by the closest SNPs flanking the confidence interval.

eQTL was mapped by using the R/eqtl package11 on top of R/qtl. Specifically, all

expressed genes (FPKM > 0 in at least one sample) were used in an R/qtl scan as if

they were normally distributed traits after normalization using the FPKM approach. QTL

regions were identified using R/eqtl. As with multiple testing correction in the pQTL

mapping, genome-wide 5% and 1% Lod significanceGenome-wide (corrected) Lod

thresholds were determined by first performing permutation testing (X 1,000

4

permutations) on 36 randomly selected expression traits according to Churchill and

Doerge10 to generate the representative null distribution, and then determinefollowed by

determination of the 95% (for 5% global significance threshold) or 99% (for 1% global

significance threshold) upper-bound from the traits distribution.10, 11 A 1.5 Lod drop-

support interval was determined with a maximum width of 50cM. Cis-eQTLs were

defined as eQTL with peak SNP located within 2 Mb flanking regions of the

corresponding regulated genes.

Causality testing model:

For each causality test, anchor SNPs (M), gene expression (trait A) and phenotypes

or another genes expression (trait B) are used to test if trait A causally affects trait B (A

→ B). To this end, a local SEM (Structural Equation Model) based method is used to

assess the fits of 5 models: M → A → B, A ← B ← M, A ← M → B, M → A ← B, and A

→ B ← M. Of these 5 models, M → A → B is the causal model, which implies the

anchor SNP marker has causal effect on gene expression, which in turn has causal

effect on phenotypes.12 The causality scores (LEO.NB.SingleMarker) are the log10 of the

probability of causal model divided by the probability of next best alternative model.12 A

causality threshold was set at 0.3, consistent with previous reports.13-15

Network construction:

To build co-expression networks, WGCNA software was used to compute a pair-

wise Pearson correlation matrix, and then an adjacency matrix was calculated by raising

the correlation matrix to a power of 10, determined using the WGCNA built-in function,

to meet the scale-free topology criterion.16 The blockwiseModules function in WGCNA

was performed to construct the network for entire data set. Topological overlap based

dissimilarity was used for average linkage hierarchical clustering, then the clustering

tree was cut into branches using hybrid dynamic tree-cutting. The modules were defined

as the branches of the clustering tree with at least 30 gene members and height of >

0.25 (Supplementary Figure II).

qPCR:

Total RNA was extracted from liver tissue from all available F2 rats (N=144) with

TRIzol reagent (Life Technologies, Grand Island, N.Y.) and cDNA was prepared (iScript

cDNA synthesis kit, BioRad Life Sciences, Hercules, CA) according to manufacturer

recommendations. Quantitative real-time PCR (qPCR) was carried out utilizing 5’

5

nuclease assays (PrimeTime Standard qPCR Assay, Integrated DNA Technologies,

Inc., Coralville IA) containing hydrolysis probes and primers for RGD1562963

(Rn.PT.53a.35814531), β-Actin (Rn.PT.39a.22214838.g), Aqp11 (Rn.PT.56a.7710326),

Prcp (Rn.PT.56a.37325111), and Mapk9 (Rn.PT.56a.9657756.g) according to

manufacturer recommendations. No SNVs between LH and LN strains were present in

the primer or probe sequences. Gene Expression (∆Ct) was normalized with -Actin,

and relative quantification (2-∆∆Ct) was calculated using cDNA reversed transcribed from

a Sprague Dawley (SD) universal rat RNA (1ug/ml, Bio Chain) as a reference biological

sample. Gene expression differences between groups were determined by ANOVA

followed by post-hoc tests for pairwise significance compared to the LH parental control.

Correlations between gene expression and phenotypes were determined using

Spearman correlation, with nominal P values of < 0.05.

6

Supplementary table I. LH and LN SNPs in pQTL analyses.

Ensembl ID rs ID Chr (rn4)

Genetic Position (cM)

Genome Position (rn4)

Reference Allele

Alternate Allele

ENSRNOSNP123063 rs106770876 1 0.00 20,115,155 T C

ENSRNOSNP2783480 NA 1 24.30 42,970,573 T C

ENSRNOSNP2783493 rs107457854 1 25.50 44,878,370 T C

ENSRNOSNP2783596 rs105583965 1 35.63 67,895,144 T C

ENSRNOSNP238794 rs106435301 1 36.24 69,719,765 C T

ENSRNOSNP2783609 rs106083246 1 36.24 70,692,604 T C

ENSRNOSNP241977 rs106837898 1 38.24 72,294,125 A T

ENSRNOSNP2783624 rs65586488 1 39.92 73,279,922 A G

ENSRNOSNP243378 rs106800609 1 39.92 74,460,828 T C

ENSRNOSNP2783632 rs105616687 1 39.92 75,769,029 A T

ENSRNOSNP245875 rs106130948 1 40.22 76,624,472 A G

ENSRNOSNP247530 rs104903310 1 40.22 78,125,034 G T

ENSRNOSNP2783678 rs107460276 1 41.72 81,092,885 T C

ENSRNOSNP251676 rs63944245 1 41.72 82,552,544 C A

ENSRNOSNP2783702 rs65375061 1 42.32 83,910,244 G C

ENSRNOSNP2783811 rs106258197 1 55.43 100,156,400 T C

ENSRNOSNP2783818 rs106179812 1 56.63 101,444,028 T C

ENSRNOSNP2783937 rs13455595 1 62.76 118,784,190 A G

ENSRNOSNP2783980 rs105351682 1 67.83 123,235,931 T C

ENSRNOSNP2783985 rs105001389 1 68.42 124,825,851 A G

ENSRNOSNP45308 rs107564878 1 78.57 136,552,144 G A

ENSRNOSNP2784080 rs107137975 1 78.88 137,749,064 G A

ENSRNOSNP2784091 rs65359478 1 78.88 138,406,217 C T

ENSRNOSNP57124 rs65935225 1 82.59 144,307,816 T A

ENSRNOSNP63663 rs107379369 1 86.62 149,258,049 C A

ENSRNOSNP64597 rs106376519 1 86.62 150,096,169 T A

ENSRNOSNP2784200 rs106749520 1 86.62 151,110,344 C T

ENSRNOSNP70383 rs106018237 1 88.43 154,061,178 C T

ENSRNOSNP70864 rs13458429 1 88.43 154,627,781 G A

ENSRNOSNP71772 rs8157023 1 88.73 155,612,106 C T

ENSRNOSNP75240 rs107571159 1 90.54 160,129,372 A T

ENSRNOSNP123669 rs8143500 1 117.10 202,010,692 A C

ENSRNOSNP125138 rs8156467 1 118.60 203,934,819 G A

7

ENSRNOSNP2784717 rs106535573 1 119.19 204,573,537 C T

ENSRNOSNP2784727 rs106785456 1 120.09 206,413,402 T G

ENSRNOSNP2784737 rs105143241 1 120.38 207,503,859 C T

ENSRNOSNP2784743 NA 1 120.38 208,010,733 T C

ENSRNOSNP2784752 rs65883814 1 120.38 209,238,631 T C

ENSRNOSNP149777 rs13452948 1 161.57 247,350,344 A G

ENSRNOSNP2785026 rs105054039 1 161.57 248,118,570 A G

ENSRNOSNP151407 rs8175083 1 162.16 249,210,876 A G

ENSRNOSNP2785240 rs65192640 2 0.00 5,920,810 G T

ENSRNOSNP1422108 rs106665797 2 0.30 7,032,124 A G

ENSRNOSNP2785254 rs106881009 2 0.30 9,917,276 A C

ENSRNOSNP2785268 rs66223561 2 0.89 11,685,054 T G

ENSRNOSNP2785272 rs13453846 2 0.89 12,363,339 A T

ENSRNOSNP1178365 rs104960254 2 1.19 13,099,235 T A

ENSRNOSNP2785285 rs105697694 2 1.48 14,167,043 A T

ENSRNOSNP2785291 rs64140537 2 1.48 15,512,223 G T

ENSRNOSNP2785299 rs106048371 2 1.48 17,372,105 T C

ENSRNOSNP2785309 rs106919233 2 3.36 18,830,279 T C

ENSRNOSNP1297981 rs105504101 2 6.23 21,524,501 C A

ENSRNOSNP1303786 rs13448914 2 6.53 22,013,014 A G

ENSRNOSNP2785392 rs107519613 2 10.07 26,082,111 G A

ENSRNOSNP2785400 rs106622761 2 10.96 26,922,760 T C

ENSRNOSNP1362220 rs106938352 2 11.56 27,649,780 A T

ENSRNOSNP2785497 rs106480153 2 20.99 38,285,110 G A

ENSRNOSNP1392962 rs105010773 2 28.54 49,683,422 C A

ENSRNOSNP2785620 rs65189319 2 28.83 50,772,526 C T

ENSRNOSNP2785628 rs8147216 2 28.83 51,496,838 A G

ENSRNOSNP2785657 rs64325710 2 31.27 53,676,108 C T

ENSRNOSNP2785665 rs107495379 2 31.56 55,302,717 T C

ENSRNOSNP2785670 rs105459747 2 31.86 56,113,665 G A

ENSRNOSNP2785679 rs64950094 2 32.75 57,457,967 G A

ENSRNOSNP2785684 rs105223048 2 32.75 57,885,280 T C

ENSRNOSNP2785688 rs13456588 2 33.05 58,497,110 G A

ENSRNOSNP2785843 rs107194753 2 39.84 75,962,301 T C

ENSRNOSNP2785860 rs105195551 2 40.86 77,529,301 A C

8

ENSRNOSNP2785895 rs107362207 2 42.23 81,853,967 A G

ENSRNOSNP1442847 rs105623791 2 42.55 82,582,378 C T

ENSRNOSNP1455708 rs106854360 2 45.58 91,137,459 T C

ENSRNOSNP1457121 rs105865989 2 45.58 91,961,687 A G

ENSRNOSNP2785965 rs106512311 2 46.17 92,611,675 C T

ENSRNOSNP2786243 rs106781749 2 56.77 126,534,709 G A

ENSRNOSNP1179242 rs105284506 2 57.36 131,966,723 A C

ENSRNOSNP2786254 rs105853187 2 57.96 133,866,697 T C

ENSRNOSNP2786261 rs106449110 2 57.96 134,548,184 T G

ENSRNOSNP1183919 rs106782036 2 57.96 135,450,210 T C

ENSRNOSNP1186624 rs65421613 2 57.96 136,533,768 A G

ENSRNOSNP2786284 rs107559642 2 58.25 137,573,602 T C

ENSRNOSNP2786291 rs66394748 2 58.55 138,601,331 A C

ENSRNOSNP2786304 rs105631640 2 59.14 140,572,749 T C

ENSRNOSNP1193636 rs106549588 2 60.04 141,185,733 A G

ENSRNOSNP1194771 rs107157688 2 60.33 142,368,463 A G

ENSRNOSNP1195854 rs106487501 2 60.94 143,151,102 G A

ENSRNOSNP2786609 rs13457335 2 75.84 175,912,348 C T

ENSRNOSNP2786635 rs105040547 2 76.88 177,969,703 G A

ENSRNOSNP1249616 rs13455971 2 77.92 179,808,287 C T

ENSRNOSNP2786668 rs13448800 2 77.92 180,850,106 A G

ENSRNOSNP1251191 rs106246317 2 77.92 181,838,143 C T

ENSRNOSNP2786735 rs64909320 2 79.42 190,383,702 C T

ENSRNOSNP2786744 rs105308270 2 79.42 191,277,899 C G

ENSRNOSNP1267625 rs8169914 2 79.75 192,382,797 G A

ENSRNOSNP1270948 rs13457102 2 81.40 195,173,973 C T

ENSRNOSNP2786811 rs63922710 2 81.57 196,312,011 G A

ENSRNOSNP1276270 rs106716534 2 83.07 200,614,452 G A

ENSRNOSNP1277020 rs13452901 2 83.07 201,204,555 C T

ENSRNOSNP1279976 rs13457288 2 83.37 202,490,468 G A

ENSRNOSNP1280617 rs66127962 2 83.96 203,403,562 G A

ENSRNOSNP2786910 rs104938443 2 84.86 205,216,548 A G

ENSRNOSNP2786922 rs107455419 2 85.98 206,534,306 T C

ENSRNOSNP1287796 rs105983380 2 86.35 208,362,643 A G

ENSRNOSNP2786943 rs106600504 2 87.24 209,680,193 A G

9

ENSRNOSNP2786950 rs107119053 2 87.83 210,708,786 A G

ENSRNOSNP2786974 rs13456154 2 89.03 212,648,983 G T

ENSRNOSNP2786982 rs65678463 2 89.33 213,692,443 G T

ENSRNOSNP2786994 rs107279445 2 91.45 215,265,692 G A

ENSRNOSNP2787010 rs105219423 2 92.05 216,945,691 A G

ENSRNOSNP1314694 rs107464428 2 102.68 226,787,712 T A

ENSRNOSNP2787143 rs105881737 2 104.49 229,961,860 G A

ENSRNOSNP2787153 rs106088213 2 104.79 231,255,760 T G

ENSRNOSNP1324965 rs13449719 2 110.55 234,410,732 T C

ENSRNOSNP2787208 rs13453633 2 112.05 236,317,353 T C

ENSRNOSNP1330830 rs107019908 2 113.55 238,293,093 C T

ENSRNOSNP1332945 rs8158480 2 113.55 239,719,177 C T

ENSRNOSNP1334580 rs106701684 2 113.85 240,727,362 G A

ENSRNOSNP2787260 rs106695069 2 115.05 241,642,296 T C

ENSRNOSNP2787376 rs13451356 2 121.92 252,756,348 C T

ENSRNOSNP2787387 rs66254132 2 122.81 254,504,430 G A

ENSRNOSNP2787400 rs104902078 2 124.94 255,426,094 G A

ENSRNOSNP2787414 rs105087078 2 125.23 256,981,558 T A

ENSRNOSNP2787568 rs105357257 3 0.00 17,273,405 G A

ENSRNOSNP1605071 rs105985649 3 0.29 18,532,686 G A

ENSRNOSNP1606733 rs107580542 3 0.29 19,624,961 C T

ENSRNOSNP2787599 rs106929875 3 1.19 20,676,398 T C

ENSRNOSNP1609761 rs105229217 3 1.81 21,232,434 A T

ENSRNOSNP2787653 rs105285606 3 9.35 28,286,643 T C

ENSRNOSNP2787657 rs105405533 3 9.35 28,983,889 T C

ENSRNOSNP1618854 rs65203326 3 9.64 29,857,569 A C

ENSRNOSNP2787673 rs13457227 3 10.65 30,757,572 G A

ENSRNOSNP2787682 rs106425548 3 12.35 31,676,756 G A

ENSRNOSNP2787688 rs106997035 3 13.24 32,556,027 A G

ENSRNOSNP2787692 rs13453730 3 13.24 33,622,318 C T

ENSRNOSNP1628296 rs8169763 3 19.34 39,116,716 T C

ENSRNOSNP1661114 rs107559557 3 49.19 68,379,702 G T

ENSRNOSNP2788022 rs106406355 3 49.49 68,863,429 A G

ENSRNOSNP2788083 rs13448419 3 50.92 76,362,194 G C

ENSRNOSNP2788092 rs63846853 3 51.49 77,204,141 T C

10

ENSRNOSNP1676406 rs8172240 3 52.13 78,111,520 G C

ENSRNOSNP1677504 rs106679028 3 53.44 79,485,433 C T

ENSRNOSNP1679545 rs8170221 3 54.41 81,020,079 A C

ENSRNOSNP2788136 rs65093390 3 54.41 82,226,113 A C

ENSRNOSNP1683184 rs106895726 3 54.73 84,607,710 G A

ENSRNOSNP2788152 rs105855287 3 55.03 86,014,696 A G

ENSRNOSNP1687579 rs8157077 3 55.62 87,853,385 C A

ENSRNOSNP2788178 rs63940713 3 56.22 88,878,291 A G

ENSRNOSNP2788182 NA 3 56.52 89,537,978 T A

ENSRNOSNP2788188 rs107242049 3 56.52 90,496,734 T A

ENSRNOSNP2788193 rs64330976 3 56.82 91,362,648 A G

ENSRNOSNP2788488 rs106275602 3 79.87 127,329,819 G A

ENSRNOSNP2788633 rs13449756 3 102.57 146,943,779 A C

ENSRNOSNP2788641 NA 3 102.57 148,023,496 T C

ENSRNOSNP2788649 rs64095878 3 104.36 148,841,495 A G

ENSRNOSNP1582102 rs107250284 3 107.24 149,869,423 A G

ENSRNOSNP2788683 rs13457888 3 111.17 154,316,878 A G

ENSRNOSNP2788799 rs107105643 3 125.63 167,757,730 A G

ENSRNOSNP2788805 rs106804614 3 126.23 168,436,931 A G

ENSRNOSNP1786268 rs66435800 4 0.00 29,179,025 G A

ENSRNOSNP2789065 rs105186013 4 3.08 33,438,806 T C

ENSRNOSNP1802541 rs64614610 4 10.30 42,490,803 G A

ENSRNOSNP1805015 rs65679406 4 10.89 44,222,969 A G

ENSRNOSNP2789174 rs106615432 4 11.19 46,371,328 C T

ENSRNOSNP1814053 rs65597057 4 13.96 50,046,123 G A

ENSRNOSNP2789247 rs106838405 4 19.72 56,264,967 T C

ENSRNOSNP2789270 rs107102897 4 21.22 57,996,038 G A

ENSRNOSNP2789277 rs105141329 4 21.22 58,526,205 C A

ENSRNOSNP2789947 rs105019221 4 75.99 140,255,677 G C

ENSRNOSNP2789980 rs64173702 4 78.11 143,848,958 T C

ENSRNOSNP2789993 rs105142075 4 78.70 145,301,811 G A

ENSRNOSNP2790620 rs64710337 5 0.00 38,032,935 G A

ENSRNOSNP1981750 rs106708813 5 0.58 39,290,316 A G

ENSRNOSNP1994261 rs107134470 5 6.27 47,566,697 G A

ENSRNOSNP1995340 rs66106242 5 6.89 48,320,078 C T

11

ENSRNOSNP1996318 rs105447238 5 7.83 49,120,145 C T

ENSRNOSNP2790905 rs105760469 5 21.33 70,387,267 G A

ENSRNOSNP2790916 rs105488151 5 21.93 71,275,122 C A

ENSRNOSNP2790927 rs106811357 5 21.93 72,351,484 G A

ENSRNOSNP2045323 rs63922053 5 28.05 88,484,679 C T

ENSRNOSNP2791227 rs105279327 5 36.34 105,434,525 G A

ENSRNOSNP2791239 rs105246591 5 36.34 106,861,431 A G

ENSRNOSNP2791242 rs106555283 5 36.34 107,100,313 C T

ENSRNOSNP2791423 rs105048931 5 47.30 126,129,324 G A

ENSRNOSNP1911860 rs64154258 5 48.22 127,023,735 G A

ENSRNOSNP2791445 rs13449331 5 48.81 128,323,772 C T

ENSRNOSNP2791453 rs13454701 5 49.41 129,011,779 G A

ENSRNOSNP2791470 rs106454466 5 50.00 130,615,592 T C

ENSRNOSNP2791611 rs13452947 5 60.63 145,702,445 T C

ENSRNOSNP2791616 rs13457523 5 60.94 146,121,190 G A

ENSRNOSNP2791638 rs107222715 5 63.38 147,795,279 A C

ENSRNOSNP2791648 rs13450644 5 65.89 149,155,027 C T

ENSRNOSNP2791690 rs105538466 5 72.19 154,007,725 G C

ENSRNOSNP1940802 rs63913493 5 75.02 156,547,853 G A

ENSRNOSNP2791722 rs8164558 5 75.02 157,041,150 T C

ENSRNOSNP2791925 rs64594327 6 0.00 8,879,010 A G

ENSRNOSNP2791931 rs8143705 6 0.59 10,449,349 C T

ENSRNOSNP2792401 rs65542101 6 92.83 76,311,471 G A

ENSRNOSNP2792411 rs65031474 6 93.42 77,447,888 A C

ENSRNOSNP2181336 rs107198143 6 94.01 82,242,142 G C

ENSRNOSNP2792457 rs107469843 6 94.31 83,907,076 A G

ENSRNOSNP2792483 rs105443411 6 95.20 87,411,605 G A

ENSRNOSNP2792485 rs64531053 6 95.50 87,871,847 C T

ENSRNOSNP2792495 rs64562153 6 95.50 89,223,086 G A

ENSRNOSNP2792509 rs105166381 6 95.79 90,656,232 C G

ENSRNOSNP2101697 rs106040284 6 145.24 129,528,331 T C

ENSRNOSNP2792973 rs13457847 6 154.46 143,322,902 T A

ENSRNOSNP2792997 rs106329145 6 161.04 146,584,242 G A

ENSRNOSNP2792998 rs106728103 6 161.33 147,563,183 C T

ENSRNOSNP2793128 rs105646864 7 0.00 21,906,237 A G

12

ENSRNOSNP2273929 rs105628872 7 3.72 26,077,499 T A

ENSRNOSNP2793256 rs105748687 7 20.26 36,688,099 C T

ENSRNOSNP2304361 rs106565851 7 36.50 53,525,339 A T

ENSRNOSNP2306631 rs105228185 7 39.81 57,625,500 T G

ENSRNOSNP2793401 rs105119072 7 40.11 58,278,784 T A

ENSRNOSNP2793407 rs105534902 7 40.11 59,291,326 A G

ENSRNOSNP2793913 rs64158997 7 96.76 121,711,167 G A

ENSRNOSNP2794056 rs13450908 7 111.70 134,623,555 C T

ENSRNOSNP2794062 rs106509700 7 112.29 135,040,810 A G

ENSRNOSNP2391513 rs106009046 8 0.00 2,958,785 C T

ENSRNOSNP2794173 rs107084268 8 0.00 3,334,241 A C

ENSRNOSNP2794177 rs106176824 8 0.60 4,245,056 T C

ENSRNOSNP2794182 rs107513229 8 0.89 5,807,484 C T

ENSRNOSNP2794200 rs63890187 8 1.80 7,693,832 C T

ENSRNOSNP2794235 rs107174395 8 7.25 13,030,536 A T

ENSRNOSNP2794297 rs8172893 8 22.26 27,926,394 T C

ENSRNOSNP2794298 rs64675414 8 22.45 27,936,911 A C

ENSRNOSNP2391530 rs66013904 8 24.78 29,595,411 T C

ENSRNOSNP2393344 rs8159626 8 25.38 31,175,883 C A

ENSRNOSNP2794416 rs64909114 8 32.58 42,955,796 G A

ENSRNOSNP2410287 rs107155024 8 32.85 43,348,444 C T

ENSRNOSNP2794483 rs107454630 8 42.52 50,193,853 A G

ENSRNOSNP2420644 rs105893052 8 45.67 53,917,510 A G

ENSRNOSNP2794530 rs13450981 8 45.98 54,970,317 T G

ENSRNOSNP2794551 rs106252354 8 47.20 56,965,810 G A

ENSRNOSNP2794552 NA 8 47.20 57,050,768 T G

ENSRNOSNP2794568 rs106623587 8 48.10 58,352,800 A G

ENSRNOSNP2794599 rs8161355 8 49.30 62,013,644 C T

ENSRNOSNP2429107 rs107496406 8 49.67 63,767,815 G T

ENSRNOSNP2794636 rs105462351 8 49.67 65,265,217 A G

ENSRNOSNP2794650 rs106604606 8 50.74 66,866,467 T C

ENSRNOSNP2794663 rs65070495 8 52.49 70,156,352 A G

ENSRNOSNP2795058 rs107143218 8 116.76 111,144,561 T C

ENSRNOSNP2369819 rs13448281 8 119.23 115,388,381 C T

ENSRNOSNP2795126 rs107561209 8 119.52 116,491,411 G A

13

ENSRNOSNP2795137 rs107214993 8 121.67 117,770,897 C T

ENSRNOSNP2795146 rs64168176 8 121.67 118,910,620 G A

ENSRNOSNP2795435 rs106672675 9 0.00 20,708,657 C A

ENSRNOSNP2795443 rs105659399 9 0.88 21,257,038 T A

ENSRNOSNP2795479 rs105434440 9 6.65 26,700,782 C T

ENSRNOSNP2503128 rs105725261 9 7.25 27,351,512 C T

ENSRNOSNP2505023 rs107172204 9 8.15 28,447,654 A T

ENSRNOSNP2534827 rs106909950 9 29.03 63,966,251 G A

ENSRNOSNP2535749 rs106223872 9 29.33 64,655,722 A G

ENSRNOSNP2795744 rs107156793 9 30.25 65,813,870 C T

ENSRNOSNP2795799 rs105125797 9 37.92 73,186,288 G C

ENSRNOSNP2795817 rs63842729 9 37.92 74,612,156 A G

ENSRNOSNP2795827 rs106714627 9 38.52 76,058,997 A G

ENSRNOSNP2795839 rs106304110 9 40.05 77,183,776 G A

ENSRNOSNP2553075 rs105322922 9 43.84 82,395,988 G A

ENSRNOSNP2795922 rs107246213 9 47.62 88,898,109 G A

ENSRNOSNP2795928 rs106201124 9 47.92 89,692,461 G A

ENSRNOSNP2560431 rs8163467 9 50.04 92,428,288 T C

ENSRNOSNP2795960 rs105179825 9 51.54 93,743,746 A G

ENSRNOSNP2795965 rs107009078 9 52.43 94,495,899 G T

ENSRNOSNP2796023 rs105023681 9 62.25 104,287,010 G C

ENSRNOSNP2796034 rs8167131 9 63.14 105,531,821 C G

ENSRNOSNP2796046 rs66399512 9 64.00 106,385,988 T C

ENSRNOSNP2796068 rs13450835 9 66.41 110,221,111 G A

ENSRNOSNP2796075 rs105967180 9 66.41 111,114,599 G A

ENSRNOSNP2796186 rs13454451 10 0.00 13,622,523 C T

ENSRNOSNP276060 rs13454279 10 0.00 13,944,627 C T

ENSRNOSNP2796224 rs106380754 10 1.51 15,667,747 C T

ENSRNOSNP2796230 rs106518483 10 1.81 16,508,520 G A

ENSRNOSNP278656 rs105564673 10 3.04 18,460,765 G A

ENSRNOSNP2796250 rs13449203 10 3.04 18,915,217 C T

ENSRNOSNP2796354 rs65761120 10 18.23 28,464,947 G T

ENSRNOSNP2796405 rs105623883 10 29.87 33,584,564 T A

ENSRNOSNP2796417 rs65528655 10 32.63 35,329,229 C T

ENSRNOSNP2796424 rs65894318 10 33.53 36,183,842 C T

14

ENSRNOSNP2796429 rs105745689 10 34.12 36,970,925 G A

ENSRNOSNP2796437 rs63776226 10 34.71 37,954,559 A G

ENSRNOSNP2796455 rs105160280 10 35.61 39,541,848 G T

ENSRNOSNP306167 rs106176106 10 36.20 41,019,050 C T

ENSRNOSNP2796474 rs106704272 10 36.50 41,517,398 G A

ENSRNOSNP2796516 rs106021433 10 39.25 46,015,612 C T

ENSRNOSNP313093 rs13457224 10 40.15 47,678,382 G C

ENSRNOSNP2796566 rs63992150 10 40.95 48,470,940 G A

ENSRNOSNP2796578 rs105623009 10 41.64 49,338,801 T G

ENSRNOSNP316059 rs66382342 10 42.04 50,841,613 T C

ENSRNOSNP2796606 rs65740014 10 42.34 52,177,540 G A

ENSRNOSNP2796615 rs66299363 10 42.93 53,670,113 T C

ENSRNOSNP318514 rs107301123 10 43.83 54,629,237 C A

ENSRNOSNP2796630 rs106656260 10 43.83 55,055,714 T C

ENSRNOSNP320109 rs8162042 10 44.12 56,691,083 C G

ENSRNOSNP2796662 rs107457956 10 44.12 57,261,171 A C

ENSRNOSNP321734 rs107276651 10 44.42 58,629,743 C A

ENSRNOSNP323059 rs8158245 10 45.61 60,099,713 C T

ENSRNOSNP332998 rs8143284 10 51.02 72,005,676 A C

ENSRNOSNP333806 rs8165152 10 51.61 73,186,058 A G

ENSRNOSNP2796821 rs105881436 10 52.50 74,850,066 A G

ENSRNOSNP2796827 rs107175393 10 53.10 75,516,973 G A

ENSRNOSNP2796833 rs105048371 10 53.99 76,346,247 A T

ENSRNOSNP2796840 rs106502079 10 54.29 77,352,099 G A

ENSRNOSNP2796857 rs107234941 10 57.04 78,676,498 C T

ENSRNOSNP2796873 rs66060136 10 58.55 80,398,271 G T

ENSRNOSNP2796884 rs107072643 10 60.67 81,609,982 G C

ENSRNOSNP2796902 rs63789249 10 61.87 83,698,648 A G

ENSRNOSNP2796984 rs106536158 10 70.99 92,012,234 C A

ENSRNOSNP2797065 rs65250213 10 79.70 100,931,353 T G

ENSRNOSNP2797075 rs105929075 10 81.20 102,258,625 A G

ENSRNOSNP2797082 rs105024739 10 81.50 102,845,949 C T

ENSRNOSNP2797116 rs64055175 10 85.26 107,081,025 A G

ENSRNOSNP2797129 rs63908111 10 86.46 108,586,083 A G

ENSRNOSNP2797262 rs105382727 11 0.00 20,360,040 A T

15

ENSRNOSNP383621 rs105327764 11 3.43 26,267,064 G T

ENSRNOSNP2797332 rs106394358 11 4.63 27,509,653 G A

ENSRNOSNP386496 rs64509900 11 4.63 28,599,528 A G

ENSRNOSNP402790 rs64184715 11 19.53 45,306,617 A G

ENSRNOSNP424642 rs8158213 11 39.58 69,976,738 A G

ENSRNOSNP425566 rs106095095 11 39.58 70,934,691 G A

ENSRNOSNP427067 rs106672350 11 40.48 72,360,751 T C

ENSRNOSNP436004 rs64199609 11 49.12 81,620,868 T C

ENSRNOSNP440092 rs105531605 11 50.01 84,918,930 T A

ENSRNOSNP441277 rs66423207 11 50.91 86,115,520 G A

ENSRNOSNP443120 rs107514662 11 50.91 87,685,260 G T

ENSRNOSNP460651 rs105159451 12 0.00 2,108,721 C G

ENSRNOSNP2797840 rs106004878 12 0.17 3,502,826 C T

ENSRNOSNP486053 rs107011130 12 2.16 5,775,431 A G

ENSRNOSNP488236 rs105292851 12 3.66 7,375,797 C T

ENSRNOSNP490975 rs106426397 12 9.43 9,649,436 G A

ENSRNOSNP2797916 rs64604372 12 11.87 11,859,337 G A

ENSRNOSNP448836 rs106006189 12 15.12 13,396,034 A G

ENSRNOSNP459208 rs105791310 12 25.21 19,602,323 T A

ENSRNOSNP2797981 rs106429522 12 25.81 20,908,031 C T

ENSRNOSNP2797997 rs106631488 12 25.81 22,027,641 A C

ENSRNOSNP2798012 rs66331812 12 27.93 23,933,587 A G

ENSRNOSNP2798016 rs106655006 12 31.34 24,541,684 A T

ENSRNOSNP2798020 rs106023868 12 31.34 24,946,777 G A

ENSRNOSNP2798030 rs105012997 12 31.34 25,820,593 G A

ENSRNOSNP2798105 rs106485257 12 48.71 33,317,195 A G

ENSRNOSNP472379 rs106684121 12 51.54 36,140,101 G C

ENSRNOSNP2798193 rs65894707 12 57.44 41,252,250 T C

ENSRNOSNP478554 rs8160963 12 58.65 42,394,639 A G

ENSRNOSNP478862 rs106928035 12 58.94 42,780,500 A G

ENSRNOSNP519876 rs106071887 13 0.00 36,215,647 C A

ENSRNOSNP2798495 rs64264626 13 0.29 37,661,709 G T

ENSRNOSNP2798508 rs107270751 13 1.17 39,150,472 G A

ENSRNOSNP2798556 rs107166436 13 7.51 45,258,577 T C

ENSRNOSNP528477 rs105095085 13 10.00 46,672,486 C T

16

ENSRNOSNP2798577 rs65538309 13 10.85 47,278,969 C T

ENSRNOSNP533370 rs105868971 13 15.24 52,795,049 T A

ENSRNOSNP535961 rs107251891 13 16.14 54,356,913 C T

ENSRNOSNP2798659 rs107258954 13 16.73 56,547,619 C T

ENSRNOSNP2798669 rs105369634 13 17.62 58,025,737 A G

ENSRNOSNP558084 rs105285143 13 27.05 69,847,960 C A

ENSRNOSNP558708 rs106953970 13 27.97 70,949,553 G A

ENSRNOSNP563370 rs105971128 13 31.25 74,768,389 G A

ENSRNOSNP2798789 rs106793067 13 31.85 75,866,145 A C

ENSRNOSNP2798801 rs107428093 13 32.14 77,407,572 T C

ENSRNOSNP2799004 rs105541172 13 65.23 107,533,922 T C

ENSRNOSNP614968 rs107329417 14 0.00 19,325,830 G T

ENSRNOSNP2799417 rs106401952 14 20.68 42,606,113 G A

ENSRNOSNP728628 rs106946278 14 67.51 97,788,600 C T

ENSRNOSNP729555 rs65826570 14 67.80 98,199,138 C T

ENSRNOSNP730720 rs106304876 14 68.10 99,020,689 G T

ENSRNOSNP2799895 rs105302527 14 76.07 106,551,326 A G

ENSRNOSNP2799948 rs107243180 15 0.00 198,590 G T

ENSRNOSNP744762 rs106435470 15 0.00 1,173,475 A G

ENSRNOSNP2799960 rs106142624 15 0.00 1,415,651 A G

ENSRNOSNP2799993 rs106845690 15 4.42 9,074,368 A G

ENSRNOSNP2799996 rs107297318 15 5.33 9,538,364 T C

ENSRNOSNP2800146 rs13456030 15 23.22 32,491,831 G T

ENSRNOSNP2800156 rs8162851 15 24.12 33,130,035 T C

ENSRNOSNP764923 rs64715170 15 25.93 37,530,679 T C

ENSRNOSNP766313 rs107086889 15 26.23 38,675,536 G C

ENSRNOSNP768843 rs106377243 15 27.43 40,492,608 T A

ENSRNOSNP2800192 rs63950775 15 27.72 41,079,264 C A

ENSRNOSNP769763 rs105101131 15 28.32 42,636,804 C T

ENSRNOSNP770079 rs63962160 15 28.61 42,982,439 C T

ENSRNOSNP2800299 rs8150132 15 34.71 59,649,651 A G

ENSRNOSNP2800309 rs107014251 15 35.00 60,342,529 G A

ENSRNOSNP2800339 rs64233923 15 38.73 68,203,757 T C

ENSRNOSNP2800343 rs66330146 15 39.62 70,138,472 G A

ENSRNOSNP789810 rs105799128 15 40.22 70,923,321 G A

17

ENSRNOSNP2800534 rs64527952 15 73.36 93,739,684 A G

ENSRNOSNP2800670 rs65628027 16 0.00 569,054 A G

ENSRNOSNP2800677 rs8167567 16 0.92 2,736,374 C T

ENSRNOSNP2800686 rs104920617 16 1.53 3,507,875 A T

ENSRNOSNP2800699 rs65348202 16 2.14 4,761,163 T C

ENSRNOSNP844166 rs105246022 16 13.90 17,271,011 T G

ENSRNOSNP2801031 rs13453641 16 21.03 47,992,265 A G

ENSRNOSNP2801036 rs106421598 16 21.64 48,999,764 G A

ENSRNOSNP909024 rs105601066 16 28.42 57,161,240 A G

ENSRNOSNP910156 rs107128902 16 29.31 58,565,155 C T

ENSRNOSNP2801107 rs13452796 16 36.10 62,532,819 T C

ENSRNOSNP2801124 rs66269567 16 39.50 64,736,777 A G

ENSRNOSNP2801127 rs65906767 16 40.09 65,015,297 C T

ENSRNOSNP962219 rs107291522 17 0.00 29,903,973 C T

ENSRNOSNP2801587 rs65958164 17 0.26 30,176,601 G A

ENSRNOSNP971009 rs107137386 17 8.09 39,428,849 A G

ENSRNOSNP971187 rs105805725 17 8.38 39,585,392 T C

ENSRNOSNP2801703 rs64918204 17 10.50 42,225,476 C T

ENSRNOSNP998840 rs66432968 17 14.23 62,447,199 C T

ENSRNOSNP2801875 rs105935159 17 14.23 62,753,480 A G

ENSRNOSNP2801881 rs105136264 17 14.23 63,660,554 T A

ENSRNOSNP2801895 rs65978719 17 14.23 65,022,528 G A

ENSRNOSNP2801903 rs107297991 17 14.82 65,711,758 C T

ENSRNOSNP2801934 rs65633771 17 15.72 69,768,405 A G

ENSRNOSNP1011183 rs105739016 17 16.61 70,671,232 C A

ENSRNOSNP2802115 rs105876746 17 46.20 90,920,974 C T

ENSRNOSNP2802297 rs64471632 18 0.00 13,973,616 T G

ENSRNOSNP2802356 rs104914175 18 3.39 18,677,622 C T

ENSRNOSNP2802359 rs64233852 18 4.03 19,030,064 T C

ENSRNOSNP2802408 rs65745472 18 7.11 24,666,649 A G

ENSRNOSNP2802422 rs106034809 18 7.41 25,742,671 T C

ENSRNOSNP2802425 rs66025107 18 7.41 26,310,939 A G

ENSRNOSNP2802487 rs105326107 18 8.91 35,061,718 G A

ENSRNOSNP2802496 rs106332231 18 9.21 36,823,716 T C

ENSRNOSNP1067977 rs104916242 18 9.21 38,286,522 C G

18

ENSRNOSNP1068935 rs107288890 18 9.21 40,709,567 G A

ENSRNOSNP2802513 rs107285433 18 9.21 42,410,894 T C

ENSRNOSNP1073148 rs106350687 18 10.10 48,128,997 C T

ENSRNOSNP2802558 rs105518800 18 11.00 49,221,314 C T

ENSRNOSNP1076513 rs106571447 18 11.59 51,069,678 G A

ENSRNOSNP2802582 rs106755091 18 12.49 51,975,049 T C

ENSRNOSNP2802610 rs105754007 18 15.58 54,679,661 C T

ENSRNOSNP1084583 rs8173020 18 25.45 61,931,123 A G

ENSRNOSNP2802665 rs107590158 18 28.51 62,439,211 G A

ENSRNOSNP2802702 rs13457367 18 32.58 66,326,024 G A

ENSRNOSNP2802708 NA 18 33.77 66,983,723 C T

ENSRNOSNP1093597 rs105769865 18 34.07 68,177,312 A G

ENSRNOSNP1097184 rs106162095 18 37.12 71,588,216 C T

ENSRNOSNP2802755 rs106166612 18 37.98 71,998,940 T C

ENSRNOSNP1098139 rs8161561 18 37.98 72,187,971 T C

ENSRNOSNP1100988 rs66080336 18 41.71 76,742,127 G A

ENSRNOSNP2802794 rs106473471 18 43.21 78,021,976 T C

ENSRNOSNP2802808 rs66386457 18 44.10 79,209,590 C T

ENSRNOSNP2802817 rs8170555 18 45.30 80,537,593 G C

ENSRNOSNP2802825 rs8149243 18 45.30 81,524,610 T C

ENSRNOSNP1110362 rs105237290 19 0.00 12,031,156 G A

ENSRNOSNP1113431 rs66356548 19 1.83 13,835,308 C A

ENSRNOSNP1117868 rs105735761 19 2.73 15,089,582 C T

ENSRNOSNP1118363 rs107096943 19 2.73 15,466,429 T C

ENSRNOSNP2803158 rs105886806 19 19.01 38,278,572 C T

ENSRNOSNP2803159 rs105818768 19 19.31 38,643,927 C T

ENSRNOSNP2803248 rs105002316 19 48.58 49,524,696 G A

ENSRNOSNP2803259 rs104995976 19 48.58 49,890,772 C G

ENSRNOSNP2803269 rs8167419 19 50.09 52,040,890 T C

ENSRNOSNP2803271 rs66227830 19 50.09 52,216,500 T C

ENSRNOSNP1477796 rs105935964 20 0.00 15,922,883 T G

ENSRNOSNP1480615 rs105753634 20 2.78 17,705,570 A G

ENSRNOSNP2803718 rs105924531 20 60.79 42,712,815 C T

ENSRNOSNP2803770 rs106898136 20 66.18 48,406,587 C T

ENSRNOSNP2803779 rs106057280 20 66.78 49,189,263 T C

19

Supplementary table II. Phenotyping protocol.

Trait Group Trait

Age of Measure (weeks)

Fasting Plasma Levels

Triglycerides (mM) 12, 16, 18

Total Cholesterol (mM) 12,16,18

Glucose (mg/dL) 12, 16

Leptin (ng/mL) 18

Weight and Adiposity

Maximum slope of growth curve 3-18

Area under the growth curve 3-18

Body weight adjusted by length (g/cm) 12, 16

White adipose fat weight (g) (perirenal + gonadal)

18

Brown fat pad (g) 18

Blood Pressure Systolic blood pressure (mmHg) 14, 17

Diastolic blood pressure (mmHg) 14, 17

Mean arterial pressure (mmHg) 14, 17

Heart rate (bpm) 14, 17

20

Supplementary table III. RNA-seq data coverage and quality.

Sample Number of read pairs

Uniquely mapped

pairs

Uniquely mapping

rate

Mapping rate to

genome

Mapping rate to

junction

Mapping rate to indel

Average base

quality of first end

Average base

quality of second

end

LH rep1 30,072,303 20,291,261 67.5% 52.0% 15.3% 0.1% 38.0 37.9

LH rep2 28,467,348 18,842,585 66.2% 52.2% 13.9% 0.2% 38.0 37.7

LH rep3 28,285,213 17,878,456 63.2% 49.2% 13.8% 0.2% 38.0 37.8

LH rep4 30,433,373 20,014,652 65.8% 50.9% 14.7% 0.1% 38.0 37.8

LH rep5 25,931,689 16,744,991 64.6% 50.8% 13.6% 0.2% 38.0 37.8

LH rep6 28,753,740 18,732,100 65.1% 50.8% 14.2% 0.2% 38.0 37.9

F2 ind1 32,882,772 22,744,144 69.2% 57.8% 11.1% 0.3% 37.9 37.8

F2 ind2 25,890,787 17,583,886 67.9% 55.4% 12.3% 0.2% 37.9 37.7

F2 ind3 32,814,122 21,193,946 64.6% 51.1% 13.4% 0.2% 37.9 37.7

F2 ind4 32,044,754 21,622,993 67.5% 52.7% 14.6% 0.1% 37.9 37.8

F2 ind5 29,093,554 20,417,997 70.2% 58.3% 11.6% 0.2% 37.9 37.9

F2 ind6 23,184,064 16,220,982 70.0% 58.6% 11.1% 0.3% 37.9 37.9

F2 ind7 35,463,997 23,456,891 66.1% 51.7% 14.3% 0.2% 38.1 37.8

F2 ind8 30,202,026 20,164,920 66.8% 51.9% 14.7% 0.1% 38.1 37.8

F2 ind9 22,996,302 14,777,395 64.3% 51.3% 12.7% 0.2% 38.0 37.7

F2 ind10 25,836,707 17,093,047 66.2% 55.3% 10.6% 0.3% 38.0 37.7

F2 ind11 35,755,526 23,287,031 65.1% 51.6% 13.3% 0.2% 38.1 37.8

F2 ind12 32,642,158 21,573,152 66.1% 51.9% 14.0% 0.2% 38.1 37.8

F2 ind13 34,797,345 23,776,715 68.3% 54.8% 13.3% 0.2% 38.1 37.8

F2 ind14 34,285,269 23,625,693 68.9% 58.8% 9.8% 0.3% 38.0 37.7

F2 ind15 27,216,799 17,672,308 64.9% 50.7% 14.1% 0.1% 38.1 37.7

F2 ind16 33,568,657 23,024,539 68.6% 56.8% 11.5% 0.2% 38.0 37.7

F2 ind17 27,238,491 19,166,631 70.4% 59.3% 10.8% 0.3% 38.1 37.8

F2 ind18 24,172,746 15,774,288 65.3% 53.9% 11.1% 0.3% 38.0 37.7

F2 ind19 36,676,008 24,979,873 68.1% 55.5% 12.3% 0.2% 37.6 37.5

F2 ind20 35,112,483 23,140,593 65.9% 53.5% 12.2% 0.2% 37.7 37.6

F2 ind21 37,442,569 24,474,576 65.4% 51.4% 13.8% 0.2% 37.7 37.5

F2 ind22 30,962,162 20,485,275 66.2% 53.1% 12.9% 0.2% 37.6 37.5

F2 ind23 34,961,800 23,255,332 66.5% 54.0% 12.3% 0.2% 37.6 37.5

F2 ind24 27,409,633 18,749,414 68.4% 57.0% 11.1% 0.3% 37.7 37.6

F2 ind25 31,961,846 21,897,220 68.5% 55.1% 13.2% 0.1% 37.5 37.4

F2 ind26 32,583,672 22,410,179 68.8% 55.4% 13.2% 0.2% 37.4 37.3

F2 ind27 36,709,103 24,678,739 67.2% 52.8% 14.3% 0.2% 37.5 37.3

F2 ind28 37,545,277 25,457,167 67.8% 54.3% 13.3% 0.2% 37.4 37.3

21

F2 ind29 36,754,448 25,175,912 68.5% 54.7% 13.6% 0.2% 37.4 37.3

F2 ind30 36,865,491 24,535,566 66.6% 53.3% 13.1% 0.2% 37.5 37.4

F2 ind31 32,388,991 23,934,799 73.9% 63.9% 9.6% 0.3% 37.8 37.4

F2 ind32 31,669,595 21,297,350 67.2% 53.8% 13.3% 0.2% 37.8 37.4

F2 ind33 35,176,286 23,618,962 67.1% 52.4% 14.6% 0.2% 37.8 37.3

F2 ind34 38,446,909 26,019,022 67.7% 54.9% 12.6% 0.2% 37.7 37.4

F2 ind35 35,017,373 24,204,253 69.1% 57.3% 11.5% 0.3% 37.7 37.4

F2 ind36 35,693,000 24,064,006 67.4% 54.7% 12.5% 0.2% 37.8 37.4

LN rep1 40,669,941 27,602,270 67.9% 52.5% 15.2% 0.1% 37.7 37.3

LN rep2 43,255,953 28,936,905 66.9% 52.3% 14.4% 0.2% 37.6 37.3

LN rep3 36,595,922 24,665,905 67.4% 52.3% 15.0% 0.1% 37.7 37.3

LN rep4 30,384,980 20,403,714 67.2% 52.3% 14.7% 0.1% 37.6 37.3

LN rep5 35,640,932 24,844,991 69.7% 58.0% 11.5% 0.2% 37.6 37.3

LN rep6 26,434,650 17,686,806 66.9% 52.5% 14.3% 0.2% 37.7 37.3

22

Supplementary table IV. Traits of LH, LN and F2.

Trait LH (n=9-13) F2 (n=143-171) LN (n=14-20)

MAP 14 wk 122.0 +/- 1.2‡‡‡ 105.3 +/- 0.5 ***, ††† 98.4 +/- 1.3 SBP 14 wk 147.5 +/- 1.4‡‡‡ 124.8 +/- 0.6 ***,††† 114.5 +/- 1.4 DBP 14 wk 100.8 +/- 1.1‡‡‡ 89.0 +/- 0.5 ***,†† 84.9 +/- 1.2 HR 14 wk 326.5 +/- 2.5‡‡‡ 346.4 +/- 1.2 ***, ††† 359.0 +/- 2.7 MAP 17 wk 136.1 +/- 1.7‡‡‡ 109.3 +/- 0.7 ***,††† 99.9 +/- 1.2 SBP 17 wk 161.2 +/- 2.1‡‡‡ 129.4 +/- 0.8 ***,††† 116.0 +/- 1.4 DBP 17 wk 113.9 +/- 1.6 ‡‡‡ 92.0 +/- 0.6 ***,††† 86.1 +/- 1.1 HR 17 wk 319.2 +/- 1.8‡‡‡ 340.7 +/- 1.3 ***,††† 346.5 +/- 2.7 Growth AUC 4102.3 +/- 39.3‡‡‡ 3467.4 +/- 22.3***,††† 2851.2 +/- 35.6 Max Growth Slope 45.1 +/- 0.74‡‡‡ 40.2 +/- 0.45**,††† 35.2 +/- 0.52 Adj. BW 12 wk 15.9 +/- 0.21‡‡‡ 13.4 +/- 0.07***,††† 11.9 +/- 0.12 Adj. BW 16 wk 17.0 +/- 0.21‡‡‡ 14.7 +/- 0.08***,††† 13.2 +/- 0.22 White Adipose 1.43 +/- 0.09‡‡‡ 1.01 +/- 0.02***,††† 0.72 +/- 0.03 Glucose 12 wk 116.5 +/- 5.0‡ 127.8 +/- 2.3† 149.8 +/- 10.8 Glucose 16 wk 141.9 +/- 8.1 133.5 +/- 2.2† 155.5 +/- 7.7 Trig 12 wk 0.56 +/- 0.06 0.48 +/- 0.026 0.47 +/- 0.09 Trig 16 wk 0.43 +/- 0.05 0.51 +/- 0.019 0.40 +/- 0.05 Trig 18 wk 0.91 +/- 0.07‡‡‡ 0.42 +/- 0.018*** 0.39 +/- 0.05 Chol 12 wk 1.54 +/- 0.18 1.63 +/- 0.046††† 1.09 +/- 0.10 Chol 16 wk 1.62 +/- 0.20‡‡ 1.95 +/- 0.034*,††† 1.10 +/- 0.12 Chol 18 wk 2.11 +/- 0.14 2.10 +/- 0.037 1.86 +/- 0.17 Leptin 18 wk 1.68 +/- 0.34‡‡ 0.70 +/- 0.03**,†† 0.35 +/- 0.03

F2 VS LH * p<0.05 ** p<0.01 *** p<0.001

F2 VS LN † p<0.05 †† p<0.01 ††† p<0.001

LH VS LN ‡ p<0.05 ‡‡ P<0.01 ‡‡‡ P<0.001

23

Supplementary table V. Phenotype nomenclature and description.

Phenotype names Description

Leptin 18 wk Plasma Leptin 18 weeks of age

Trig 12 wk Plasma Triglycerides 12 weeks of age



Chol 12 wk Plasma Cholesterol 12 weeks of age



Brown Fat Weight of Brown Fat Pad

Max Growth Slope Maximum slope of the growth curve

Growth AUC Area under the growth curve

Adj BW 12 wk Body weight/body length 12 weeks of age

Adj BW 16 wk Body weight/body length 16 weeks of age

Glucose 12 wk Blood glucose 12 weeks of age

Glucose 16 wk Blood glucose 16 weeks of age

White Adipose Weight of abdominal white adipose pads

SBP 14 wk Systolic Blood Pressure 14 weeks of age

SBP 17 wk Systolic Blood Pressure 17 weeks of age

DBP 14 wk Diastolic Blood Pressure 14 weeks of age

DBP 17 wk Diastolic Blood Pressure 17 weeks of age

MAP 14 wk Mean Arterial Blood Pressure 14 weeks of age

MAP 17 wk Mean Arterial Blood Pressure 17 weeks of age

HR 14 wk Heart Rate 14 weeks of age

HR 17 wk Heart Rate 17 weeks of age

24

Supplementary table VI. pQTL identified in an F2 intercross of LH and LN rats.

Phenotype Chr Peak SNP Peak pos (bp)

Peak pos (cM)

Lod Method Left flank SNP Left flank (bp)

Right flank SNP

Right flank (bp)

RGD QTL

Growth AUC

1 ENSRNOSNP70383

154061178 88.4 5.7 P ENSRNOSNP2783985

124825851 ENSRNOSNP123669

202010692 Bw33, Bw34, Bw35

Growth AUC

2 ENSRNOSNP2785895

81853967 42.2 3.62 P ENSRNOSNP2785497

38285110 ENSRNOSNP2786243

126534709

Adj BW 12 wk

1 ENSRNOSNP45308

136552144 78.6 3.93 P ENSRNOSNP2783937

118784190 ENSRNOSNP123669

202010692 Bw33, Bw34, Bw35

Adj BW 12 wk

10 ENSRNOSNP321734

58629743 44.4 6.47 P ENSRNOSNP2796615

53670113 ENSRNOSNP2796857

78676498

Adj BW 16 wk

1 ENSRNOSNP75240

160129372 90.5 4.86 P ENSRNOSNP2783985

124825851 ENSRNOSNP151407

249210876 Bw33, Bw34, Bw35

Adj BW 16 wk

10 ENSRNOSNP321734

58629743 44.4 5.73 P ENSRNOSNP2796566

48470940 ENSRNOSNP332998

72005676

Chol 16 wk 2 ENSRNOSNP1280617

203403562 84 3.49 NP ENSRNOSNP2786243

126534709 ENSRNOSNP2786994

215265692


73279922 39.9 4.77 NP ENSRNOSNP2783493

44878370 ENSRNOSNP2783811

100156400


53670113 42.9 4.33 NP ENSRNOSNP2796429

36970925 ENSRNOSNP332998

72005676

HR 14 wk 2 ENSRNOSNP1194771

142368463 60.3 5.6 P ENSRNOSNP1362220

27649780 ENSRNOSNP2786609

175912348


142368463 60.3 5.98 P ENSRNOSNP1362220

27649780 ENSRNOSNP2786609

175912348

25


59649651 34.7 3.82 P ENSRNOSNP2800146

32491831 ENSRNOSNP2800534

93739684

Leptin 18 wk

17 ENSRNOSNP1011183

70671232 16.6 4.62 NP ENSRNOSNP2801587

30176601 ENSRNOSNP2802115

90920974

MAP 14 wk 17 ENSRNOSNP2801903

65711758 14.8 3.86 P ENSRNOSNP962219

29903673 ENSRNOSNP2802115

90920974 Bp242, Bp245, Bp247

MAP 17 wk 17 ENSRNOSNP2801903

65711758 14.8 3.78 P ENSRNOSNP962219

29903673 ENSRNOSNP2802115

90920974 Bp242, Bp245, Bp247

SBP 14 wk 17 ENSRNOSNP2801903

65711758 14.8 5.06 P ENSRNOSNP962219

29903673 ENSRNOSNP2802115

90920974 Bp242, Bp245, Bp247

SBP 17 wk 17 ENSRNOSNP2801895

65022528 14.2 6.06 NP ENSRNOSNP962219

29903673 ENSRNOSNP2802115

90920974 Bp242, Bp245, Bp247

Phenotypes described in Supplementary Table V. Traits with normal distribution mapped using parametric analysis (P);

traits without normal distribution mapped using non-parametric analysis (NP). 1.5-lod drop Confidence intervals denoted

by nearest left and right flank SNPs. Rightmost column are QTL annotated in RGD from previous LH x LN mapping

study.17

26

Supplementary table VII. Mapped traits and supporting evidence of finally identified

MetS related genes.

Gene Name Maker name of peak Traits mapped to

eQTL

Phenotypes in

knock-out mice

Traits of GWAS

hits

Aqp11 ENSRNOSNP70864 Adj BW 12 wk,

Growth AUC, Adj

BW 16 wk

Fatal renal failure,

increase HDL

cholesterol18, 19

Pigl ENSRNOSNP2796578 Chol 18 wk, Adj BW

16 wk, Adj BW 12

wk

Body mass index,

subcutaneous

fat20

Aqp11 ENSRNOSNP45308 Adj BW 12 wk,

Growth AUC, Adj

BW 16 wk

Fatal renal failure,

increase HDL

cholesterol18, 19

RGD1562963 ENSRNOSNP962219 SBP 14 wk, SBP 17

wk, SBP 17 wk,

MAP 14 wk, MAP

17 wk

Iah1 ENSRNOSNP962219 SBP 14 wk, SBP 17

wk, SBP 17 wk,

MAP 14 wk, MAP

17 wk

Neurl4 ENSRNOSNP2796827 Chol 18 wk, Adj BW

16 wk, Adj BW 12

wk

Pex11b ENSRNOSNP2786744 HR 17 wk, HR 14

wk, Chol 16 wk

Prcp ENSRNOSNP2784200 Adj BW 12 wk,

Growth AUC, Adj

BW 16 wk

Reduces body

weight and

attenuates the

metabolic effects

of diet-induced

obesity21, 22

27

LOC303140 ENSRNOSNP2796873 Adj BW 16 wk, Adj

BW 12 wk

Mapk9 ENSRNOSNP2796424 Chol 18 wk, Adj BW

16 wk

Neurl4 ENSRNOSNP2796437 Chol 18 wk, Adj BW

16 wk, Adj BW 12

wk

Phldb3 ENSRNOSNP2783493 Chol 18 wk

Aspa ENSRNOSNP2796902 Adj BW 12 wk

Popdc2 ENSRNOSNP2785628 HR 17 wk, Growth

AUC, HR 14 wk

Prmt6 ENSRNOSNP2786609 HR 17 wk, HR 14

wk, Chol 16 wk

Blood pressure23

Rtn4ip1 ENSRNOSNP313093 Chol 18 wk, Adj BW

16 wk, Adj BW 12

wk

Echocardiograpy24

Mapk9 ENSRNOSNP2796840 Chol 18 wk, Adj BW

16 wk, Adj BW 12

wk

Prpsap2 ENSRNOSNP2796516 Chol 18 wk, Adj BW

16 wk, Adj BW 12

wk

Ivns1abp ENSRNOSNP962219 SBP 14 wk, SBP 17

wk, SBP 17 wk,

MAP 14 wk, MAP

17 wk

Lipoproteins,

VLDL25

Supt4h1 ENSRNOSNP962219 SBP 14 wk, SBP 17

wk, SBP 17 wk,

MAP 14 wk, MAP

17 wk

The eQTL hotspot in RNO17 (ENSRNOSNP962219) is highlighted in bold.

28

Supplementary table VIII. SNVs in transcription factor binding sites in promoter of

RGD1562963.

SNP Chr

SNP Position

Ref Allele

Alt Allele

LH LN Factor binding

Binding site start

Binding site end

17 29730769 A C CC AA AP-2 29730762 29730771

17 29730769 A C CC AA E1 29730763 29730772

17 29730769 A C CC AA Sp1 29730765 29730775

17 29730769 A C CC AA NF-1 29730769 29730779

17 29730892 A G AA GG C/EBPalpha 29730887 29730896

17 29730892 A G AA GG GR 29730888 29730897

17 29730892 A G AA GG MyoD 29730889 29730901

17 29730892 A G AA GG Sp1 29730889 29730902

17 29730892 A G AA GG CACCC-binding_f

29730890 29730899

17 29730892 A G AA GG E1 29730891 29730900

17 29731772 C T TT CC Sp1 29731760 29731774

17 29731772 C T TT CC C/EBPalpha 29731767 29731779

17 29731772 C T TT CC GATA-1 29731769 29731778

17 29731772 C T TT CC Ftz 29731770 29731779

17 29731772 C T TT CC Eve 29731770 29731779

17 29731772 C T TT CC YY1 29731771 29731780

17 29732202 C T CC TT C/EBPalpha 29732197 29732208

17 29732202 C T CC TT AP-1 29732201 29732210

17 29732742 T C TT CC PHO2 29732737 29732746

17 29732742 T C TT CC Oct-1 29732738 29732747

17 29732742 T C TT CC GATA-1 29732740 29732749

17 29732801 A G AA GG Oct-1 29732792 29732801


17 29732801 A G AA GG HSE-binding_pro

29732797 29732806

17 29732801 A G AA GG ICSBP 29732801 29732810

17 29733150 C T CC TT GR 29733144 29733153

17 29733150 C T CC TT C/EBPalpha 29733145 29733154

17 29733150 C T CC TT NF-1 29733145 29733154

17 29733150 C T CC TT NF-1 29733147 29733156

17 29733150 C T CC TT COUP 29733148 29733157

29

17 29733150 C T CC TT T3R-alpha 29733149 29733158

17 29733165 G A AA GG C/EBPalpha 29733160 29733169

17 29733165 G A AA GG YY1 29733160 29733169

17 29733165 G A AA GG ICSBP 29733163 29733172

17 29733165 G A AA GG TBP 29733163 29733172

17 29733165 G A AA GG HNF-3B 29733164 29733173

17 29733378 G A AA GG Sp1 29733372 29733381

17 29733378 G A AA GG Sp1 29733378 29733389

17 29733530 T C CC TT COUP 29733522 29733531

17 29733530 T C CC TT Sp1 29733522 29733531

17 29733530 T C CC TT NF-kappaB1

29733528 29733537

17 29733530 T C CC TT NF-kappaB 29733528 29733537

17 29733613 T G TT GG REV-ErbAalpha

29733607 29733616

17 29733613 T G TT GG COUP 29733607 29733616

17 29733613 T G TT GG GAL4 29733608 29733617

17 29733613 T G TT GG Elf-1 29733608 29733617

17 29733613 T G TT GG NF-kappaB 29733610 29733619

17 29733613 T G TT GG Sp1 29733612 29733622


17 29734002 A G AA GG Oct-1 29733994 29734007

17 29734002 A G AA GG Oct-6 29733999 29734008

17 29734132 T C TT CC GATA-1 29734123 29734132

17 29734132 T C TT CC c-Jun 29734125 29734134

17 29734132 T C TT CC Sp1 29734131 29734140

17 29734193 A G AA GG T3R 29734186 29734195

17 29734193 A G AA GG SRF 29734188 29734197

17 29734193 A G AA GG Sp1 29734189 29734198

17 29734193 A G AA GG COUP 29734190 29734199

17 29734193 A G AA GG GATA-1 29734192 29734201

17 29734213 C T CC TT E2 29734207 29734216

17 29734300 T C TT CC TEC1 29734291 29734300

17 29734300 T C TT CC GATA-1 29734292 29734301

17 29734300 T C TT CC Sp1 29734298 29734309

17 29734300 T C TT CC PU.1 29734299 29734308

17 29734966 G A GG AA NF-1 29734958 29734967

30

17 29734966 G A GG AA NF-1 29734960 29734969

17 29734966 G A GG AA Oct-1 29734962 29734971

17 29734966 G A GG AA GATA-1 29734964 29734973

17 29735098 G A AA GG YY1 29735092 29735101

17 29735098 G A AA GG TEC1 29735092 29735101

17 29735098 G A AA GG c-Jun 29735096 29735105

17 29735098 G A AA GG AP-1 29735097 29735106

17 29735108 A G AA GG USF 29735099 29735108

17 29735108 A G AA GG CPC1 29735100 29735109

17 29735108 A G AA GG CREB 29735100 29735109

17 29735108 A G AA GG HOXB8 29735100 29735109

17 29735108 A G AA GG AP-1 29735100 29735109

17 29735108 A G AA GG GATA-1 29735104 29735113

17 29735108 A G AA GG repressor_of_CA

29735108 29735117

The SNPs in conserved promoter regions are highlighted in bold.

31

Supplementary figure I. Uniquely mapped rates of 48 RNA-seq libraries. The libraries

from left to right are LH rep1 to LH rep 6, F2 ind1 to F2 ind36 and LN rep1 to LN rep6.

32

Supplementary figure II. Cluster dendrogram of genes used in network construction.

33

Supplementary figure III. Heatmap of differentially expressed genes between LH and LN.

Z score scaled FPKM values of differentially expressed genes are color-coded

according to the legend. The genes and samples are both clustered by average linkage

hierarchical clustering using 1-Pearson correlation coefficient as the distance metric.

The significant GO (gene ontology) terms for LH up-regulated genes (bottom) and LH

down-regulated (top) gene are shown on the right side.

34

Supplementary figure IV. The heatmap of correlation coefficients between 23

phenotypes and eigengenes of all modules. The correlation coefficient and P value (in

bracket) are shown in each cell.

35

Supplementary figure V. UCSC Genome browser (Rn4 assembly) view of eQTL region

shows variation in putative regulatory regions of RGD1562963. The top panel shows

sequence variants and insertion/deletions in and flanking RGD1562963 along with

regions of vertebrate conservation. The bottom panel represents three single nucleotide

variants (SNVs) in conserved regions that are candidates for functional variants.

36

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systems biology with high-throughput sequencing...

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