Hong Jiao,1,2 Agné Kulyté,3 Erik Näslund,4 Anders Thorell,4,5 Paul Gerdhem,6,7

Juha Kere,1,2 Peter Arner,3 and Ingrid Dahlman3

Whole-Exome Sequencing SuggestsLAMB3 as a Susceptibility Gene forMorbid ObesityDiabetes 2016;65:2980–2989 | DOI: 10.2337/db16-0522

Identification of rare sequencing variants with a largerfunctional impact has the potential to highlight newpathways contributing to obesity. Using whole-exomesequencing followed by genotyping, we have identified alow-frequency coding variant rs2076349 (V527M) in thelaminin subunit b3 (LAMB3) gene showing strong asso-ciation with morbid obesity and thereby risk of type2 diabetes. We exome-sequenced 200 morbidly obesesubjects and 100 control subjects with pooled DNA sam-ples. After several filtering steps, we retained 439 obesity-enriched low-frequency coding variants. Associationsbetween genetic variants and obesity were validated se-quentially in two case-control cohorts. In the final analy-sis of 1,911 morbidly obese and 1,274 control subjects,rs2076349 showed strong association with obesity (P =9.67 3 1025; odds ratio 1.84). This variant was also asso-ciated with BMI and fasting serum leptin. Moreover,LAMB3 expression in adipose tissue was positively cor-related with BMI and adipose morphology (few but largefat cells). LAMB3 knockdown by small interfering RNA inhuman adipocytes cultured in vitro inhibited adipogene-sis. In conclusion, we identified a previously not reportedlow-frequency coding variant that was associated withmorbid obesity in the LAMB3 gene. This gene may beinvolved in the development of excess body fat.

Obesity is a common chronic disease with a strong hereditycomponent; it is a major contributor to the global burdenof chronic disease such as type 2 diabetes (1). Genome-wide

association studies (GWAS) have revolutionized the geneticanalysis of multifactorial traits and led to the successfuldiscovery of numerous genetic risk markers for obesity (2).However, large gaps in our understanding of the heredityimpact on obesity remain. First, identified single nucleotidevariants (SNVs) associated with obesity tend to have a minorimpact on disease risk, thereby complicating the charac-terization of underlying functional genetic variants andpathophysiology. Second, together, identified genetic lociexplain ,3% of variation in BMI in the population (2).

In an attempt to identify additional susceptibility geneloci for complex disease, researchers have begun to applythe recently developed high-throughput sequencing tech-nology, which has shown power to detect low-frequencyand rare disease-causing variants by deep sequencing ofall known exons (3,4). A few gene variants have been shownto associate with severe obesity (5,6). Whole-exome sequenc-ing (WES) might be most efficient when applied to patientswith more extreme forms of common diseases such as morbidobesity for a number of reasons. First, rare and low-frequencyvariants must have a high penetrance to allow statistical de-tection. Second, sequencing variants in the protein codingsequence might be more likely to have a stronger impact onphenotypes than variants in intergenic regions (e.g., knownmonogenic or major gene causes of obesity are associatedwith a severe phenotype and major impact on BMI) (7).

In this study, we used WES to detect low-frequencygene variants enriched in adult subjects with morbid

1Department of Biosciences and Nutrition, Science for Life Laboratory, KarolinskaInstitutet, Stockholm, Sweden2Clinical Research Centre, Karolinska University Hospital, Huddinge, Sweden3Lipid Laboratory, Department of Medicine, Huddinge, Karolinska Institutet,Stockholm, Sweden4Department of Clinical Sciences, Danderyd Hospital, Karolinska Institutet, Stock-holm, Sweden5Department of Surgery, Ersta Hospital, Karolinska Institutet, Stockholm,Sweden6Department of Orthopaedics, Karolinska University Hospital, Stockholm, Sweden7Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden

Corresponding authors: Hong Jiao, hong.jiao@ki.se, and Ingrid Dahlman, ingrid.dahlman@ki.se.

Received 24 April 2016 and accepted 12 July 2016.

This article contains Supplementary Data online at http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db16-0522/-/DC1.

© 2016 by the American Diabetes Association. Readers may use this article aslong as the work is properly cited, the use is educational and not for profit, and thework is not altered. More information is available at http://www.diabetesjournals.org/content/license.

2980 Diabetes Volume 65, October 2016

OBESITY

STUDIES

obesity and validated them against never obese elderlyadults. We believed that it would be possible to enrich forobesity genes among cases and to filter away such genesin the control subjects. In this study, we report a low-frequency obesity-associated variant in the coding regionof the laminin subunit b3 (LAMB3) gene.

RESEARCH DESIGN AND METHODS

Sample SelectionObese and control subjects were recruited since 1993through local advertisement for the purpose of studyinggenes regulating body weight or in association with plannedvisits to our medical or surgical units for morbid obesityor scoliosis, respectively. Subjects were examined in themorning after a night fast, and anthropometric measure-ments were performed. Serum leptin was measured byELISA (Mercodia, Uppsala, Sweden).

At present, the total sample set consists of ;2,400nonobese and ;4,300 obese subjects. From the obese sub-set, we selected, among the 10% with the highest BMI,200 subjects with extreme obesity for WES. We have per-formed two rounds of the sequencing, each containing100 obese samples. The 100 subjects in the first exami-nation were reported previously (6). The results pre-sented in this study included WES data from all 200 obesesubjects. As control subjects, we used 100 subjects withidiopathic scoliosis who were sequenced using the samelayout setting for WES from a genetic study (8). For thefirst genotyping validation, we selected the 484 morbidlyobese subjects with the highest BMI in the large samplecollection described above, including the 200 subjectswho had been used in WES and 491 never-obese subjectswith age .40 years and BMI always ,30 kg/m2. In thesecond validation, we genotyped 1,427 morbidly obesesubjects and 783 never-obese control subjects with age .40years and BMI always ,30 kg/m2.

All investigated obese subjects in this study had morbidobesity (i.e., BMI .40 kg/m2). The nonobese control sub-jects for WES had scoliosis. All other control subjects werehealthy according to self-report. There was an overrepre-sentation of women in the studied cohorts because obesewomen are more likely to search for medical advice for theirobesity, and scoliosis is more common among women. Allsubjects were of European ancestry and living in Sweden.The study was approved by the Regional Ethics Committeesof Stockholm, and all subjects gave their informed consentto participation.

LAMB3 mRNA expression in abdominal subcutaneouswhite adipose tissue (WAT) was measured in 114 Swedishwomen without diabetes who were recruited from thegeneral adult population in Stockholm, Sweden; this data-set is described by Dahlman et al. (9). The women dis-played a large interindividual variation in BMI.

DNA Preparation and PoolingGenomic DNA was prepared from peripheral blood mono-nuclear cells using the QiAmp DNA Blood Maxi kit (catalog

number 51194; Qiagen, Hilden, Germany). DNA purity andquality was confirmed by A260/280 ratio .1.8 on Nano-drop (Thermo Fisher Scientific, Waltham, MA) and agarosegel electrophoresis. DNA concentration was measured byQubit (Life Technologies, Stockholm, Sweden). Subsequently,we took 0.8 mg of each DNA sample and randomly dividedthem into 10 pools, each containing 10 samples of eitherobese cases or control subjects. The concentrations of pooledDNA samples were measured with Qubit (Life Technologies)and the samples run on agarose gel.

WESWES was performed at the Science for Life Laboratory(Stockholm, Sweden) as previously descried (6). Briefly, eachDNA library was prepared from 3 mg of the pooled genomicDNA. DNA was sheared to 300 base pairs (bp) using aCovaris S2 instrument (Covaris, Brighton, U.K.) and enrichedby using the SureSelectXT Human All Exon 50 Mb kit(Agilent Technologies) and an Agilent NGS workstation ac-cording to the manufacturer’s instructions (SureSelectXTAutomated Target Enrichment for Illumina Paired-End Mul-tiplexed Sequencing, version A; Agilent Technologies). Theclustering was performed on a cBot cluster generation sys-tem using a HiSeq paired-end read cluster generation kitaccording to the manufacturer’s instructions (Illumina).Samples were sequenced on HiSEq 2500 (Illumina) aspaired-end reads to 100 bp/read. The sequencing runswere performed according to the manufacturer’s instruc-tions. Demultiplexing and conversion were done by usingCASAVA v1.8.2 (http://support.illumina.com/sequencing/sequencing_software/casava.html).

Sequencing Read Mapping, Variant Calling, andFunctional AnnotationWES data processing was described previously (6). Allsequencing reads were first aligned to the human referencegenome assembly hg19/GRCH37 using Burrows-WheelerAligner (BWA version 0.6.1; http://bio-bwa.sourceforge.net/) with a read-trimming parameter quality score of20. Sequence variants were called by using the mpileupfunction of samtools-0.1.18 (http://samtools.sourceforge.net/) with a minimum mapping quality of 20 and read depthbetween 83 and 1,0003 for filtering. PCR duplicates wereremoved using samtools prior to variant calling. Annovar(http://www.openbioinformatics.org/annovar/ [10]) wasused to integrate variant information from public data-bases, including gene reference (http://hgdownload.soe.ucsc.edu/goldenPath/hg19/database/refGene.txt, 2013),Single Nucleotide Polymorphism database (dbSNP; SNP138;http://hgdownload.soe.ucsc.edu/goldenPath/hg19/database/snp137.txt.gz), and the 1000 Genomes Project (http://www.1000genomes.org/).

Variant Filtering and Enrichment for ObesityVariant enrichment for obesity was achieved throughseveral filtering and comparison steps (Fig. 1). The en-richment was performed based on the WES data from20 obese pools against 10 control pools. First, we filtered

diabetes.diabetesjournals.org Jiao and Associates 2981

out nonexonic and synonymous SNVs and kept missenseand nonsense SNVs or SNVs causing potentially splicingchanges. Secondly, we counted appearance frequencies ofSNVs in obese and control pools separately and com-pared differences of the frequencies between the twotypes of pools. We retained SNVs appearing more oftenin obese pools using the following criteria: 1) present inat least two obese pools and 2) not present in any of thecontrol pools. Our focus was low-frequency codingSNVs with potential functional impact. The last filter-ing step was based on minor allele frequency (MAF).With the assumption that a causative variant shouldbe low frequency in the normal population, we filteredfor SNVs that were either not found in public data-bases or known SNVs with an MAF in European originpopulations (MAFEur) #5% (1000 Genomes Project,2011 May release).

Variant Validation and Association StudiesA two-stage validation strategy was used (Fig. 1). In thefirst stage, all obesity-enriched variants from the abovefiltering steps that could fit into a 384-well panel weregenotyped in 484 obese subjects and 491 control subjectsusing the Illumina GoldenGate assay (Illumina) performedat the SNP&SEQ Technology Platform at Uppsala University(http://www.molmed.medsci.uu.se/SNP+SEQ+Technology+Platform/Genotyping/). The results were analyzed usingthe software GenomeStudio 2011.1 from Illumina. For

the second step, the SNVs showing nominal significantassociation (P # 0.05) with obesity in the first stepwere genotyped in additional 1,427 obese and 783 controlsubjects. Genotyping was performed using matrix-assistedlaser desorption/ionization time-of-flight mass spectrom-etry (SEQUENOM; Agena Bioscience, San Diego, CA) atthe Mutation Analysis Core Facility at Karolinska Institu-tet (11). Multiplexed assays were designed using MassAR-RAY Assay Design v4.0 Software (Agena Bioscience). Protocolfor allele-specific base extension was performed according toAgena Bioscience’s recommendation.

Additionally, rs2076349 was validated by using Sangersequencing at Eurofins (http://www.eurofins.com/en.aspx)and TaqMan genotyping (C__22274317_10; Applied Bio-systems, Foster City, CA) to confirm the genotypes of allhomozygote for the rare allele T of the variant and a fewheterozygote subjects.

All genetic association results in validation cohorts1 and 2 have been submitted to GWAS Central.

Fat Cell Studies in a Population of Healthy SubjectsPatients were investigated in the morning after an over-night fast when abdominal subcutaneous WAT was ob-tained by fine-needle aspiration. Adipocytes were isolatedfrom abdominal subcutaneous WAT and adipocytes isolatedas described (12). By comparing the size of the adipocyteswith the total amount of body fat, the morphology ofWAT could be quantitatively determined (delta values),

Figure 1—Overview of working process. MQ, sequencing read mapping quality; unknown (variants), variants that are not included inSNP138.

2982 LAMB3 Is an Obesity-Associated Gene Diabetes Volume 65, October 2016

as described in detail (13). Positive values are indicative ofhypertrophy (few but large fat cells) and negative valuesby hyperplasia (many small fat cells).

Gene ExpressionWAT LAMB3 expression in the population of healthysubjects was assessed by Gene 1.0 and 1.1 ST Affymetrixarrays (Affymetrix) as described (9).

Adipocyte Cell Culture and Small Interfering RNATransfection of Human Mesenchymal Stem CellsIsolation, growth, and differentiation of human mesen-chymal stem cells (hMSCs) were previously described (14).hMSCs were reverse transfected 24 h before induction ofadipogenesis using ON-TARGETplus SMARTpool small in-terfering RNAs (siRNAs) targeting LAMB3 or nontarget-ing siRNA pool (Dharmacon, Lafayette, CO). For theknockdown of gene expression, 6.25 pmol of siRNA (giv-ing a final concentration of 50 nmol/L) was incubatedtogether with 0.35 mL of Dharmafect-3 (Thermo FisherScientific) and medium (final volume of 25 mL) in a 96-wellplate for 30 min at room temperature. Thereafter, 10,000 ofcells in a volume of 100 mL were added. When transfectionwas performed in a 24-well plate, 3.2 mL of Dharmafect-3was mixed with 50 nmol/L of siRNA in a final volume of100 mL following addition of 100,000 cells in a volume of400 mL. Cells were incubated for 24 h in a proliferationmedium without fibroblast growth factor 2, and then adipo-genesis was induced.

The hMSCs were collected at days 2, 7, 9, and 12 afterthe induction of differentiation for isolation of RNA.Total RNA was extracted using NucleoSpin RNA II kit(Macherey-Nagel, Düren, Germany) according to the man-ufacturer’s instructions. Concentration and purity of RNAwere measured using a Nanodrop ND-1000 Spectropho-tometer (Thermo Fisher Scientific). Reverse transcriptionwas performed using the iScript cDNA synthesis kit (Qiagen)and random hexamer primers (Invitrogen, Carlsbad, CA).Quantitative RT-PCR of coding genes was performed usingcommercial TaqMan probes (Applied Biosystems). Gene ex-pression was normalized to the internal reference gene 18s.Relative expression was calculated using the 2(2DD thresh-old cycle) method (15).

Neutral Lipid and DNA Staining in AdipocytesThe hMSCs were transfected and differentiated in 96-wellplates for 12 days as described above, washed with PBS,

and fixed with 4% paraformaldehyde solution containing0.123 mol/L NaH2PO4 3 H2O, 0.1 mol/L NaOH, and 0.03mol/L glucose for 10 min at room temperature. Fixed cellswere washed with PBS. Cell nuclei were stained withHoechst 33342 (2 mg/mL; Molecular Probes, ThermoFisher Scientific), and neutral lipids were stained withBodipy 493/503 (0.2 mg/mL; Molecular Probes) dilutedin PBS for 20 min at room temperature.

After washing, accumulation of intracellular lipid drop-lets and cell number (amount of stained nuclei) werequantified with the Acumen eX3 imager (TTP Labtech,Hertfordshire, U.K.). Total Bodipy (lipid droplet) fluores-cence was normalized to the amount of nuclei in each well(Hoechst staining).

Statistical AnalysesGenetic association analyses and odds ratio (OR) calculationswere performed for A1 (minor alleles) based on genotypesusing PLINK (http://pngu.mgh.harvard.edu/;purcell/plink/).An x2 test was used in statistical analysis. Hardy-Weinbergequilibrium among case and control subjects was checkedseparately for each round of validation prior to the associ-ation analyses. A nominal P value of 0.01 in control sub-jects was used as a cutoff value for exclusion of SNVs fromfurther association analyses. Obesity status was character-ized by BMI and reported as mean 6 SD for obese andcontrol subjects separately (Table 1). Quantitative pheno-types were analyzed using standard linear regression imple-mented via PLINK. Sex and age were used as covariates toevaluate their influence on the association of a variant withBMI. Correction for multiple tests was performed usingPLINK adjust function, with genomic control–correctedp value calculated based on genotypes of all variants inthe final analysis. Standard least square algorithm wasused in regression analysis of mRNA expression in thepopulation of healthy subjects. Student t test was used infat cell functional assays.

RESULTS

Clinical Characteristics of SubjectsAll obese subjects used in WES and validations hadmorbid obesity (BMI .40 kg/m2). Genotyped controlsubjects were chosen from never-obese subjects withage .40 years and BMI ,30 kg/m2. The characteristicsof the studied subjects for WES and validations areshown in Table 1 and Jiao et al. (6).

Table 1—Clinical information on new samples used for whole-exome sequencing and variant validations

Obese cases Control subjects

Nationality Female/male BMI (kg/m2) Age (years) Nationality Female/male BMI (kg/m2) Age (years)

Discovery by WES Swedish 74/26 55.8 6 3.6 41.0 6 11.0 Swedish 76/24 21.9 6 4.3 24.5 6 12.8

First validation Swedish 349/135 51.2 6 3.7 41.0 6 11.6 Swedish 348/143 24.4 6 2.7 55.1 6 5.8

Second validation Swedish 1,069/358 43.1 6 2.5 42.3 6 11.6 Swedish 697/86 23.9 6 2.7 44.9 6 3.9

Total* 1,418/493 1,045/229

Values for BMI and age are mean 6 SD. Control subjects used in the two runs of validations were nonobese. *Nonduplicated counting(i.e., individuals used in the WES were included in the first validation).

diabetes.diabetesjournals.org Jiao and Associates 2983

Identification of Obesity-Associated Low-FrequencyVariants by WESWES generated ;100 million nonduplicated sequencingreads for each of 20 obese pools and 10 control pools(Supplementary Table 1). The vast majority of paired readswere uniquely mapped onto the same chromosome with apairing rate up to 90%. These reads were aligned to thehuman reference genome (hg19/GRCh 37) with mappingrates .95% (except one of the control pools) (Supplemen-tary Table 1), and .90% of the target regions were com-pletely covered with at least 83 depth (SupplementaryTable 2).

Potentially interesting variants were identified throughseveral filtering steps (Fig. 1). Obtained variants weresummarized in Supplementary Table 3. The majority ofthe variants (between 111,589 and 201,653 per pool)were SNVs, of which .20,000 were exonic or at potentialsplicing sites. Our focus was SNVs with functional impact(i.e., missense, stop codon, or potential splicing site var-iants). We therefore filtered for exonic SNVs with theexclusion of synonymous variants. At this stage, wegot .10,000 (10,511–13,297) such functional SNVs fromeach pool (Supplementary Table 3). Next, we filtered forSNVs shared by at least two obesity pools and not presentin any control pools. Finally, after removing common SNVswith MAFEur .0.05 (1000 Genomes Project, http://www.1000genomes.org/), 439 SNVs were retained for thevalidation studies.

SNV Validation and Association AnalysesTwo rounds of validations by genotyping were performed.In the first round, 382 SNVs, out of 439, that successfullypassed quality criterion in primer design (final score.0.4)for the Illumina GoldenGate assay (Illumina) were geno-typed in 484 morbidly obese subjects and 491 never-obeseelderly control subjects. Two additional SNVs (with ascore close to 0.4) were added to fill a 384-well geno-typing panel. Genotyping of 19 SNVs failed; this waslikely due to technical problems with the assay becauseeach SNV had 0% in call rate. For remaining genotypedSNVs, the average sample call rate was 98.6%. Among suc-cessfully genotyped SNVs, eight were nonpolymorphic.Hardy-Weinberg equilibrium was checked prior to associa-tion analysis. After removal of eight more SNVs with skewedgenotypes (P, 0.01), 349 SNVs were used in associationanalysis.

Twenty-five SNVs showed nominal association (P #0.05) with obesity in the first validation (Table 2). Theywere subsequently subjected to a second round of geno-typing in an additional large case-control cohort consist-ing of 1,427 morbidly obese subjects and 783 never-obeseelderly control subjects. In this round, three SNVs failedin genotyping, and one failed in primer design. Out of theremaining 21 SNVs (Supplementary Table 4), rs2076349(P = 0.011 and 0.003 for the first and second validations,respectively) and rs142678624 (P = 0.026 and 0.047, re-spectively) consistently displayed a significant association

with obesity (Supplementary Table 4) in the two roundsof validations. In the final joint data analysis with allgenotyped subjects, rs2076349 showed strong associa-tion with obesity (P = 9.67 3 1025; OR [95% CI], 1.84[1.35–2.53]), whereas the association of rs142678624with obesity was diminished. Moreover, the associationof rs2076349 was demonstrated in both females (P =0.002; OR [95% CI], 1.69 [1.21–0.2.36]) and males (P =0.003; OR [95% CI], 4.19 [1.48–11.85]) (Table 3). Besidesrs2076349, six other SNVs also showed nominal significant(P # 0.05) associations with obesity in the final joint anal-ysis of all genotyped subjects (Table 3).

Additionally, association analyses with quantitative traitswere performed to observe possible influence of rs2076349on obesity-related phenotypes. rs2076349 demonstratedstrong associations with BMI (P = 0.0002; b = 2.8). TheSNV also showed nominal associations with height (P =0.0054; b = 21.66), body weight (P = 0.01; b = 6.00),and fasting serum leptin (P = 0.032; b = 6.93) (Table 4).Homozygous subjects for the rs2076349*T allele hadthe highest average BMI (mean 44.28 kg/m2) when com-pared with subjects who were heterozygous or homo-zygous for the rs2076349*C allele (Supplementary Table5). The T allele was overrepresented in obese subjects(MAF 4%), especially in the homozygous form (10 obesevs. 1 control). To further evaluate any confounding effectof age or sex, we added these variables as covariates inregression models and tested their individual effects sep-arately. The association of rs2076349 with BMI main-tained strong with P = 0.0001 after adjustment by sex.The association between rs2076349 and BMI was withP = 0.0079 after adjustment for age (SupplementaryTable 6). Analysis of association of rs2076349 with waistafter adjustment for BMI was significant with P = 0.003(Supplementary Table 7).

rs2076349 located at 209,800,230 bp on chromosome1 (hg19) is a missense variant (V527M; valine to methio-nine) in the exon 12 (NM_001017402 and NP_001017402)of the LAMB3 gene. The variant is located in the laminin-type epidermal growth factor–like 5 domain of LAMB3. Weused PolyPhen and PredictSNP to predict the possible im-pact of rs2076349 of protein function (16,17). According toPoplyphen, the SNV was predicted to be possibly damaging,with a score of 0.6. According to PredictSNP, which appliesseveral independent algorithms to predict the pathogenicpotential, rs2076349 is with 61–83% likelihood neutral inits effect on protein function.

rs2076349*T homozygous genotypes were confirmedby Sanger sequencing and TaqMan genotyping. The asso-ciation of rs2076349 with obesity maintained significantwith a Bonferroni adjusted P value of 0.002 (Supplemen-tary Table 8).

Correlation of LAMB3 mRNA With BMILAMB3 mRNA expression was investigated in human sub-cutaneous WAT. The gene expression showed strong pos-itive correlation with BMI (r = 0.38; P, 0.0001) (Fig. 2A).

2984 LAMB3 Is an Obesity-Associated Gene Diabetes Volume 65, October 2016

Tab

le2—Asso

ciations

ofSNVswith

obesity

inthe

first

validatio

n

CHR

SNV

A1

Freq_case

Freq_control

A2

Pvalue

OR

L95U95

Gene

1rs45588635

C0.04211

0.01987G

0.0050812.168

1.2463.771

CLC

NKA

1rs11588392

A0.05925

0.02342G

7.00E-05

2.6261.605

4.298CLC

NKB

1rs116377172*

T0.02899

0.01527A

0.039471.924

1.0213.626

SRGAP2

1rs2076349

T0.04029

0.02138C

0.015651.921

1.1223.29

LAMB3

2rs150681136

A0.03926

0.01833G

0.0056682.188

1.243.862

GPC1

2rs34069570*

A0.02583

0.01227G

0.028572.134

1.0664.273

ANO7

3rs11920543

A0.04772

0.02851G

0.026791.707

1.0582.755

IQCB1

4rs7680970

A0.03838

0.06033C

0.025710.6217

0.40810.9472

FAM13A

8rs143039156

A0.03878

0.0224C

0.036051.761

1.0313.007

NKX2–6

8rs80304851

C0.06405

0.04388T

0.04871.491

12.224

PARP10

9rs56200518

A0.02169

0.009202G

0.025332.388

1.0885.24

DAB2IP

10rs61753067

G0.0339

0.01939A

0.047621.775

0.99883.153

HSPA12A

11rs16937251

C0.04307

0.02444G

0.023061.796

1.0772.997

NAV2

14rs61985140*

A0.03512

0.01939G

0.03281.841

1.0433.251

DDHD1

15rs142678624

A0.01346

0.004082G

0.026273.328

1.08110.24

APBA2

15rs143879080

C0.006198

0A

0.01348NA

NA

NA

CYP11A

1

15rs61734378

T0.05475

0.02953C

0.005531.903

1.23.02

AKAP13

16rs61737709

A0.06612

0.04397C

0.032111.539

1.0352.29

TMC5

16rs9928053

A0.02686

0.01327G

0.032222.053

1.0494.019

ACSM5

16rs74029025

C0.02692

0.005102G

0.00012175.394

2.06314.1

PKD1L2

17rs150237469

A0.01653

0.03157G

0.030380.5156

0.28010.9489

PIK3R

6

17rs41298712

G0.04969

0.03163A

0.043591.601

1.012.537

ENDOV

20rs78568430

G0.03209

0.01731C

0.03541.882

1.0353.424

MYT1

22rs56340734*

A0.01349

0.03926G

0.00041110.3346

0.17710.6321

TTC38

Xrs5977625

A0.05529

0.03469G

0.042361.629

1.0132.619

FRMD7

A1,m

inorallele;A

2,alternativeallele;C

HR,chrom

osome;Freq

,frequencies

ofminor

alleleA1;L95,low

er95%

CI;N

A,not

available;

U95,up

per

95%CI.*S

NPsnot

usedin

thesecond

validation.

diabetes.diabetesjournals.org Jiao and Associates 2985

Tab

le3—

Ass

ociations

ofSNVswithobes

ityin

theco

mbined

datafrom

tworoun

dsofva

lidations

CHR

SNV

A1

Freq

_cas

eFreq

_con

trol

A2

Pva

lue

OR

L95

U95

Gen

e

Total

1rs11

5883

92A

0.04

397

0.03

199

G0.01

611.39

1.06

1.82

CLC

NKB

1rs20

7634

9T

0.03

935

0.02

169

C9.67

E-05

1.85

1.35

2.53

LAMB3

8rs80

3048

51C

0.05

708

0.04

437

T0.02

589

1.30

1.03

1.65

LOC10

5375

801

11rs16

9372

51C

0.03

775

0.02

684

G0.01

788

1.42

1.06

1.91

NAV2

15rs61

7343

78T

0.04

806

0.03

275

C0.00

2905

1.49

1.14

1.94

AKAP13

16rs61

7377

09A

0.06

171

0.04

937

C0.03

752

1.26

1.01

1.58

TMC5

16rs99

2805

3A

0.02

417

0.01

657

G0.03

938

1.47

1.02

2.13

ACSM5

Female

1rs11

5883

92A

0.04

545

0.03

182

G0.01

579

1.45

1.07

1.96

CLC

NKB

1rs20

7634

9T

0.04

066

0.02

454

C0.00

2028

1.69

1.21

2.36

LAMB3

8rs80

3048

51C

0.05

989

0.04

11T

0.00

3453

1.49

1.14

1.94

LOC10

5375

801

11rs16

9372

51C

0.03

942

0.02

697

G0.01

785

1.48

1.07

2.05

NAV2

15rs61

7343

78T

0.04

710.03

276

C0.01

238

1.46

1.08

1.97

AKAP13

16rs61

7377

09A

0.06

126

0.04

817

C0.04

836

1.28

1.00

1.65

TMC5

16rs99

2805

3A

0.02

337

0.01

877

G0.27

131.25

0.84

1.87

ACSM5

Male

1rs11

5883

92A

0.03

971

0.03

275

G0.51

711.22

0.67

2.24

CLC

NKB

1rs20

7634

9T

0.03

557

0.00

8734

C0.00

3449

4.19

1.48

11.85

LAMB3

8rs80

3048

51C

0.04

898

0.05

921

T0.41

730.82

0.50

1.33

LOC10

5375

801

11rs16

9372

51C

0.03

292

0.02

62G

0.49

231.27

0.65

2.48

NAV2

15rs61

7343

78T

0.05

081

0.03

275

C0.12

381.58

0.88

2.85

AKAP13

16rs61

7377

09A

0.06

301

0.05

482

C0.54

431.16

0.72

1.87

TMC5

16rs99

2805

3A

0.02

648

0.00

6579

G0.01

254.11

1.24

13.64

ACSM5

Boldface

values

inthetable

indicateP,

0.05

.A1,

minor

allele;A2,

alternativeallele;CHR,ch

romos

ome;

Freq

,freq

uenc

iesof

minor

allele

A1;

L95,

lower

95%

CI;U95

,up

per

95%

CI.

2986 LAMB3 Is an Obesity-Associated Gene Diabetes Volume 65, October 2016

The expression was also positively correlated with adiposemorphology (r = 0.25; P = 0.008) (Fig. 2B), meaning thathypertrophy (few large cells) was associated with high ex-pression and the other way around for hyperplasia (manysmall cells). These correlations remained unchanged inmultiple regression including age and array batch as in-dependent factors. For 21 women, we had informationabout both LAMB3 genotype and adipose tissue LAMB3expression levels. LAMB3 expression levels did not differbetween genotypes (values not shown).

LAMB3 Inhibits AdipogenesisTo study if LAMB3 affected adipogenesis in vitro, its ex-pression was monitored during differentiation of hMSCs.The expression was increased almost twofold (day 13 ofdifferentiation compared with the beginning, day 0) (Fig.3A). To evaluate the possible role of LAMB3 in the differ-entiation of adipocyte cells, the gene was knocked downin vitro using siRNA in the hMSCs 24 h before inductionof differentiation (day 21). Expression levels of LAMB33 days after the transfection of siRNA (day 2 of differen-tiation) were decreased by ;90%. The expression ofLAMB3 gradually recovered during the differentiation,but was still inhibited by 60% at day 12 of differentiation(Fig. 3B). Knockdown of LAMB3 also significantly de-creased expression of adipocyte-specific gene ADIPOQ atdays 7 (P , 0.01) and 9 (P , 0.05) of differentiation (Fig.3B). Differentiation grade was also negatively affected, asevidenced by 15% decreased accumulation of neutral lip-ids (Fig. 3C).

DISCUSSION

This study has identified a previously unreported low-frequency coding SNV, rs2076349 (V527M) in the LAMB3gene, associated with morbid obesity in a Swedish popu-lation using WES followed by genotyping. LAMB3 mRNAin WAT correlated with BMI and adipose morphology,and reduction of LAMB3 expression by siRNA inhibited fatcell differentiation, pointing to a specific role of LAMB3 inadipose tissue.

The minor allele T of rs2076349 was low frequency(MAFEur 2%) in the European population but enriched inobese subjects, especially among those homozygous, whencompared with control subjects (10 vs. 1). Our bioinfor-matics analysis predicted that the LAMB3 V527M couldpossibly be damaging, indicating that it could be a causalmarker for obesity. It is located in a domain of the genefor which function is not defined in detail. To our knowl-edge, rs2076349 has not been reported as a cause of otherdiseases.

LAMB3 forms one subunit of the Laminin 5 (also calledLaminin-332) heterotrimer, which is a component of theextracellular matrix and thought to mediate the organiza-tion of cells into tissues (18). Laminin 5 is typically foundin the dermoepidermal junction of the skin (19). Muta-tions in each of the three subunits of Laminin 5 are pre-sent in patients with junctional epidermolysis bullosa, anautosomal-recessive disease (20,21). Moreover, Laminin5 induces osteogenic gene expression and promotes oste-ogenic differentiation of MSCs (18,22). Laminin 5 is alsoimportant for b-cell function (23) and has a role in var-ious types of cancer (24–26). Whether Laminin 5 orLAMB3 has functions in organs of importance for thecontrol of obesity such as the brain or WAT has notbeen described before.

Adipocyte number and size are the major determinantsfor fat mass, and obese subjects have a higher absoluteproduction of new fat cells and a higher number of fatcells in total (27). About 10% of adipocytes in adult hu-mans are renewed every year, suggesting that adipogene-sis might contribute quite substantially to body fat mass(13,27). In agreement with this, GWAS support a role for

Table 4—Quantitative trait analyses of rs2076349T withdifferent phenotypes in total final validation data

Phenotype NMISS BETA* P value

BMI 3,174 2.803 0.000198

Leptin 1,955 6.926 0.032

Waist 2,726 3.756 0.05541

Weight 3,145 5.986 0.01014

NMISS, number of nonmissing genotypes. *The additive effectsof allele A1.

Figure 2—mRNA expression of LAMB3 in abdominal subcutaneous WAT of 114 Swedish women. Gene expression was compared withBMI (A) and adipose morphology (B) by linear regression analysis. For morphology, positive values indicate hypertrophy and negativehyperplasia.

diabetes.diabetesjournals.org Jiao and Associates 2987

adipogenesis in obesity susceptibility (2). Our observationthat WAT LAMB3 mRNA levels correlate with BMI andthat knockdown of LAMB3 in vitro inhibits of adipogen-esis give additional support to the notion that productionof fat cells is important for various aspects of develop-ment of obesity

The hypothesis underlying our study is that more severeforms of obesity might be due to low-frequency genevariants with a larger impact on the phenotype. A minorportion of morbidly obese individuals has been shown to bedue to genetic variants with high penetrance (7,28). Theassociation of rs2076349 in LAMB3 with obesity providesanother supporting evidence for the notion that low-frequency variants contribute to morbid obesity. One ex-planation why rs2076349 has not been reported before asa cause of obesity is that this SNV has not been assayed incommonly used genome-wide genotyping platforms. An-other explanation is that most GWAS have focused onBMI or common obesity, rather than morbid obesity.However, a noncoding SNP, rs12130212, located distalto the 39 end of LAMB3, has been reported to be associ-ated with extreme obesity in a Danish cohort (29).

One important limitation of the current study isrelatively low sequencing depth and thereby low power

to detect rare (MAF ,1%) genetic variants causing mor-bid obesity in the general population. In contrast, analysisof association between such rare SNVs and obesity re-quires a very large sample size to achieve significance,which is beyond reach using available cohorts of morbidlyobese cases. Another limitation is that although the asso-ciation between SNV rs2076349 and obesity was promi-nent, it did not reach the level of genome-wide statisticalsignificance. However, it is likely that the present level ofassociation, in combination with functional data for thegene, represents a biological relevant link between LAMB3and obesity.

In conclusion, a low-frequency SNV rs2076349 (V527M)in the LAMB3 gene is strongly associated with morbid obe-sity. This gene may be involved in the development of excessbody fat at least in part by controlling adipogenesis and theproduction of new fat cells.

Acknowledgments. The authors thank the mutation analysis facility atthe Karolinska Institutet and the SNP&SEQ Technology Platform at Uppsala Uni-versity for performing genotyping and technical assistance. The authors alsothank the Science for Life Laboratory, the National Genomics Infrastructure,and Uppmax for providing assistance in massive parallel sequencing and com-putational infrastructure; Anna Grauers (Clinical Science, Intervention and

Figure 3—Reduced levels of LAMB3 inhibit adipogenesis. A: Expression levels of LAMB3 were determined using quantitative RT-PCRduring differentiation of hMSCs to adipocytes in vitro. Results were analyzed using the t test and are presented as relative fold change 6SEM vs. negative control of day 0. B: Expression of LAMB3 was knocked down using siRNA in hMSCs in vitro, followed by induction ofdifferentiation, upon which the expression of LAMB3 and ADIPOQ were monitored. Results were analyzed using the t test and are presentedas relative fold change 6 SEM vs. negative control (NegC) of each time point during differentiation. C: Expression of LAMB3 was knockeddown using siRNA in hMSCs in vitro, and accumulation of neutral lipids, as well as number of cells, was evaluated. Results were analyzedusing the t test and are presented as relative fold change 6 SEM vs. nontargeting siRNA pool (siNegC). *P < 0.05, **P < 0.01, ***P < 0.001.

2988 LAMB3 Is an Obesity-Associated Gene Diabetes Volume 65, October 2016

Technology, Karolinska Institutet) for scoliosis sample collection and character-ization; Elisabeth Dungner (Lipid Laboratory, Department of Medicine, Huddinge,Karolinska Institutet) for DNA preparation; and Christel Björk (Lipid Labora-tory, Department of Medicine, Huddinge, Karolinska Institutet) for the reverse-transfection protocol of hMSCs.Funding. The project was supported by grants from Stockholm County (ALF),the Swedish Research Council, Novo Nordisk Foundation, the Diabetes StrategicResearch Program at Karolinska Institutet, Center for Innovative Medicine, andthe Erling-Persson Family Foundation.Duality of Interest. No potential conflicts of interest relevant to this articlewere reported.Author Contributions. H.J. provided study design, data analysis, andwrote the first version of the manuscript. A.K. provided research data. E.N. andA.T. performed sample collection. P.G. provided the scoliosis sample collection.J.K., P.A., and I.D. provided study design. All authors contributed to revision ofthe first version of the manuscript and approved the final version. I.D. is theguarantor of this work and, as such, had full access to all the data in the studyand takes responsibility for the integrity of the data and the accuracy of the dataanalysis.

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