Genetic architecture of lumbar spinal stenosis

Domain: chemistry
Published: 2026-04-06
Editorial outcome: minor_revision

Abstract
Lumbar spinal stenosis (LSS) affects over 100 million people globally, with an increasing
incidence due to an ageing population. While LSS is known to be heritable, its genetic basis
remains poorly understood. We conduct a genome-wide meta-analysis of LSS in 40,303 cases and
741,469 controls. We identify 73 previously unreported loci in addition to 15 known loci, and
highlight spinal degeneration as a key pathogenic mechanism. In 12,784 surgically treated cases,
we discover five loci specifically associated with severe disease. Age-of-onset analyses show that
most variants influence risk after midlife, but some confer susceptibility as early as age 34.
Mendelian randomisation further demonstrates causal effects of higher body mass and fat-free
mass on LSS risk. Overall, our findings expand knowledge of the genetic background of LSS and
inform future translational research.

Introduction
Lumbar spinal stenosis (LSS) narrows the lumbar central spinal canal or foramina, causing
symptoms mainly in the buttocks and lower extremities1,2. The term central stenosis is used if the
whole central spinal canal is compressed, and foraminal or lateral stenosis if the single nerve root
exiting the neural foramina is compressed1. LSS uPsually develops with ageing when lumbar
structures degenerate, including changes in interve rtebral discs, facet joints, ligamentum flavum,
and vertebrae. In addition, spondylolisthesis can cause or aggravate LSS1. Even though the
majority of LSS cases have a degenerative oIrigin, in a smaller subgroup of patients, LSS arises
from developmental or congenital facEtors2,3. Naturally, developmental LSS can predispose
individuals to degenerative LSS as they age3. Developmental causes include a congenitally small
central canal and Paget disease, which leads to atypical bone growth1,3.

In the general population, the majority of radiological LSS cases are asymptomatic. The
prevalence of diagnostic LSST is around 11%, increasing significantly with age1,4. LSS becomes
clinically significant wheRn it causes symptoms affecting daily life. The main symptoms include
discomfort, pain, fatigue, paresthesia, and/or numbness in the lower extremities or the buttocks,
which exacerbate during activities such as walking, frequently called neurological claudication1,2,4.
Low back pain can be present or absent2. The symptoms are typically relieved during sitting or
forward flexion1,2. Diagnosis is based on patient history and symptoms, and clinical examination,
with confirmation through imaging, usually magnetic resonance imaging (MRI) or computed
tomography (CT)1. Non-surgical management, such as physiotherapy, oral medication, and
activity modification, is usually the first-line treatment. Surgical treatment is warranted if the
symptoms are severe or non-surgical care does not improve the symptoms enough1,2. A previous
MRI-based twin study5, estimated the heritability of LSS to be 0.67, indicating a substancial
genetic contribution to LSS risk. Further research is needed to fully understand the hereditary
background of LSS, which is currently poorly characterized. Using data from FinnGen, Estonian
Biobank, and UK Biobank, we perform a genome-wide meta-analysis and identify previously
unreported loci. We also show that examining LSS by utilizing surgical procedure codes provides
further information regarding a more severe form of the disease that requires surgical treatment.

Results
88 loci associated with LSS risk in meta-analysis
In this study, we used the International Classification of Diseases (ICD)-10 code M48.0 to
characterize LSS in the genome-wide meta-analysis. Data from FinnGen, the Estonian Biobank,
and the UK Biobank were analysed, comprising 40,303 cases and 741,468 controls
(Supplementary Data 1). We can presume that diagnoses are symptomatic since they are clinically
determined. Depending on the study cohort, the prevalence of LSS in our study varied from 2.5-
8.4%; in the meta-analysis, it was 5.2%. A total of 88 loci with at least one genome-wide
significant (P<5x10-8) variant were associated with LSS in the meta-analysis. Seventy-three of the
loci were previously unreported (Fig. S1-Fig. S73, Supplementary Data 2). We also successfully
replicated fifteen loci previously associated with LSS, while 5 loci did not replicate (Fig. 1A,
Supplementary Data 2-4, Fig. S74)6–9. Given the observed heterogeneity in some observed effects,
we also performed random-effect meta-analysis as a sensitivity analysis (Fig. S74, Supplementary
Data 4). In the conditional analysis, we also detected secondary signals at five LSS-associated loci
(Supplementary Data 2). In addition, we fine-mapped all loci associated with LSS in the meta-
analysis (Supplementary Data 5). In eight loci there were only one variant in each credible set.
Most of these loci were intronic or intergenic, but we also fine-mapped the same missense variant
rs11556924 near ZC3HC1 (zinc finger C3HC-type containing 1) that we also observed in the meta-
analysis.

Depending on the study dataset used, the LD Escore regression-derived SNP-based
heritability on the liability scale ranged between 0.1969-0.2499, indicating that genetic factors
explain 19.7-25% of the common variation in LSS Prisk (Supplementary Data 6). The genomic
inflation factor lambda GC varied between 1.14-1.5 4 and suggested inflation in the test statistics.
Considering the observed intercept values varNied between of 1.00-1.14, the inflation is likely
attributed to a polygenic signals10. Our heritabIility estimates were higher compared to the previous
SNP-based heritability estimates, which hav e estimated that 10.1-14.9% of the common variation
in LSS risk originates from the genetic factors
(https://nealelab.github.io/UKBB_ldsc/h2_summary_M13_SPINSTENOSIS.html)6.

Meta-analysis focusing on LSS that requires surgery
The sensitivity meta-analysis conducted in FinnGen and the Estonian Biobank focused on LSS
patients who required surgery and identified 32 genome-wide significant loci (Fig. 1A, Fig. S75,
Fig. S76, Supplementary Data 7, Supplementary Data 8). The surgical cases were required to have
procedure codes of Nomesco v1.15: ABC36, ABC56, ABC66, ABC99, NAG61, NAG62, NAG63,
NAG66, NAG67, and NAG99. The surgical meta-analysis consisted of 12,784 cases and 418,246
controls (Supplementary Data 1). We compared the original meta-analysis with the sensitivity
meta-analysis on LSS patients requiring surgery and observed differences in the effect estimates
for 6 variants, indicating larger effect estimates in the surgical cohort (Fig. 1B, Fig. S75,
Supplementary Data 9).

LSS-associated loci observed near genes related to degenerative changes in the spine
LSS is suggested to be caused by degenerative changes in the spine11, and we identified several
potential genes that may account for these degenerative changes. Several loci near genes that
function in biological pathways, such as TGF-, BMP or WNT-signalling pathways, which are
known to play a role in musculoskeletal disorders were identified (Supplementary Data 3). Some
of observed loci have also been associated with other musculoskeletal disorders, such as
osteoarthritis12, lumbar disc herniation13,14, or back pain15,16. The same was observed for some loci
in our PheWAS analysis, where we found that many LSS-associated lead variants were also
associated with other musculoskeletal disorders. On the other hand, some of the associations
appear to be LSS-specific based on our results (Supplementary Data 3). Of particular interest was
the locus close to BMP6 (bone morphogenetic protein 6), the effect estimate of which was also
significantly larger in the LSS surgical meta-analysis compared to the primary LSS meta-analysis
(Fig. 1B, Supplementary Data 9). BMP6 has previously been associated with spondylosis17 and
osteoarthritis18 and has been found to play an important role in bone and cartilage formation and
repair19 and can also induce angiogenesis20. Interestingly, in our study, we also detected other loci
in the close proximity of angiogenesis-related genes, such as ANGPT1 (angiopoietin 1) that has a
role in vascular development and angiogenesis21. Vascular changes are often observed in
association with spinal degeneration; examples of these changes include the association of
angiogenesis to osteogenesis and thus a possible impact on bone homeostasis22, and vascular
ingrowth in intervertebral discs23. Another interesting LSS-associated locus was observed near
CCDC91 (coiled-coil domain containing 91), which has previously been associated with the
ossification of the posterior longitudinal ligament (OPLL)24,25. OPLL is characterized by ectopic
bone formation within the posterior longitudinal ligament in the spinal canal. OPLL may also
contribute to the pathogenesis of LSS, as ossified ligament can compress the spinal cord or nerve
roots26,27. In addition, we identified and replicated previousEly identified loci near genes related to
the nervous system, such as DCC (DCC netrin 1 receptor) and NGF (nerve growth factor), the
latter of which is also already a known potential medical target for pain28. The identification of
nervous system-related genes suggests that, in addition to structural changes, nervous system-
related pathways are potentially involved in the underlying symptomatic LSS and may thus explain
the differences in symptoms experienced by patients. Similar findings have also been made in
GWAS studies investigating the genetic background of LDH13 and chronic back pain29. The loci
we observed in the surgical meta-analysis largely overlapped with those observed in the meta-
analysis. We also observed five loci that were specific to the surgical GWAS, however the effects
of these variants were in the same direction in the original meta-analysis (Fig. S76, Supplementary
Data 7). Furthermore, these associated loci potentially play a role in the same biological pathways
as what we observed in the LSS meta-analysis (Supplementary Data 3).

Functional annotations and colocalizations provide further information about LSS
Twenty-five genes that we identified as potential candidate genes at the novel LSS-associated loci
were also highlighted by the GCTA-fastBAT analysis30 and the MAGMA31 gene-based test
(Supplementary Data 2, Fig. 2A, Supplementary Data 10-11). The pathway responsible for
neurotrophin receptor binding showed the most significant enrichments in the MAGMA gene-set
analysis (Fig. 2B), which used the pFDR<0.05 significance threshold. Gene-set analysis also
highlighted pathways associated with ossification, pathways controlling biosynthetic processes
and differentiation as well as pathways related to vasculature development (Fig. S77).
Additionally, enrichments were found in many gene-sets related to the nervous system, such as the
pathway related to the binding of neurotrophin receptors. There were no tissues identified in the
MAGMA tissue expression analysis (Fig. S77) that exhibited a positive correlation between
associations with LSS and tissue-specific gene expression profiles. The lack of the relevant tissues,
namely bone and cartilage, in the Genotype-Tissue Expression (GTEx) dataset utilized for these
analyses is likely the reason for the null result.

In total, we detected 758 colocalizations (posterior probability [PP] for the shared variant
 0.8) between LSS GWAS and 322 different resources that contained numerous QTL datasets
and other FinnGen R12 endpoints. Unsurprisingly, several colocalizations were observed with
other back-related disorders in FinnGen. For example, LSS association signals on chr12 near gene
SOX5 (SRY-box transcription factor 5) and chr15 near SMAD3 (SMAD family member 3) were
colocalized with association signals of several back-related disorders (Supplementary Data 12).
These findings, together with our PheWAS findings, suggest that some of the biological pathways
involved in the pathogenesis of LSS overlap with other back-related disorders. The LSS
association signal on chr2 near GFPT1 (glutamine-fructose-6-phosphate transaminase 1) and chr7
near IL6 (interleukin 6) colocalized specifically with several immune-related eQTLs. For both
genes, colocalizations were observed monocytes and macrophages, and also with fibroblasts and
tibial artery (Supplementary Data 12).

Disease onset-modifying variants were discovered in survival analyses
LSS usually manifests later in life1. Before our study, an age-of-onset analysis for genetic variants
was not possible since the genetic background of the LSS has not been investigated extensively
and there have been limitations in the availability of large, longitudinal datasets. At each locus
associated with LSS, we examined the differences in the cumulative incidences between
homozygotes for the lead variants and calculated the cumulative incidence of LSS diagnoses.
Around age 40, the number of LSS diagnoses started to accumulate, with the majority of diagnoses
occurring between the ages of 50 and 70. All lead variants showed a similar trend in terms of the
accumulation of LSS diagnoses, with the lead variant genotypes showing only slight variation and
cumulative morbidities following the sample prevalence value (6.88% for LSS in FinnGen,
Supplementary Data 13). Interestingly, we observed that for LSS-associated variant near the
OTUD7B (OTU deubiquitinase 7B) the cumulative incidence differed between homozygotes
already at the age of 34 (p=0.013, Fig. S78, Supplementary Data 13). With the majority of the
other variants, the difference in the cumulative LSS diagnoses between homozygotes manifested
at a later age, but with some variants no significant difference was observed at all (Supplementary
Data 13). We carried out the same analysis in the Estonian Biobank and discovered that diagnoses
were accumulating for the variant in a similar manner to that observed in FinnGen (Fig. S78). We
also performed genome-wide survival association analysis in FinnGen, with the aim to identify
genetic variants associated with LSS age of onset. In this GATE analysis32, we identified 24 loci
associated with age of onset of LSS, 22 of which were loci we identified in GWAS (Supplementary
Data 14). One of the identified variants was the same rs11205303 variant near OTUD7B, further
validating its potential role in LSS onset. Two loci specific to age of onset were found near genes
NCOA1 (nuclear receptor coactivator 1, rs1041837006, p=8.17x10-9) and APOE (apolipoprotein
E, rs429358, p=2.43x10-11). While our findings related to OTUD7B are interesting, based on our
PheWAS and colocalization findings (Supplementary Data 3, Supplementary Data 12), the variant
would be a poor potential target for translational research, as the rs11205303 variant appears to protect
from LSS while increasing the risk of cardiovascular disorders and cancer.

Numerous significant pain-related positive correlations were observed
Genetic correlations were studied between 849 traits and LSS (Fig. 3, Supplementary Data 15).
Back pain (rg=0.58, pFDR=3.01x10-145) showed the most significant positive genetic correlation
in terms of the lowest p-value, and the diagnosis of M54 Dorsalgia (rg=0.71, pFDR=4.60x10-29)
showed the largest significant genetic correlation. There was also a positive genetic correlation
between LSS and many pain-related endpoints, including hip pain (rg=0.54, pFDR=8.43x10-64)
and leg pain on walking (rg=0.54, pFDR=1.26x10-46). Positive genetic correlations were also
observed for general health- and mood-related traits, such as long-standing illness, disability or
infirmity (rg=0.43, pFDR=1.57x10-69) and major depressive disorder (ICD-10 coded) (rg=0.40,
pFDR=1.10x10-24).

Potential causal relationships observed between anthropometric factors and LSS
Prior research has shown associations between anthropometric traits and LSS2,33,34; however, the
causal relationships between these factors have not been thoroughly examined. With the help of
Mendelian randomization, we uncovered that there are also potential causal relationships between
several anthropometric traits and LSS (Fig. 4A, Fig. S79- Fig. S82, Supplementary Data 16,
Supplementary Data 17). The most significant potential causal relationships were observed
between whole body fat-free mass and higher LSS risk (OR=1.724, pFDR=1.11x10-20, Fig. S79)
and between body mass index (BMI) and higher LSS risk (OR=1.553, pFDR=2.12x10-24, Fig.
S80). Our findings related to BMI and hip circumference were consistent with earlier MR research
that looked into the relationship between these traits and LSS35, but our study also indicates
previously unreported potential causal relationships.
The only outcome for which we found LSS to be potentially causal was back pain
(OR=1.027, pFDR=5.92x10-13, Fig. 4B, Fig. S8 3, Supplementary Data 16). However, the
sensitivity analysis with MR Egger (OR=1.025, pFDR=0.3156), was not statistically significant.
The fact that we did not observe significant directional pleiotropy ( Supplementary Data 16)
suggests that the null result we obtained with MR Egger could be the consequence of the known
reduced statistical power of the method compared with IVW. Although pleiotropy was not
observed, some causal estimates were heterogeneous (Supplementary Data 16), and thus, these
results should be interpreted with caution. Due to the observed heterogeneity for some causal
estimates, we conducted further sensitivity analyses and used the weighted median method to
derive causal estimates resilient to the presence of some invalid instruments and leave-one-out
analysis to assess whether any single variant disproportionately influenced the results (Fig. 4A-B,
Supplementary Data 18). These results aligned with our primary analysis performed with IVW.
MRPRESSO36 was also applied to obtain outlier-corrected causal estimates (Fig. 4A-B);
the results
were consistent with the IVW estimates (Supplementary Data 16). We also investigated the
possibility of reverse causality, mapping potential causal relationships for the same traits in the
other direction as well. Back pain (OR=88.140, pFDR=1.94x10-13) was the only trait for which we
observed a statistically significant potential causal relationship in both directions (Supplementary
Data 16). The sensitivity analysis using MR Egger (OR=8.311, pFDR=0.459), however, did not
reach statistical significance. This observed potential two-way causal relationship is intriguing and
may be explained by the fact that those suffering from back pain usually seek treatment and are
diagnosed2.

Methods
Our research complies with all necessary ethical regulations (Supplementary Note). Patients and
control subjects in FinnGen provided informed consent for biobank research, based on the
Finnish Biobank Act. Alternatively, separate research cohorts, collected prior the Finnish
Biobank Act came into effect (in September 2013) and start of FinnGen (August 2017), were
collected based on study-specific consents and later transferred to the Finnish biobanks after
approval by Fimea (Finnish Medicines Agency), the National Supervisory Authority for Welfare
and Health. Recruitment protocols followed the biobank protocols approved by Fimea. The
Coordinating Ethics Committee of the Hospital District of Helsinki and Uusimaa (HUS)
statement number for the FinnGen study is Nr HUS/990/2017.
The FinnGen study is approved by Finnish Institute for Health and Welfare (permit
numbers: THL/2031/6.02.00/2017, THL/1101/5.05.00/2017, THL/341/6.02.00/2018,
THL/2222/6.02.00/2018, THL/283/6.02.00/2019, THL/1721/5.05.00/2019 and
THL/1524/5.05.00/2020), Digital and population data service agency (permit numbers:
VRK43431/2017-3, VRK/6909/2018-3, VRK/4415/2019-3), the Social Insurance Institution
(permit numbers: KELA 58/522/2017, KELA 131/522/2018, KELA 70/522/2019, KELA
98/522/2019, KELA 134/522/2019, KELA 138/522/2019, KELA 2/522/2020, KELA
16/522/2020), Findata permit numbers THL/2364/14.02/2020,
THL/4055/14.06.00/2020,,THL/3433/14.06.00/2020, THL/4432/14.06/2020,
THL/5189/14.06/2020, THL/5894/14.06.00/2020, THL/6619/14.06.00/2020,
THL/209/14.06.00/2021, THL/688/14.06.00/2021, THL/1284/14.06.00/2021,
THL/1965/14.06.00/2021, THL/5546/14.02.00/2020, THL/2658/14.06.00/2021,
THL/4235/14.06.00/202, Statistics Finland (permit numbers: TK-53-1041-17 and
TK/143/07.03.00/2020 (earlier TK-53-90-20) TK/1735/07.03.00/2021, TK/3112/07.03.00/2021)
and Finnish Registry for Kidney Diseases permission/extract from the meeting minutes on 4th July
2019.
The Biobank Access Decisions for FinnGen samples and data utilized in FinnGen Data
Freeze 9 include: THL Biobank BB2017_55, BB2017_111, BB2018_19, BB_2018_34,
BB_2018_67, BB2018_71, BB2019_7, BB2019_8, BB2019_26, BB2020_1, Finnish Red Cross
Blood Service Biobank 7.12.2017, Helsinki Biobank HUS/359/2017, HUS/248/2020, Auria
Biobank AB17-5154 and amendment #1 (August 17 2020), AB20-5926 and amendment #1 (April
23 2020) and it´s modification (Sep 22 2021), Biobank Borealis of Northern Finland_2017_1013,
Biobank of Eastern Finland 1186/2018 and amendment 22 § /2020, Finnish Clinical Biobank
Tampere MH0004 and amendments (21.02.2020 & 06.10.2020), Central Finland Biobank 1-2017,
and Terveystalo Biobank STB 2018001 and amendment 25th Aug 2020.

Study populations
FinnGen
FinnGen strives to advance our understanding of disease mechanisms and the efficacy of medical
care. The studies aim is to identify links between individual genetic differences and diseases. The
FinnGen project has the necessary ethical and prior permits for biobank research (Supplementary
Note), and all persons who have provided a research sample are aware of the intended use of the
samples and have given their written consent to biobank research either in connection with sample
donation or when participating in older research projects, the materials of which have been
transferred to Finnish biobanks with the written consent of Fimea. We used The International
Classification of Diseases Tenth Revision (ICD-10) code M48.0 and Ninth Revision (ICD-9) codes
7230A, 7240A, and 7240B (Supplementary Data 1) to characterize cases in FinnGen. Patients
without a record of these ICD codes were classified as controls, and participants who were
diagnosed with other dorsopathies were excluded (M40-M54, M76). Patient data were obtained
from the Hospital Discharge Registry and the Cause of Death Registry however, patients whose
registration data was only available in the primary care registry were excluded from the analysis.
The FinnGen (R12-version) data used in the study included 26,090 LSS cases and 353,224
controls.

Estonian Biobank (EstBB)
The Estonian Biobank cohort is a volunteer-based sample of the Estonian resident adult population
(aged ≥ 18 years)38. Estonians represent 83%, Russians 14%, and other nationalities 3% of all
participants. The current number of participants is >205,000 and represents a large proportion,
>15% of the Estonian adult population, making it ideally suited to population-based studies.
General practitioners (GPs) and medical personnel in the special recruitment offices have recruited
participants throughout the country. At baseline, the GPs performed a standardized health
examination of the participants, who also donated blood samples for DNA, white blood cells and
plasma tests and filled out a 16-module questionnaire on health-related topics such as lifestyle,
diet and clinical diagnoses described in WHO ICD-10. A significant part of the cohort has whole
genome sequencing (3000), whole exome sequencing (2500), genome-wide single nucleotide
polymorphism (SNP) array data (200 000) and/or NMR metabolome data (200 000) available. In
the meta-analysis, 71,277 participants from the Estonian Biobank were included; 6,019 of these
were LSS cases, and 65,258 were controls.

UK Biobank (UKBB)
The UK biobank39 cohort comprises of samples acquired between 2006-2010. Hundreds of
thousands of people aged 40-69 from across Britain provided samples. In our research, we used a
subset of European ancestry, consisting of 8,194 LSS patients and 322,986 controls from the UK
biobank, more precisely summary statistics from the PanUKBB project
(https://pan.ukbb.broadinstitute.org/).

Genotyping, imputation & quality control
FinnGen
Genotyping was performed using Illumina and Affymetrix DNA microarrays. Genotype data were
quality controlled to exclude variants with a low Hardy-Weinberg equilibrium (HWE) p-value
(<1x10-6), minor allele count (MAC) below three, and high missingness (cut-off 2%), as well as
individuals with high genotype missingness (cut-off 5%), high levels of heterozygosity (4 SD),
non-Finnish ancestry, and individuals whose sex did not match the genotype data. Eagle 2.3.5 was
used in prephasing, were 20 000 was used as a threshold of conditioning haplotypes. Genotype
imputation was done with Beagle 4.1 (version 08Jun17.d8b), and Finnish SISu v4.2 was used as a
reference panel. Whole imputation protocol has been described at https://docs.finngen.fi/finngen-
data-specifics/red-library-data-individual-level-data/genotype-data/imputation-panel/sisu-v4.2-
reference-panel. For post-imputation quality control, we excluded variants with imputation
information less than 0.6.

EstBB
Illumina Human CoreExome, OmniExpress, 370CNV BeadChip and GSA arrays were used for
genotyping. Individuals were excluded from the analysis if their call-rate was < 95% or sex defined
using X chromosome heterozygosity estimates didn’t match phenotypic data. Quality control
included filtering on the basis of sample call rate (< 98%), heterozygosity (> mean ± 3SD),
genotype and phenotype sex discordance, cryptNic relatedness (IBD > 20%) and outliers from the
European descent based on the MDS plot in comparison with HapMap reference samples. SNP
quality filtering included call rate (<99% ), MAF (<1%) and extreme deviation from Hardy–
Weinberg equilibrium (P < 1 × 10−4). SHAPEIT2 was used for prephasing, while the imputation
was performed with the Estonian-specific reference panel40 and IMPUTE241 with default settings.
Variants with call-rate < 95%, MAF < 1% or HWE p-value < 1e-4 (autosomal variants only) and
indels were excluded. For post-imputation quality control, we excluded variants with imputation
information less than 0.6.

UKBB
In UKBB, Applied Biosystems UK BiLEVE Axiom Array by Affymetrix and Applied Biosystems
UK Biobank Axiom Arrays were used for genotyping. Individuals that were identified as outliers
for heterozygosity and missing rate and individuals whose sex did not match the genotype data
were removed. Phasing on the autosomes was carried out using SHAPEIT342, with the 1000
Genomes phase 3 dataset43 used as a reference panel. For imputation the Haplotype Reference
Consortium (HRC) dataset44 was used as the main imputation reference panel but also merged
UK10K and 1000 Genomes phase 345 reference panels were utilized. Imputation was carried out
with the IMPUTE4 program (https://jmarchini.org/software/) which is a re-coded version of the
haploid imputation functionality implemented in IMPUTE241. Variants with INFO scores > 0.8
and with an allele count of at least 20 were retained. Also, variants that had a greater than 5%
overall missing rate and had a MAF of less than 0.0001 were removed.

GWAS
The Regenie (version 2.2.4) program46 was used to conduct GWAS using an additive genetic
model in FinnGen and EstBB, modifying each phenotype for age, sex, the first 10 genetic principal
components and genotyping arrays. The sensitivity analysis was performed using Regenie and the
same covariates as above. In UKBB, GWAS was performed using an additive genetic model and
the SAIGE pipeline, modifying each phenotype for age, sex, and the first 10 genetic principal
components. A more detailed description of GWAS pipeline and implementation can be found
here: https://github.com/atgu/ukbb_pan_ancestry/wiki/Batch-pipeline.

Sensitivity analysis: surgical GWAS
Additionally, we conducted sensitivity analysis in FinnGen and EstBB, with the aim of identifying
genomic regions associated with more severe LSS that required surgical treatment. The sensitivity
analysis was done using patients with LSS who had undergone surgery (NOMESCO version 1.15,
ABC36, ABC56, ABC66, ABC99, NAG61, NAG62, NAG63, NAG66, NAG67, and NAG99)37,
with the control group defined similarly to the meta-analysis. A total of 11,747 cases and 352,997
controls were included in this analysis. LSS cases that lacked documentation of studied surgical
codes were not included in the analysis. The same policy was followed in cases of patients who
underwent surgery but did not have an LSS diagnosis because it is likely that these patients had an
acute injury. The same approach was utilized in the Estonian Biobank. A total of 1,037 cases and
65,249 controls were included in the analysis.

LSS genome-wide meta-analysis (FinnGen, EstBB, UKBB)
An inverse variance-weighted fixed-effect meta-analysis of the GWAS results from FinnGen,
EstBB, and UK Biobank was conducted using the METAL software47. Variant data from the
Estonian and UK Biobanks were converted from hg19 to hg38 before being meta-analysed using
the Picard liftover (http://broadinstitute.io/picard). Meta-analysis data consists only SNPs that
have been observed in at least two of the datasets, totaling 15,144,278 SNPs. In total, there were
781,771 participants in the meta-analysis, of which there were 40,303 cases and 741,468 controls.

Random effect meta-analysis
In order to account for heterogeneity that was observed for some lead variants, we additionally
conducted a random effect meta-analysis. The lead variants observed in the main meta-analysis
were used in the analysis that were conducted in same cohorts as out main meta-analysis. The
analysis was conducted using the metafor R package using the "REML" model48.

Surgical meta-analysis
The METAL software47 was used to do an inverse variance-weighted fixed-effect meta-analysis
of the surgical GWAS results from FinnGen and EstBB. Prior to the meta-analysis, variant data
from the Estonian Biobank were converted from the hg19 to the hg38 prior to the meta-analysis.
The meta-analysis only included SNPs that were present in both datasets, totaling 13,994,491
SNPs. There were 12,784 cases and 418,246 controls among the 431 030 participants in the
surgical meta-analysis.

Candidate gene characterization
A locus was defined as a window of 2MB (± 1,000,000 bases) that contains at least one variant
associated with LSS at P<5x10-8. Using literature and databases (Genbank49, UniProt50, GTEx-
Portal51) and identified a potential candidate gene with a relevant biological function for the loci
that had not been reported in association with LSS in previous studies.

Fine mapping and conditional analyses
We applied the FinnGen fine-mapping pipeline (available at
https://github.com/FINNGEN/finemapping-pipeline) with default parameters after first extracting
the summary statistics of each locus associated in the meta-analysis. Exception was made for
variants in HLA region that were left out. The pipeline uses FinnGen samples to compute linkage
disequilibrium within the regions of interest using LDstore252, creates 99% credible sets using the
SUm of Single Effects (SuSiE)53, and summarises the findings. Additionally, with help of GCTA
software (1.93.0 beta Linux)54, we performed conditional analyses for the loci to identify possible
secondary signals. We used lead variants observed from the loci as a covariate and utilized
FinnGen as the reference sample to estimate linkage disequilibrium (LD) corrections. The
associations were first conditioned on the most significant variant at each locus using the --cojo-
cond option with the default settings. For those loci where secondary signals were detected, the
analysis was repeated using the first conditional analysis results. Conditioning was continued until
no variant reached genome-wide significance (p<5x10-8).

Heritability
The LD score regression-derived SNP-based heritability estimate was computed using LDSC
software (version 1.0.1)10. Heritability estimation was performed using the liability scale, with
sample prevalence in each study population (Supplementary Data 1) and population prevalence
estimated at 0.11. as estimated by Jensen et al. (2 020)4. For heritability estimation we used the
HapMap3, a European-only reference panel provided by Alkes Price’s group
(https://alkesgroup.broadinstitute.org/LDSCORE/).

PheWAS
In FinnGen, we also conducted a PheWAS analysis for lead variants associated with LSS. The
PheWAS analysis excluded lead variants that located in the HLA region. In the analysis we used
124 clinician curated core endpoints to test the association of lead variants, and results with
p<5x10-8 were considered significant. All endpoints and endpoint definitions are available here:
https://www.finngen.fi/en/researchers/clinical-endpoints. Whole PheWAS pipeline has been
described here: https://github.com/FINNGEN/pheweb/.

Functional annotations
FUMA55 was used for the functional annotations that were performed based on the meta-analysis
summary statistics. We selected that the analysis would use functional information for the
mapping. Positional mapping was also carried out, with SNP markers chosen from regions of
exons or introns that affect post-transcriptional modifications and are involved in gene regulation.
Gene expression data were also used for mapping, as well as eQTL mapping. We used whole
blood, cultured fibroblasts, tibial nerve, and cervical spinal cord cervical (GTEx v8) tissues in the
analysis, and the focused on the protein-coding genes only. MAGMA analysis31, a functional
association test, was also performed, it focuses on gene-level information, unlike GWAS, where
associations are reported at the variant level. MAGMA uses curated gene sets and GO annotations
from MSigDB56 in the analyses. We used 10kb gene window and selected GTEx v8 tissue variants
in the analysis. We also used fastBAT from the GCTA software package (1.93.0 beta Linux)54 for
gene prioritization. FastBAT calculates the association p-value for a set of SNPs from an
approximated distribution of the sum of 2-statistics over the SNPs at gene regions using LD
correlations between SNPs from a reference sample with individual-level genotypes and summary
data from GWAS30. We utilized FinnGen as a LD reference and used 2MB window for the loci.
For LD pruning 0.9 r2 threshold was used alongside default methods. We considered results with
pfastBAT<5x10-8 and pGWAS<5x10-8 significant30. Lead variants that located in the HLA region
were left out from the annotation analyses.

Colocalizations
We investigated into the colocalizations of LSS association signals using various QTL data and
other FinnGen R12 endpoints. For this the FinnGen SuSiE colocalization pipeline was utilized
(https://github.com/FINNGEN/coloc.susie.direct). Colocalizations between 571 resources are
carried out by this pipeline, including all GWAS endpoints from UKBB, FinnGen, and the eQTL
catalogue, the Generisk project, and the proteomics study from INTERVAL, UKBB, and FinnGen.
We utilized our SuSiE fine mapping results as input for the analysis. Colocalizations with a
posterior probability of  0.8 for the variant were deemed significant57.

Survival analyses
Our aim was to observe how the LSS diagnoses accumulate for different variants according to age
and to evaluate whether there are early onset variants, by exploring the cumulative events. We
determined the diagnosis age in years when the curve of the effect allele (EA) homozygotes
statistically differed from the curve of other allele (OPA) homozygotes of the same variant, of the
variants where significant differences in the accumulation of diagnoses were observed. The
calculation was performed with the two-tailed z-test by using the survival rates and survival rate
standard errors obtained with the 'survfit' function, which is part of the 'survival'
(https://github.com/therneau/survival, version 3.2-7) R library. The equation used was:
z=(‘survival rate EA/EA’-‘survival rate OA/OA’)/sqrt(‘se_survival_rate_EA/EA2’+’se_survival_rate_OA/OA2’),
p=2*(1-diff). A significant difference was defined as a P-value < 0.05. Furthermore, we calculated
cumulative morbidity for each variant to determine whether some variants result in more
diagnoses. In addition, we conducted a GATE analysis32 in FinnGen, which examines the
association between the variants and the time-to-event rather than the presence of the disease. In
the analysis, we attempted to detect variants associated with LSS disease onset by extracting
diagnosis ages for LSS cases and ages at the end of the follow-up or at death for controls. Sex and
the first 4 genetic principal components were used as covariates in the analysis.

Genetic correlations
Genetic correlations were calculated between meta- analysed LSS summary statistics and 849
other phenotypes extracted from the GWAS database provided by the MRC Integrative
Epidemiology Unit (IEU) (https://gwas.mrcieu.ac.uk/). These calculations were performed using
the LDSC software (version 1.0.1)10,58. We set the threshold for significant correlations at a false
discovery rate (FDR)-corrected p-value (pFDR) < 0.05. Genetic correlations were performed using
HapMap3 European-only reference panel provided by Alkes Price’s group
(https://alkesgroup.broadinstitute.org/LDSCORE/). For genetic correlations, we used our LSS
summary statistics and the GWAS summary statistics that were similar to those that have been
previously available in LDhub (https://ldsc.broadinstitute.org).

Mendelian randomization
In order to investigate the causal relationships between LSS and its associated risk factors, we used
the Two-Sample MR (version 0.6.17) R library to perform a Mendelian randomization. Risk
factors related to body composition and pain, were included in the analysis ( Supplementary Data
17), and we were able to assess if LSS is causal for risk factors and, conversely, if risk factors are
causal for LSS due to a bi-directional study approach. To avoid sample overlap, since many of the
GWAS data provided by the MRC-IEU are UKBB-based, we derived LSS instruments from a
meta-analysis of FinnGen and EstBB summary statistics. As above, variant data from EstBB were
first converted from hg19 to hg38, and we then conducted inverse variance-weighted fixed effect
meta-analysis used the METAL software47. This meta-analysis comprised 450,591 participants,
including 32,109 cases and 418,482 controls. Variants that were correlated were removed from the
data, leaving only independent variants for analysis. We used the default clumping settings in the
analysis (clumping window 1000kb, r2=0.001). The principal analysis method we employed was
the Inverse Variance Weighted (IVW) model. In the sensitivity analyses, we obtained MR Egger
estimates and performed Cochran's Q-test and the MR Egger intercept test to evaluate the
heterogeneity and pleiotropy of the instruments. Additionally, as a sensitivity analyses, we also
used weighted median model and obtained outlier corrected causal estimates by using
MRPRESSO with default settings (NbDistribution=10000, P-value threshold=0.05)36. Also, in
order to determine whether a particular SNP is responsible for a potentially detectable causal
relationship, a leave-one-out analysis was also carried out.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Lumbar spinal stenosis (LSS) is an important cause of musculoskeletal morbidity which is clearly age-related and poorly understood. This excellent study contributes greatly to our understanding of the genetics underlying the condition. A large sample was assembled using data from 3 robust population studies (FinnGen, Estonian Biobank and UK Biobank) and utilised ICD 10 codes for categorising cases (n>30,000) and controls (n>700,000). Overall SNP based heritability of LSS was estimated at 18% and 54 genome wide significant loci were identified. Of these 7 replicated previously associated SNPs in LSS and 17 were previously associated with similar phenotypes such as back pain or disc herniation. A sensitivity analysis based on FinnGen samples of individuals coming to spine surgery for LSS revealed higher SNP heritability of 21%.\nThe manuscript is very well written, the study is comprehensive and limited only by the poor availability of relevant tissues (vertebral bone and intervertebral disc) in the expression datasets such as GTEx. Age of onset study showed that LSS diagnoses started around 40 and majority accumulate between 50 and 70 years, with 5 variants showing cumulative incidence for homozygotes already at age 40, and these findings were replicated in a second dataset Genetic correlations were highly significant for general health and mood-related conditions.\nThe limitations paragraph is well versed on the ethnic limitations of the study which is confined to Northern Europeans but the other main limitation is the tissues available for functional studies in GTeX. My comments are minor only and are largely related to the rather scant discussion following such extensive genome-wide significant findings:-\n1. Are there heritability estimates of LSS which could be provided by way of background in the introduction?\n2. Would it be possible to give prevalence values for symptomatic LSS in the general population, by age? This gives a scale of the clinical problem, rather than imaging prevalence estimates\n3. In discussion it would be interesting to explore the pain vs spine structure genes, in particular NGF is already a drug target and merits further discussion.\n4. The discussion would also benefit from causal relationship discussion regarding whole body fat-free mass and BMI - does this suggest that both bone/ muscle mass and adiposity both contribute to risk of LSS?\n5. Does the FinnGen surgical dataset reflect enrichment for pain-mediating genes, as might be expected? Did the effect size of genetic correlations for general health and mood-related conditions change substantially in this subset?\nThis manuscript makes a very substantial contribution to the spine and back pain fields.", "reviewer_2": "Salo et al. conducted a GWAS meta-analysis of lumbar spinal stenosis (LSS) followed by some post-GWAS analyses.\nSeveral genetic association signals were identified, their related genes, pathways were validated using multiple methods.\nThe novelty included a sub-sample GWAS analysis in LSS patients requiring surgical treatment and a follow-up analysis of the cumulative incidence difference of LSS in between two homozygotes for the lead variants. Below are our detailed comments.\nMajor comments\n1. Structure,\na. Is the manuscript a short article? The manuscript does not have introduction, results and discussion.\nb. abstract did not clearly explain the results. Half of the paragraph explained the motivation of the analysis.\n2. GWAS meta-analysis\na. population stratification is an important consideration here. this is the basis of conduct a GWAS meta-analysis. This is specifically the case here since both Finnish and Estonians were reported to have population stratification when compared with other Europeans. Is this the reason why the LDSC intercept term also showed evidence of inflation?\nb. Following the previous comment, this study relied on PanUKBB GWAS, which included non-European participants. Please specify the percentage of European participants, and evaluate how would this influence the reliability of the GWAS results.\nc. Some SNPs (e.g. rs9438857) showed heterogenic effects across biobanks. Any SNP may need to report random effect meta-analysis results?\nd. At least, for top hits, need to check whether the results were consistent among three biobanks. e.g. check effect size and allele freq of the top hits among three biobanks.\ne. Table 1 and across the whole manuscript, p value need to be reported in scientific format (rather than 1e-8). The FinnGen Enrichment analysis is a useful analysis. However, it is not clear how the analysis was conducted, what does the value mean?\nf. This study relied on FinnGen R9-version. Is it possible to update the results using FinnGen R12-version to increase the number of cases? “Meta-analysis data consists only SNPs that have been observed in at least two of the datasets.” It is unclear how many SNPs only available in FinnGen were dropped in meta-analysis. However, FinnGen contributes >50% of LSS cases, and SNPs only available in FinnGen should not be dropped.\n3. The sensitivity analysis of LSS patients requiring surgical treatment, this is a quite useful analysis. Please clarify why this analysis was only been conducted in FinnGen?\n4. Gene-based analysis, please specify how many genes were the same as reported by the GWAS and how many were new genes that were not reported by the GWAS? Please cite GCTA, MAGMA and other methods across the whole manuscript. What kind of pathway enrichment analysis been used?\n5. Colocalization, are the eQTLs from GTEX? Does COLOC + Susie been considered?\n6. Longitudinal analysis, this is an interesting analysis. Similar as a Cox regression based GWAS using tools such as GATE. Will be interesting to test the top hits using GATE and see the differences. Fig S4, seems have not massively different, since they are common variants.\n7. Fig 3. I think it is not a good idea to predict medication usage using a genetic model. The variants been identified will likely to be those associated with the indication (e.g. blood pressure for anti-hypertensive drugs) rather than the drug usage itself. Similarly, I don't think operations is related to genetics. More likely to identify variants associated with major diseases.\n8. Mendelian randomization\na. How were the factors been selected in the MR analysis? Please explain.\nb. Only IVW and ME-Egger been applied? There are many more novel methods coming out recently.\nc. For those reported in Fig4, does the author consider reverse MR results? Need to make sure there is no bi-directional causality.\nd. This study identified 54 loci, but only 33 SNPs were used as instruments for LSS, as shown in Fig 4B. How would this influence the reliability of the MR results?\nMinor comments:\n1. Any gene reported before not showing up in this meta-analysis?\n2. Sensitivity analysis of LSS patients requiring surgical treatment, need to report the number of cases in the main text. The genes need to be reported in Italic format. The comparison between the main and sensitivity effects. Need to report what kind of statistical analysis been used here to show the differences statistically? The confidence interval seems overlap with each other, so is there any statistical differences.\n3. PheWAS, which tool and database been used? How many traits been included?\n4. The current code availability statement does not help improve the reproducibility of this paper. Please provide the scripts used exactly for this study (including both GWAS and post-GWAS analyses) either in supplementary or on GitHub.", "reviewer_3": "In the present study, the authors investigated the genetic architecture of lumbar spinal stenosis (LSS). They conducted a genome-wide association study using data from FinnGen, Estonian, and UK Biobank, combining the results through a genome-wide meta-analysis, which identified 47 novel loci associated with LSS. Downstream analyses revealed that LSS is an age-related condition and identified pathways associated with more severe forms of LSS. Overall, this study has deepened our understanding of the genetic architecture of LSS. Specific comments are included below:\n1.Providing the genetic background of LSS and the motivation for this study will help readers understand the novelty of this study.\n2.The study lacks detailed inclusion and exclusion criteria for the EstBB and UKBB samples. Table S1 presents only the criteria for case and control exclusions in FinnGen, failing to specify the quality control details for the other two samples. In addition, a description of the procedures for genotyping, imputation, and quality control of variants in the UKB database is lacking in the methods.\n3.I noticed that only FinnGen and EstBB were analyzed in the methods section, so how was the GWAS of LSS conducted in UKB, and whether Regenie software was also used, which I know is very helpful for extremely imbalanced case-control ratio samples.\n4.This study conducted a MAGMA analysis; however, it does not specify the critical p-value used in this analysis.\n5.This study employed Two-Sample MR to investigate the bidirectional causal relationship between LSS and its associated risk factors, which are detailed in Table S11. Please provide robust references to justify the selection of these nine risk factors.\n6.In sensitivity analyses, the results of the surgically treated GWAS compared with the original FinnGen GWAS should be provided.\n7.In the results section, the authors show that the variants identified at the LSS-associated loci are also associated with many other spine-related disorders, but Table S3 did not reflect this information, please check carefully. In addition, the description of the PheWAS analysis was also not found in the Methods section; please add this information.\n8.In the survival analysis of LSS-related variants, it is not clear how the difference in cumulative LSS diagnosis rate between homozygotes of lead variants is reflected in different age groups. Is the survival analysis performed by age group?\n9.The expressions of scientific notation in this article lack sufficient rigor; for example, powers of ten are not represented using superscripts. Please review and correct this.", "reviewer_4": "Salo et al. present a large-scale meta-analysis of GWAS for lumbar spinal stenosis, comprising three major biobank resources. While a number of novel loci are identified, the study design, as well as the structure of the main text, can be further improved. To improve the clarity of the writing, it is suggested to add subtitles in each results section. Additionally, at the beginning of each paragraph, it would be better to include the rationale for why this analysis was done.\nBelow are some comments and suggestions:\nPage #2\nThe authors mentioned that they “successfully replicated seven loci previously associated with LSS”. Are these seven loci the only signals reported in previous GWAS studies? Are there any previous signals not identified in the current study?\nThe last paragraph should be an overall design of the study rather than mixing cohort information and some analysis results.\nIn conditional analysis, what is the added value to understanding the genetic architecture of the disease? In those loci where more than one independent variant is identified, what are the functions of those variants? Have those variants linked to the regulatory effects of disease gene expression?\nPage #3\nIt’ll be more straightforward to say that the heritability estimates are based on the liability scale in the main text.\n“Our estimates were calculated using a larger sample, which is likely the main reason for the difference.” From the evidence presented in this section, this conclusion is unconvincing. There is no complementary analysis to support such a claim.\nPage #7\n“The pathway responsible for sequence-specific DNA binding showed the most significant enrichments in the MAGMA gene-set analysis”. What's the interpretation of this enrichment? Is this a unique pathway that is enriched just for this disease?\nThe colocalization analysis between GWAS and eQTL signals could be further enriched to provide deeper insights into the biological relevance of the findings. For instance, is there any gene only identified in the cervical spinal cord? If so, is that due to the SNP only showing eQTL in this tissue compared to other tissues tested, or the gene is only expressed in the cervical spinal cord?\nPage #8\n“Significant genetic correlations were found between 517 traits and LSS”. This sentence reads like all the 517 traits tested are significantly correlated with LSS.\nPage #9\nWhy only IVW and MR-Egger methods were selected for the MR analysis? IVW is not robust to the pleiotropy effect and MR-Egger lacks statistical power compared to other recent MR methods.\nFor the LSS -> Back pain analysis, given the IVW is significant and MR-Egger is not, have the authors checked if there are any instrumental variables that violate any of the three key assumptions of the MR framework?\nPage #14\nIn the meta-analysis, what is the total number of SNPs tested after selection? Were there any SNPs showing significant heterogeneity effects?\nIn the heritability analysis, have the authors estimated it in each cohort separately?\nPage #16\nIn the clumping analysis, it is suggested to reduce the window size because the stringent r2 threshold already ensures that the clumped SNPs are independent. A window size of 1 or 2Mb would return more number of instrumental variables.", "reviewer_5": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_6": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Response to Reviewer 1:\n1. Thank you for the suggestion. We now describe the heritability estimate in the introduction as follows: “A previous MRI-based twin study1 estimated the heritability of LSS to be 0.67, indicating a substantial genetic contribution to LSS risk”.\n2. Thank you for your feedback. We have revised the introduction to include also prevalence values for symptomatic LSS. The introduction was modified to:”In the general population, the majority of radiological LSS cases are asymptomatic. The prevalence of diagnostic LSS is around 11%, increasing significantly with age.”. We can presume that the LSS cases in our study were symptomatic because they were clinically defined using ICD codes. Patients without symptoms are unlikely to have sought medical care. We note in the manuscript: \"We can presume that diagnoses are symptomatic since they are clinically determined. Depending on the study cohort, the prevalence of LSS in our study varied from 2.5-8.4%; in the meta-analysis, it was 5.2%.\". Because we only have access to summary statistics from the Estonian and UK biobanks, we are unable to report age-specific prevalences from those datasets. Nevertheless, in FinnGen, we examined LSS prevalence of the across 5-year age groups, and the results are shown in the table below. We did not include this table in our manuscript, as most findings in the manuscript rely on meta-analytic results rather than cohort-specific data. We utilized For patients over 75, it is also possible that registry data is lacking if they were diagnosed prior to registrations, which only started in Finland in the 1980s.\n[Table omitted for brevity]\n3. Thank you for the suggestion. We now describe the pain vs spine structure phenomenon in the manuscript as follows:”In addition, we identified and replicated previously identified loci near genes related to the nervous system, such as DCC (DCC netrin 1 receptor) and NGF (nerve growth factor), the latter of which is also already a known potential medical target for pain. The identification of nervous system-related genes suggests that, in addition to structural changes, nervous system-related pathways are potentially involved in the underlying symptomatic LSS and may thus explain the differences in symptoms experienced by patients. Similar findings have also been made in GWAS studies investigating the genetic background of LDH and chronic back pain.”.\n4. Thank you for your thoughtful comment. Our findings may indeed suggest that both fat-free mass and adiposity play a role in LSS risk. While our results do not directly indicate the underlying mechanism, the result may indicate that higher body mass increases the risk for LSS. We have now included the following note in our manuscript: ”In light of our results, higher body mass seems to increase the risk of LSS.”.\n5. We compared the original meta-analysis and the surgical meta-analysis in terms of gene-set enrichments. In fact, the MAGMA gene-set analysis for surgical meta-analysis showed the most significant enrichment of TGF- signalling-related pathways. Subsequent top-ranked gene-sets were those linked to pain that we had observed in the LSS meta-analysis. It is important to consider that patients undergoing surgery have well-characterized structural changes that can be surgically treated. Therefore, enrichment in structural gene sets is expected in the surgical meta-analysis. Moreover, while symptomatic LSS does not always cause pain, its symptoms, like sporadic walking, have usually a big impact on a patient's quality of life. If surgery is deemed likely to improve the patient’s condition significantly, they will be operated, regardless of pain severity. In other words, pain is not usually the leading symptom when considering surgery for a symptomatic LSS patient. In the surgical meta-analysis, genetic correlations with traits related to general health and mood were slightly weaker than in the LSS meta-analysis. For example, major depressive syndrome (ICD-10 coded) id:ebi-a-GCST005903 had rg=0.40, pFDR=1.10x10-19 in the meta-analysis and rg=0.32, pFDR=2.34x10-9 in the surgical meta-analysis. However, the smaller sample size in the surgical subset likely contributes to these differences. These results do not necessarily reflect differences between the study populations.\n\nResponse to Reviewer 2:\n1a. Yes, the manuscript was originally submitted in a letter format. During the review process, we have made substantial revisions to the structure and added subheadings, which we hope will clarify the structure of the manuscript.\n1b. Thank you for your feedback. We have revised the abstract to describe our findings more comprehensively.\n2a. Yes, residual population stratification in combination with polygenicity likely causes the observed inflation, as we noted in the manuscript: “The genomic inflation factor lambda GC varied between 1.14-1.54 and suggested inflation in the test statistics. Considering the observed intercept values varied between of 1.00-1.14, the inflation is likely attributed to a polygenic signals”. For further context, Bulik-Sullivan et al.4 demonstrated that the LDSC intercept approximates lambda under population stratification and remains close to 1 under polygenicity. Since our intercepts are considerably closer to 1 than the lambda values, we consider that the polygenicity remains a plausible explanation, while acknowledging some residual stratification.\n2b. While the PanUKBB project includes various ethnicities, in our study, we used only the European subset: “In our research, we used a subset of European ancestry, consisting of 5134 LSS patients and 401 917 controls from the UK Biobank, more precisely summary statistics from the PanUKBB project.” We acknowledge that the Finnish and Estonian populations have unique histories that differ from other European populations5. Considering that most lead variants identified in our meta-analysis showed consistent effect sizes across all three studies, our results can be considered robust (Fig S1 & Table S2).\n2c. Thank you for this suggestion. We have now performed a random-effect meta-analysis for the lead variants we identified in the meta-analysis. We have added a mention of this in our manuscript and reported the results of the analysis in Fig. S1 and Table S4. The estimates from the random-effects model were highly consistent with those from the fixed-effects model.\n2d. We have checked the effect allele frequencies and effect sizes for all lead variants, and results presented in the supplementary materials (Fig S1 & Table S2). The observed effect sizes were consistent across all study populations.\n2e. Thank you for your suggestion. We have now converted p-values to scientific format. The method for calculating Finnish enrichments was described in the table legend: “Fin Enric., enrichment in Finns (calculated Finnish EAF/European (non-Finnish) EAF in the Genome Aggregation Database [gnomAD, v.3.1.2], where FIN EAF is the allele frequency in Finns and European (non-Finnish) EAF is the allele frequency in non-Finnish”. This value indicates whether the allele is more enriched in the Finnish population compared to the European population that did not include Finnish or Estonian participants. In the revised manuscript, the Finnish enrichments are given in Table S2, and we have clarified the interpretation in its legend:”Values >1 mean that the EAF is larger in Finnish population than on non-Finnish Europeans and <1 that EAF is larger in non-Finnish European”.\n2f. Our manuscript has now been updated from the FinnGen R9 version to FinnGen R12, and we have also incorporated updated datasets from Estonian and UK biobanks. This has resulted in both an increase in case numbers and a number of novel associations. While we agree that the SNPs identified in the FinnGen study could be reported and that the FinnGen contribution to LSS cases is substantial, we chose to exclude these variants to improve the reproducibility and generalizability of our findings.\n3. We asked our collaborators to replicate the surgical GWAS in the Estonian Biobank dataset. In the resubmitted manuscript, we now report results from meta-analysis combining FinnGen and Estonian Biobank-based surgical GWASs.\n4. Thank you for these questions. Regarding the first point, GWAS reports associations at SNP level, whereas MAGMA provides gene-level results, so the two are not directly comparable. Since MAGMA analyses are based on our GWAS summary statistics, the genes it identifies should correspond to loci highlighted in the GWAS. If the Reviewer is referring to the overlap between MAGMA-identified genes and the genes we considered most likely to explain LSS associations at each locus, the number is 25. Regarding the 2nd point, our enrichment results refer to the MAGMA gene-set analysis6 as stated in the manuscript. We also edited the manuscript to add citations for methods that we utilized across the manuscript.\n5. Thank you for the suggestion. In the revised manuscript, we have performed colocalization analyses using the coloc SuSiE approach, utilizing the FinnGen pipeline (https://github.com/FINNGEN/coloc.susie.direct) and leveraging a broad set of data from 571 resources, such as GWAS endpoints from UKBB and FinnGen, eQTL data from GTEx and eQTL Catalogue, and proteomics, metabolomics and lipidomics data from various sources. These enhancements have substantially improved the depth and quality of our colocalization results compared to the original version.\n6. Thank you for this excellent suggestion. We have now performed GATE, which yielded interesting results and validated several findings from our survival analysis. Many of the loci identified are located near the same genes that we observed in our meta-analysis, however, we also observed some age-of-onset specific associations (Table S13). Additionally, Fig S5 (previously Fig S4) has been revised due to the updated datasets. The variant near the OTUD7B gene now stands out more prominently (Table S12), and the exact same variant was also identified in GATE analysis, supporting its potential role in LSS onset. Additionally, as you pointed out, since the majority of the variants we observed are common, the differences between homozygotes are minimal.\n7. We agree that medication and operation-related traits are likely to reflect the underlying indication rather than the treatment itself. In response, we have removed the medication and operation-related traits from the genetic correlation analysis.\n8a. We have revised our manuscript to describe better the rationale for selecting the risk factors in the MR analysis: “Prior research has shown associations between anthropometric traits and LSS7–9; however, the causal relationships between these factors have not been thoroughly examined.” We also cited previous MR studies that have studied causal relationships between the same risk factors and LSS: “Our findings for BMI and hip circumference were consistent with earlier reports10, and additionally, our study also highlights previously unreported potential causal relationships.”.\n8b. In the revised version of the manuscript, we have have expanded the methodological framework by also applying the weighted median model and conducting outlier-corrected causal estimates using MRPRESSO11. These results were consistent with those obtained using IVW and MR-Egger (Fig 4).\n8c. We explored the possibility of bi-directional causality using the Steiger directionality test (mr_steiger; (https://mrcieu.github.io/TwoSampleMR/reference/mr_steiger.html). However, its results were inconclusive and did not provide clear evidence favoring one causal direction over the other, and the associations were not significant. Given this ambiguity, we interpreted the results in the context of previously observed associations7–9 and known biological functions. We acknowledge the limitations of this approach and have added a note in the study limitations: \"While our MR analyses support potential causal relationships, the findings should be interpreted with caution, as the possibility of bi-directional causality between the endpoints cannot be excluded.\".\n8d. Sample overlap in two-sample MR could lead to substantial bias and inflated Type I error12. To avoid sample overlap, we used summary statistics from a meta-analysis based on FinnGen and EstBB to extract instruments for LSS, as the summary statistics for most other traits include UKBB data. Therefore, the number of instrument SNPs is smaller than the number of loci identified. We had described this in our manuscript: “To avoid sample overlap, since many of the GWAS data provided by the MRC-IEU are UKBB-based, we derived LSS instruments from a meta-analysis of FinnGen and EstBB summary statistics.”.\nMinor 1. Yes, five loci that were not associated with LSS in our study have been identified in earlier studies. The manuscript now includes this information: “Seventy-three of the loci were previously unreported. We also successfully replicated fifteen loci previously associated with LSS, while 5 loci did not replicate.”.\nMinor 2. Our manuscript now includes the numbers of cases and controls in the surgical meta-analysis. Gene names are now consistently formatted in italics. The captions of Figure 1B and Table S5 describe the statistical method (z-test) used to compare effect sizes. The differences in the effect sizes are statistically significant (p<0.05) if the effect size from one analysis falls outside the confidence interval of the other, even if the confidence intervals themselves partially overlap.\nMinor 3. Thank you for pointing this out. The description of the PheWAS analysis was unintentionally omitted from the original methods section. We have now added this information to the manuscript: “In FinnGen, we also conducted a PheWAS analysis for lead variants associated with LSS, excluding those located in the HLA region. In the analysis we used 124 clinician-curated core endpoints to test the association of lead variants, and associations with p<5x10-8 were considered significant. Endpoint definitions are available at https://www.finngen.fi/en/researchers/clinical-endpoints, and the PheWAS protocol has been described at https://github.com/FINNGEN/pheweb/.”.\nMinor 4. Thank you for your comment. We would like to note that we have not developed any custom code specific to this study. All analyses were conducted using publicly available, open-source tools, as cited in Methods. We believe our current code availability statement “The citations and URLs listed in the Methods section contain the code for every tool used in the analyses. All tools used in our study were open source” is consistent with Nature Communications’ guidelines. To further support transparency and reproducibility, we will make the GWAS summary statistics from the LSS meta-analysis and the surgical meta-analysis publicly available in FinnGen’s public cloud bucket upon publication.\n\nResponse to Reviewer 3:\n1. Thank you for the suggestion. We have revised the manuscript to include heritability estimates obtained in twin studies that demonstrate a significant genetic component. We also emphasize that, despite this evidence, the genetic basis remains poorly characterized, and our study contributes to filling this gap.\n2. We have modified Table S1 to also describe the inclusion and exclusion criteria for the Estonian and UK biobanks. Furthermore, we have expanded the methods section to describe in more detail how genotyping, imputation, and quality control were conducted in the UKBB. We hope these revisions clarify the data processing steps across all cohorts.\n3. We have added a detailed description of the GWAS conducted in the UKBB to the methods section. In UKBB, GWAS was performed using an additive genetic model and the SAIGE pipeline.\n4. Thank you for pointing this out. We have added the p-value threshold used in the analyses to both the figure captions and within the main text of the manuscript: “Overall, 105 genes with genome-wide significance (p<5x10⁻⁸)were identified by GCTA-fastBAT analysis, and 148 genes were identified by the MAGMA gene-based test. Some of the genes were identified in both analyses. The pathway responsible for neurotrophin receptor binding showed the most significant enrichments in the MAGMA gene-set analysis (Fig. 2B), which used the pFDR<0.05 significance threshold.”.\n5. We have narrowed the list of used risk factors to the ones that we listed previously. Additionally, we have revised the manuscript text to better justify the selection of these traits: “Prior research has shown associations between anthropometric traits and LSS7–9; however, the causal relationships between these factors have not been thoroughly examined.” We have also cited previous MR studies investigating causal relationships between the same risk factors and LSS: “Our findings for BMI and hip circumference were consistent with earlier reports10, and additionally, our study also highlights previously unreported potential causal relationships.”.\n6. We have now included a genome-wide meta-analysis of the FinnGen and EstBB surgical GWASs in the revised manuscript. The results of the surgical meta-analysis are compared with those from the original LSS meta-analysis, and in addition, we present the cohort-specific results (Fig S2, Fig S3, Table S7).\n7. Thank you for your comment. We apologize for the unintentional omission of the description of the PheWAS analysis in the original manuscript – this information has been added to the revised manuscript. In addition, we have revised the PheWAS results description: “Several loci near genes that function in biological pathways, such as TGF-, BMP or WNT-signalling pathways, which are known to play a role in musculoskeletal disorders were identified (Table S3). Some of observed loci have also been associated with other musculoskeletal disorders, such as osteoarthritis, lumbar disc herniation, or back pain. The same was observed for some loci in our PheWAS analysis, where we found that many LSS-associated lead variants were also associated with other musculoskeletal disorders. On the other hand, some of the associations appear to be LSS-specific based on our results (Table S3).”.\n8. Thank you for your comment. Our survival analysis was designed to study the cumulative incidence of LSS diagnoses across the full age range of the FinnGen sample, with age as the underlying time scale. This approach allows us to evaluate whether certain lead variants are associated with an earlier onset of LSS. Stratifying the analysis by age groups would limit the ability to capture lifetime risk and would reduce the power to detect differences in age of onset. We therefore chose to analyze genotype groups across the full cohort rather than within specific age bands. We agree that age-stratified analyses could be informative for other research questions, but such analyses are beyond the scope of the current study.\n9. Thank you for pointing this out. We have modified scientific notations to use proper formatting.\n\nResponse to Reviewer 4:\nPage 2. Thank you for your comment. Indeed, the number of replicated loci is seven. We have now identified five loci from earlier GWASs that were not associated with LSS in our study and added this information to the manuscript.\nPage 2. Thank you for your suggestion. We have revised the final paragraph of the introduction to describe the overall design and motivation of the study. It now reads: “Further research is needed to fully understand the hereditary background of LSS, which is currently poorly characterized. Using data from FinnGen, Estonian Biobank, and UK Biobank, we perform a genome-wide meta-analysis and identify previously unreported loci. We also show that examining LSS by utilizing surgical procedure codes provides further information regarding a more severe form of the disease that requires surgical treatment.”.\nPage 2. Conditional analysis enables the identification of multiple independent association signals within the same locus. While its added value to a better understanding of the genetic architecture may be modest, it can reveal additional signals that are masked in standard GWAS. As with most GWAS findings, it remains challenging to identify the true association-causing variants and their functions. Most of the variants identified in the conditional analyses, however, are located in genomic regions that potentially regulate gene expression. We have added the variants identified in the conditional analysis and their annotations to Table S3.\nPage 3. We have now clarified the main text that the heritability estimates are reported on the liability scale. While this was already mentioned in the methods, we agree it should also be mentioned in the main text.\nPage 3. We have revised the wording regarding the observed heritability estimates and removed this claim. It now reads: ”We estimated LD score regression-derived SNP-based heritability on the liability scale to be 0.1969-0.2499, suggesting genetic factors account for 19.7-25% of the common variation in LSS risk depending on the study dataset utilized (Table S6). The genomic inflation factor lambda GC varied between 1.14-1.54 and suggested inflation in the test statistics. Considering the observed intercept values varied between of 1.00-1.14, the inflation is likely attributed to a polygenic signal10. Our heritability estimates were higher compared to the previous SNP-based heritability estimates, which have estimated that 10.1-14.9% of the common variation in LSS risk originates from the genetic factors (https://nealelab.github.io/UKBB_ldsc/h2_summary_M13_SPINSTENOSIS.html).”.\nPage 7. Since updating from FinnGen R9 to R12, this pathway is no longer the most significantly enriched gene-set. More broadly, gene-set enrichments should be interpreted with caution, as statistical enrichment does not necessarily indicate that the pathway in question is involved in the disease under study, but describes which biological pathways are overrepresented among associated genes. We have avoided making functional claims and emphasize the need for further functional research to determine the role of specific biological pathways in LSS. Regarding the sequence-specific DNA binding gene-set, the gene set is broad: \"Binding to DNA of a specific nucleotide composition, e.g. GC-rich DNA binding, or with a specific sequence motif or type of DNA e.g. promotor binding or rDNA binding.\" Therefore, it is unlikely to be an LSS-specific signal.\nPage 7. We have updated our colocalization analysis by switching to coloc SuSiE in the revised manuscript. This has substantially expanded our analysis from four eQTL datasets to over 500 resources. In the revised version, we have attempted to provide a more profound understanding of the biological significance of the findings. In the revised colocalization results paragraph say the following: ”In total, we detected 758 colocalizations (posterior probability [PP] for the shared variant 0.8) between LSS GWAS and 322 different resources that contained numerous QTL datasets and other FinnGen R12 endpoints. Unsurprisingly, several colocalizations were observed with other back-related disorders in FinnGen. For example, LSS association signals on chr12 near gene SOX5 (SRY-box transcription factor 5) and chr15 near SMAD3 (SMAD family member 3) were colocalized with association signals of several back-related disorders (Table S12). These findings, together with our PheWAS findings, suggest that some of the biological pathways involved in the pathogenesis of LSS overlap with other back-related disorders. The LSS association signals on chr2 near GFPT1 (glutamine-fructose-6-phosphate transaminase 1) and chr7 near IL6 (interleukin 6) colocalized specifically with several immune-related eQTLs. For both variants, colocalizations were observed in eQTL datasets related to monocytes and macrophages, and also in possible structural eQTL datasets such as fibroblasts and tibial artery (Table S12).”.\nPage 8. Thank you for pointing this out. To clarify, genetic correlations were tested across 517 different traits, and not all of them were significant. We have revised the wording in the manuscript as follows: “Genetic correlations were studied between 849 traits and LSS.”.\nPage 9. In the revised manuscript, we expanded the MR analysis to include weighted median model and obtained outlier-corrected causal estimates using MR-PRESSO11. Regarding the possible causal relationship between LSS and back pain, we conducted more sensitivity analyses. We found no indication that the fundamental MR principles were being violated but have added emphasis that MR Egger did not confirm our main findings. We have described our findings as follows: ”The only outcome for which we found LSS to be potentially causal was back pain (OR=1.027, pFDR=5.92x10-13, Fig. 4B, Fig. S10, Table S16). Pain is one of the main reasons for seeking care and sometimes also the main symptom2. However, the sensitivity analysis with MR Egger (OR=1.025, pFDR=0.3156), was not statistically significant. The fact that we did not observe significant directional pleiotropy (Table S16) suggests that the null result we obtained with MR Egger could be the consequence of the known reduced statistical power of the method compared with IWV. Although pleiotropy was not observed, some causal estimates were heterogeneous (Table S16), and thus, so these results should be interpreted with cautious.”. We also performed the MR Steiger test (https://mrcieu.github.io/TwoSampleMR/reference/mr_steiger.html), a statistical analysis to determine the validity of the exposure-outcome hypothesis. However, the test results were inconclusive, as neither direction reached statistical significance.\nPage 14. In the meta-analysis, the total number of SNPs tested was 15,144,278 and in the surgical meta-analysis 13,994,491. We have now added this information to the meta-analysis descriptions in the methods section of our manuscript. Regarding heterogeneity, some of the SNPs showed evidence of heterogeneity across cohorts. To account for this, in the revised manuscript, we also report results from a random-effect meta-analysis for the lead variants we identified in the fixed-effect meta-analysis. The results were highly consistent.\nPage 14. Thank you for this suggestion. In the revised manuscript, we have also estimated heritability in each cohort separately and describe the following: ”We estimated LD score regression-derived SNP-based heritability on the liability scale to be 0.1969-0.2499, suggesting genetic factors account for 19.7-25% of the common variation in LSS risk depending on the study dataset utilized (Table S5). The genomic inflation factor lambda GC varied between 1.14-1.54 and suggested inflation in the test statistics. Considering the observed intercept values varied between of 1.00-1.14, the inflation is likely attributed to a polygenic signal and residual population stratification not fully controlled by principal component adjustments in SNP-based heritability estimations performed in FinnGen and Estonian Biobank4.”. In addition, the results of the heritability estimation are presented in Table S5 in the supplementary materials.\nPage 16. Thank you for the helpful suggestion. In the revised manuscript, we have decreased the clumping window from 10,000 KB to 1000 KB, which resulted in a greater number of instrumental variables without increasing pleiotropy or heterogeneity."}, {"round": 2, "reviewer_comments": {"reviewer_2": "Salo et al have performed a whole set of additional analyses and strengthen the reliability and depth of the study. The updates have yielded some new findings too.\nI just have one additional comment:\n1. Re bidirectional MR, thanks for trying to use Steiger filtering. You can do a MR using LSS as exposure and risk factors as outcomes. That will directly inform existence of reverse causality.", "reviewer_3": "I thank the authors for their thorough and thoughtful responses to my comments. The revised manuscript has been significantly improved and adequately addresses all the concerns raised. I have no further comments and recommend acceptance in its present form.", "reviewer_4": "The authors have addressed most of my comments well.\nOne remaining question is, in the newly added text\n\"We estimated LD score regression-derived SNP-based heritability on the liability scale to be 0.1969-0.2499, suggesting genetic factors account for 19.7-25% of the common variation in LSS risk depending on the study dataset utilized (Table S6).\"\nWhat is exactly 0.1969-0.2499? Is this an estimate with 95% confidence interval?", "reviewer_5": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_6": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Response to Reviewer 2:\n1. Thank you. We performed the MR analysis as you suggested using prior exposures as an outcome, but we were unable to identify any statistically significant (pFDR<0.05) causal relationships. However, we discovered a statistically significant potential causal relationship when we examined the possible causal relationship between back pain and LSS. The results of these analyses are included in our manuscript as Table S16 and Fig. S84. We described these results and potential two-way causal relationship of back pain in LSS as follows:”We also investigated the possibility of reverse causality, mapping potential causal relationships for the same traits in the other direction as well. Back pain was the only trait for which we observed a statistically significant potential causal relationship in both directions (Fig. S84, Table S16). The observed potential two-way causal relationship is intriguing and may be partly explained by the fact that those suffering from back pain usually seek treatment and are diagnosed.”. Based on these results, we also removed the previous limitation regarding potential reverse causality from the limitations section of our manuscript.\n\nResponse to Reviewer 4:\n1. We appreciate you bringing this to our attention. The sentence in question is a little ambiguous, so we changed it in the manuscript to say: \"Depending on the study dataset used, the LD score regression-derived SNP-based heritability on the liability scale ranged between 0.1969-0.2499, indicating that genetic factors explain 19.7-25% of the common variation in LSS risk.\" ."}]

Decision letter