ECB wage tracker updated with wage agreements signed up to end of November 2025; forward-looking horizon extended to end of December 2026
Forward-looking information indicates easing of negotiated wage growth, consistent with data published following October 2025 Governing Council meeting
ECB wage tracker with unsmoothed one-off payments at 3.0% in 2025 and 2.7% in 2026
The European Central Bank (ECB) wage tracker, which covers active collective bargaining agreements, indicates negotiated wage growth with smoothed one-off payments of 3.2% in 2025 (based on a coverage of 49.5% of employees in participating countries) and 2.3% in 2026 (based on a coverage of 28.8%). The ECB wage tracker with unsmoothed one-off payments indicates negotiated wage growth of 3.0% in 2025 and 2.7% in 2026. The wage tracker excluding one-off payments indicates an easing of negotiated wage growth from 3.9% in 2025 to 2.6% in 2026. The headline ECB wage tracker is better suited to describing quarterly or monthly dynamics in negotiated wages as it smoothens one-off payments over time. Meanwhile, the ECB wage tracker with unsmoothed one-off payments is better suited to describing yearly dynamics, ensuring that one-off payments are not smoothed twice when constructing the yearly outcomes.
For 2026, the headline ECB wage tracker stands at 2.0% in the first quarter, 2.1% in the second quarter, 2.5% in the third quarter and 2.7% in the fourth quarter. The rise in the wage path over the course of the year is related to the dissipation of the mechanical downward effect of large one-off payments that were made in 2024 but not in 2025. The ECB wage tracker also suggests that there is less dispersion in negotiated wage pressures across the different euro area countries in 2026 in comparison with previous years.
The ECB wage tracker with unsmoothed one-off payments (3.1% in the first quarter, 2.5% in the second quarter, 2.4% in the third quarter and 2.7% in the fourth quarter) also reflects the more stable and less volatile outlook in negotiated wage growth for 2026 in comparison with previous years. The wage tracker excluding one-off payments stands at 2.8% in the first quarter, 2.6% in the second quarter, 2.5% in the third quarter and 2.7% in the fourth quarter, which also suggests more moderate negotiated wage dynamics than in previous years. The employee coverage in 2026 stands at 36.9% in the first quarter, 30.1% in the second quarter, 24.8% in the third quarter and 23.4% in the fourth quarter. See Chart 1 and Table 1 for further details.
Since the previous data release in November 2025, the ECB wage tracker has been expanded to retroactively include collective agreements in Finland from January 2015 onwards. The forward-looking horizon has been extended to the end of December 2026, providing some initial insights for the full year.
Overall, the ECB wage tracker may be subject to revisions, and the forward-looking component should not be interpreted as a forecast, as it only captures the information that is currently available for active collective bargaining agreements. Moreover, the ECB wage tracker does not track the indicator of negotiated wage growth precisely and deviations are to be expected over time. For a more comprehensive assessment of wage developments in the euro area, please refer to the December 2025 Eurosystem staff macroeconomic projections for the euro area, which indicate a yearly growth rate of compensation per employee in the euro area of 4.0% in 2025 and 3.2% in 2026.
The ECB publishes four wage tracker indicators for the aggregate of nine participating euro area countries on the ECB Data Portal.
Chart 1
ECB wage tracker: forward-looking signals for negotiated wages and revisions to previous data release
Sources: ECB calculations based on data provided by the Nationale Bank van België/Banque Nationale de Belgique, the Belgian Federal Public Service Employment, Labour and Social Dialogue, the Belgian National Social Security Office, the Deutsche Bundesbank, the Bank of Greece, the Banco de España, the Banque de France, the Banca d’Italia, the Dutch employers’ association AWVN, the Oesterreichische Nationalbank, Suomen Pankki – Finlands Bank, Elinkeinoelämän keskusliitto, Tilastokeskus and Eurostat. The indicator of negotiated wage growth is calculated using data from the Belgian Federal Public Service Employment, Labour and Social Dialogue, the Deutsche Bundesbank, the Ministerio de Empleo y Seguridad Social, the Banque de France, the Istituto Nazionale di Statistica (ISTAT), the Centraal Bureau voor de Statistiek, Statistik Austria, Tilastokeskus, Haver Analytics and Eurostat.
Notes: Dashed lines denote forward-looking information (which is not yet available for the indicator of negotiated wage growth). The latest observations are for December 2026 for the ECB wage tracker indicators (left panel), September 2025 for the indicator of negotiated wage growth (left panel) and September 2026 for the revisions to the previous data release (right panel).
What do the four different indicators show?
The headline ECB wage tracker is a tracker of negotiated wage growth that includes collectively agreed one-off payments, such as those related to inflation compensation, bonuses or back-dated pay, which are smoothed over 12 months.
The ECB wage tracker excluding one-off payments reflects the extent of structural (or permanent) negotiated wage increases.
The ECB wage tracker with unsmoothed one-off payments is constructed using a methodology that, in terms of both data sources and statistical methodology, is conceptually similar to, but not necessarily the same as, that used for the ECB indicator of negotiated wage growth.
The share of employees covered is the percentage of employees across the participating countries that are directly covered by ECB wage tracker data. This indicator provides information on the representativeness of the underlying (negotiated) wage growth signals obtained from the set of wage tracker indicators for the aggregate of the participating countries. Employee coverage differs across countries and within each country over time (more details can be found in Table 2).
Table 1
ECB wage tracker summary
(percentages)
ECB wage tracker
Coverage
Headline indicator
With unsmoothed one-off payments
Excluding one-off payments
Share of employees (%)
2013-24
2.2
2.3
2.1
49.5
2025
3.2
3.0
3.9
49.5
2026
2.3
2.7
2.6
28.8
Q1 2025
4.7
2.7
4.5
49.7
Q2 2025
4.0
4.1
4.3
49.9
Q3 2025
2.3
2.1
3.5
49.4
October 2025
2.0
3.1
3.3
48.9
November 2025
1.8
3.0
3.1
48.8
December 2025
1.9
3.4
3.1
48.7
January 2026
1.8
2.9
2.9
38.5
February 2026
2.1
3.6
2.8
36.2
March 2026
2.1
2.7
2.7
36.0
Q2 2026
2.1
2.5
2.6
30.1
Q3 2026
2.5
2.4
2.5
24.8
Q4 2026
2.7
2.7
2.7
23.4
Sources: ECB calculations based on data provided by the Nationale Bank van België/Banque Nationale de Belgique, the Belgian Federal Public Service Employment, Labour and Social Dialogue, the Belgian National Social Security Office, the Deutsche Bundesbank, the Bank of Greece, the Banco de España, the Banque de France, the Banca d’Italia, the Dutch employers’ association AWVN, the Oesterreichische Nationalbank, Suomen Pankki – Finlands Bank, Elinkeinoelämän keskusliitto, Tilastokeskus and Eurostat. Notes: ECB wage tracker indicators reflect yearly growth in negotiated wages as a percentage. Coverage is defined as the share of employees in participating countries as a percentage. Rows with values in italics and bold refer to the forward-looking aspect of the respective indicators. Data are subject to revisions.
Table 2
Employee coverage by country
(share of employees in each country, percentages)
Belgium
Germany
Greece
Spain
France
Italy
Netherlands
Austria
Finland
Euro area
2013-24
37.6
42.5
10.7
62.3
52.1
48.7
64.1
60.6
66.3
49.5
Q1 2025
44.7
45.0
19.2
47.3
56.2
47.4
62.2
77.6
62.5
49.7
Q2 2025
44.9
46.0
16.7
47.3
56.0
47.7
61.8
76.6
62.6
49.9
Q3 2025
44.7
45.9
10.3
46.7
55.5
47.6
61.4
76.0
62.7
49.4
Q4 2025
44.7
45.7
10.3
46.3
53.7
47.3
61.2
75.0
62.4
48.8
Q1 2026
44.9
40.9
10.1
17.5
29.9
46.0
55.1
53.9
62.2
36.9
Q2 2026
44.8
34.4
9.9
12.6
13.9
44.9
53.0
40.8
61.9
30.1
Q3 2026
44.8
26.5
9.8
6.6
9.3
44.8
40.9
35.0
59.3
24.8
Q4 2026
44.7
25.9
9.8
3.7
2.6
43.8
37.7
33.9
57.1
23.4
Sources: ECB calculations based on data provided by the Nationale Bank van België/Banque Nationale de Belgique, the Belgian Federal Public Service Employment, Labour and Social Dialogue, the Belgian National Social Security Office, the Deutsche Bundesbank, the Bank of Greece, the Banco de España, the Banque de France, the Banca d’Italia, the Dutch employers’ association AWVN, the Oesterreichische Nationalbank, Suomen Pankki – Finlands Bank, Elinkeinoelämän keskusliitto, Tilastokeskus and Eurostat. Notes: The euro area aggregate comprises the nine participating wage tracker countries. The coverage shows the relative strength of wage signals for each country and the euro area. The historical average is calculated from January 2015 for Finland, January 2016 for Greece and February 2020 for Austria. For the other countries, it is calculated from January 2013 to December 2024. Rows with values in italics and bold refer to the forward-looking aspect of the indicator. Data are subject to revisions.
For media queries, please contact Benoit Deeg, tel.: +491721683704
Notes
The ECB wage tracker is the result of a Eurosystem partnership currently comprising the European Central Bank and nine euro area national central banks: the Nationale Bank van België/Banque Nationale de Belgique, the Deutsche Bundesbank, the Bank of Greece, the Banco de España, the Banque de France, the Banca d’Italia, De Nederlandsche Bank, the Oesterreichische Nationalbank and Suomen Pankki – Finlands Bank. It is based on a highly granular database of active collective bargaining agreements for Belgium, Germany, Greece, Spain, France, Italy, the Netherlands, Austria and Finland. The wage tracker can be used to help assess wage pressures in the euro area.
The wage tracker methodology uses a double aggregation approach. First, it aggregates the highly granular information on collective bargaining agreements and constructs the wage tracker indicators at the country level using information on the employee coverage within each country. Second, it uses this information to construct the aggregate for the euro area using time-varying weights based on the total compensation of employees among the participating countries.
Given that the forward-looking nature of the tracker is dependent on the underlying collective bargaining agreements database, the wage signals should always be considered conditional on the information available at any given point in time and thus subject to revisions. This is particularly relevant at the turn of the year as many agreements are signed or renewed in the first quarter in some countries.
The results in this press release do not represent the views of the ECB’s decision-making bodies.
Transitioning to renewables is critical when it comes to confronting the climate crisis, and Germany is seeing this advance at the household level.
Small solar devices that can be plugged into household sockets are growing ever more popular in the country, with more than 1 million installed in the past three years.
The modules are usually about 2 square meters (21.5 square feet) in size, with up to four included in a minisystem, and easily installed on areas such as balconies, where they work as safely as other household appliances — only in reverse. The electricity flows from the solar module via an inverter through the household socket back into the power grid.
Increasingly, these systems also include battery storage, meaning that excess electricity can be saved for later use.
A resident of Cologne hopes his solar installation will cover most of his electricity needsImage: Gero Rueter/DW
Electricity from your own balcony
The rapidly falling cost of solar power and battery storage is a major climate success story of recent years, helping renewables overtake coal for the first time in global electricity generation in 2025.
The plummeting prices have filtered down to the household level in Germany. The price of solar panel systems for balconies has halved in the last two years, with small models available from around €200 ($233) and large ones that include storage costing under €1,000 ($1166). In Germany, they generate electricity for less than half the cost of electricity from the grid.
According to the Berlin University of Applied Sciences (HTW), the purchase usually pays for itself within four to seven years. After that, the electricity households generate for themselves is free.
Solar modules can keep working for over 30 years, and the batteries “can be expected to have a service life of 10-15 years,” said Volker Quaschning, professor of renewable energy systems at HTW. With four modules and storage, about half of the electricity requirements of a two-person household in Central Europe can be covered.
Plug-in solar systems with storage significantly reduce electricity costs in homesImage: Sabine Gudath/IMAGO
Germany leading the pack
“Most plug-in solar devices are still sold in Germany, far ahead of the rest of the world,” says David Breuer, managing director of Yuma, a German-based company selling plug-in solar devices.
Though sunnier regions elsewhere have the potential to generate far more electricity, in Germany falling prices, improved technology and political support have helped drive a solar balcony boom.
Since 2023, private solar installations in the country have been exempt from VAT, and, since autumn 2024, tenants and apartment owners have been allowed to install solar modules on their balconies themselves.
Devices with a module output of up to 2,000 watts are permitted in most EU countries, and the devices are allowed to feed up to 800 watts of electricity directly into the residential grid. This limitation protects the power lines in the home from overload, making it safe to use.
Interest is now spreading to other countries, including many in the EU, as well as Brazil, the United States and Japan.
“We just had a delegation from Tokyo visiting. They want to introduce plug-in solar devices and were gathering information about technical safety,” Thomas Seltmann, an expert on plug-in solar devices at the German Solar Industry Association, told DW.
Reducing energy costs
Germany is aiming to be climate-neutral by 2045. Plug-in solar devices could cover up to 2% of electricity demand by then, Claudia Kemfert, head of the Energy, Transportation, Environment Department at the German Institute for Economic Research, told DW. So far, most solar power in the country comes from rooftop installations, followed by large solar parks.
For many customers, a plug-in system for the balcony is just the beginning. “They are a gateway to other measures such as larger photovoltaic systems or the purchase of an electric car or a heat pump,” says Christoph Kost, head of energy systems analysis at the Fraunhofer Institute for Solar Energy Systems ISE, a German research organization.
“Plug-in solar devices enable people to become part of the energy transition themselves, reduce their electricity costs and make themselves less dependent on energy price fluctuations,” Kemfert said.
The hidden problem with solar (and how to solve it)
Get advice before buying
It is important to be well-informed before buying, said Tobias Otto, from the German Solar Energy Association, which provides independent advice.
This should start with considering first how many modules will fit on a balcony, terrace or roof and at what angle they can be installed, as well as how the sun hits the location.
For those with three to four modules, a battery storage unit with intelligent control is often worthwhile. This means electricity demand can be measured at the meter or at sockets and then ensures the battery supplies the exact amount needed. “Without such measuring devices, the storage system cannot usually be controlled effectively,” Otto told DW.
Some battery-powered plug-in devices also have an emergency supply that helps in the event of a power failure. Many can also be set up outdoors, although they do consume power themselves when placed in very cold or warm temperatures.
Experts also advise sticking to reliable suppliers. “There’s a lot of dodgy stuff on offer,” Seltman said. “We therefore recommend buying from specialist retailers.”
The European Bank for Reconstruction and Development (EBRD) is supporting Lithuania’s green transition and capital market innovation with a €50 million landmark investment in the country’s first green asset backed securitisation, originated by ILTE, Lithuania’s national development bank, as part of a €112 million issuance.
This pioneering transaction is the first true sale green securitisation in the Baltic states, setting a new benchmark for sustainable finance. Backed by ILTE’s portfolio of performing energy-efficiency loans, the notes have achieved a AAA rating from two leading international agencies and have been listed on the Nasdaq Vilnius exchange.
By transforming energy-efficiency loans into investable securities, the transaction introduces innovative market-based instruments and shows the strength of Lithuania’s recently reformed securitisation framework. It also creates a replicable model for attracting long-term institutional capital, boosting investor confidence and accelerating the development of the region’s capital markets.
The proceeds of the bond issuance will unlock ILTE’s capital, enabling it to significantly expand lending for energy-efficiency upgrades in multi-apartment buildings nationwide. This will support Lithuania’s ambitious goal of renovating 10,000 buildings by 2030. The initiative will benefit local small and medium-sized enterprises and create jobs, as well as delivering tangible environmental gains by reducing energy consumption and cutting CO₂ emissions.
The EBRD’s participation sends a strong signal to the market, introducing a novel green business structure that recycles capital for further investment in the renovation of buildings. The Bank’s expertise in securitisation structures and its targeted policy engagements in Lithuania have been instrumental in making this transaction possible, reinforcing ILTE’s capacity to diversify funding and deploy new energy-efficiency loans.
The EBRD’s Head of the Baltic States, Tomas Kairys, said: “This issuance is a milestone not just for ILTE, but for Lithuania and the entire Baltic region. By backing this pioneering securitisation, we’re sending a clear signal to investors at home and abroad: Lithuania’s capital market is growing in both size and sophistication. We look forward to supporting more innovative green issuances that deepen and strengthen the region’s financial markets, and we are pleased to continue our successful cooperation with ILTE to ensure that Lithuania’s building modernisation programme keeps expanding.”
Dainius Vilčinskas, the CEO of ILTE, said: “This issuance sends a clear signal to foreign investors that Lithuania’s capital market is open and reliable for long-term investments, and for us it marks a new stage of development. We have succeeded in attracting strong international investors and implementing a securitisation structure that has not previously been used in the Baltic states. The funds raised will significantly expand financing for the renovation of apartment buildings and ensure that modernisation projects move forward faster and more efficiently.”
ILTE, Lithuania’s national development bank, is a longstanding partner of the EBRD, supporting the country’s strategic economic objectives and sustainable growth. Working directly, through ILTE Group companies, and in cooperation with other financial market participants, the institution focuses on improving access to financing for businesses, the public sector, and clients in agriculture and fisheries. ILTE plays a key role in attracting private capital to support domestic enterprises, investing in projects that drive innovation, enhance competitiveness, promote sustainability and strengthen energy efficiency.
The EBRD is a leading institutional investor in Lithuania and the Baltic states. To date, the Bank has invested more than €2 billion in the country across 151 projects.
Porsche has always seen itself as part of society and embraces this responsibility. That is why numerous charitable initiatives were supported again in 2025. Even in challenging times, the sports car manufacturer considers it essential to engage socially. CEO Dr. Oliver Blume and Board Member for Human Resources Vera Schalwig have once again underlined the importance of this commitment for Porsche and actively championed good causes.
Make-A-Wish
Mission accomplished: To mark its 75th anniversary in 2023, Porsche donated around €1.8 million to Make-A-Wish. The organisation fulfils dreams and wishes for seriously ill children and young people worldwide. Inspired by the Porsche 356 – the first Porsche sports car – the goal was to grant 356 wishes within three years. Now, Dr. Oliver Blume, Chairman of the Executive Board of Porsche AG, proudly announced that every wish has been fulfilled as planned.
These wishes were as diverse as the stories behind the children’s and families’ journeys: a winter wonderland in the Rockies, a tandem skydive, a trip to the seaside, a visit to a locomotive museum – across the globe, Make-A-Wish and Porsche made children’s hearts beat faster and helped them forget their often difficult everyday lives for a few precious hours. Dr. Blume commented: “The life stories behind these wishes move me deeply. I am all the happier that, with the support of Make-A-Wish, we were able to give the children and their families a little hope.”
YOU Foundation
Just before Christmas, Porsche AG received a special honour: Dr h.c. Ute-Henriette Ohoven, UNESCO Special Ambassador and founder of the YOU Foundation, presented Porsche with the UNESCO Medal for its support of an educational project. The initiative was launched by the YOU Foundation in response to the devastating earthquakes in Turkey and Syria, which left many people in severe hardship through no fault of their own.
The foundation provided social and emotional support to children and young people affected by the earthquakes by introducing mobile educational programmes on site. This ensured access to high-quality learning content and allowed children to continue their education despite the disaster’s impact. Porsche contributed more than €300,000 to make the project possible.
ARTHELPS: Psychological support through art therapy
ARTHELPS is a non-profit organisation founded by artists and creatives to support people from socially disadvantaged backgrounds or those living in crisis regions. Art enables individuals to express feelings and experiences in a unique way and to unlock their own creativity. Building on this idea, ARTHELPS launched an art therapy programme for children and young people affected by the conflict in Ukraine. This approach gives therapists access and allows them to provide targeted help to counter psychological problems. Since 2022, Porsche has donated more than €200,000 for this purpose. As a result, over 1,500 group therapy sessions and 1,400 individual sessions for children, adolescents and adults have been made possible.
Charitable projects in the Stuttgart region
“Aktion Weihnachten”
The „Aktion Weihnachten“ (“Christmas Campaign”) association supports people in need in Stuttgart and the surrounding region by funding various local projects. In 2025, the initiative focused on victim and trauma counselling provided by the Seehaus e.V. association in Leonberg, which assists people affected by crime. This year, targeted support was also given to people living with dementia – in particular through “robot cats” and sound bowl therapy. Porsche first contributed financially to this campaign in 2009. In 2025, Verónica Sapena-Mas, Head of Funding Projects at Porsche AG, presented the association with a donation cheque for €10,000.
“Weihnachtsmann & Co.”
Porsche AG has been supporting the charitable association “Weihnachtsmann & Co.” (“Father Christmas & Co.”) since its founding around 50 years ago. This year, Vera Schalwig, Member of the Executive Board for Human Resources and Social Affairs at Porsche AG, presented the association with a donation cheque for €10,000. With this contribution, “Weihnachtsmann & Co.” funds charitable institutions and organisations in the Stuttgart region.
The donation was once again accompanied by voluntary work: Porsche apprentices and a dedicated team from R&D helped at the association’s sales stand at the Stuttgart Christmas market. “I think it’s fantastic that our apprentices have been involved here for years. It broadens their horizons and strengthens their social skills,” says Vera Schalwig.
Olgäle Foundation for Sick Children e.V.
Porsche also maintains a long-standing relationship with the Olgäle-Stiftung für das kranke Kind e.V (Olgäle Foundation for Sick Children e.V.), established in Stuttgart in 1997. The foundation’s mission is to support sick children and their parents during their stay at the Olgahospital children’s clinic (“Olgäle”). In addition to ensuring optimal medical care, the foundation places great emphasis on social aspects, a child-friendly environment and psychosocial support for young patients.
This year brought a very special surprise: Vera Schalwig, Member of the Executive Board for Human Resources and Social Affairs, visited the children and described the initiative as a “matter close to her heart”: “I am deeply impressed by the optimism of the children and their families. I’m keeping my fingers crossed for a speedy recovery for all of them.”
We determined migraine prevalence using EHR ICD codes and self-report Baseline Survey data (Supplementary Data 4). MVP Baseline Survey data was unavailable for 280,957 individuals, and Black and Hispanic women were more likely not to have Baseline Survey data (58 and 44%, respectively) than other strata (e.g., White men; 34%). Based on ICD, lifetime migraine prevalence was 10.4% for White, 13.5% for Black, and 13.9% for Hispanic Veterans. Self-reported migraine lifetime prevalence on the Baseline Survey was 9.3% for White, 12.5% for Black, and 13.2% for Hispanic Veterans. Women had a higher prevalence of migraine than men, with the highest ICD prevalence in Hispanic women (36.9%) and the lowest among White men (8.5%). The GWAS sample cases and control definitions were derived from the combination of EHR and the Baseline Survey (see Methods). There were 118,079 individuals with any history of migraine on either or both EHR or Baseline Survey identified as cases, and 440,255 individuals with no history of migraine on both sources were controls (Supplementary Data 2). While individuals with a history of migraine on any one data source could be counted as cases, individuals with incomplete data (missing Baseline Survey) were excluded from the control classification. Consequently, the GWAS sample proportions (Table 1) do not reflect MVP migraine phenotypic lifetime prevalence but are a function of conservative control definitions to minimize contamination (Supplementary Data 4).
Table 1 MIG-MVP study samples in the discovery GWAS by HARE-derived ancestry and ethnicity.
The MVP migraine GWAS sample included 433,010 participants, 87,859 cases, and 345,151 controls from three HARE-derived ancestral backgrounds. Table 1 shows the average age of participants at enrollment and migraine prevalence by HARE-derived ancestry/ethnicity categories and sex. European HARE-derived ancestry was the largest ancestry group (EUR, n = 338,743; 59,975 cases, 278,768 controls), followed by African HARE-derived ancestry (AFR, n = 65,178; 19,358 cases, 45,820 controls), and HARE-derived Hispanic (HIS, n = 29,089; 8,526 cases, 20,563 controls). People of East Asian and South Asian ancestry were not analyzed due to the low number of Asian individuals in MVP. The MVP sample was predominantly male, with 90.4% (n = 391,622) men and 9.6% (n = 41,388) women, consistent with the distribution (10% women) in the U.S. VA population [51]. Most participants (88.1%) were more than 50 years old, with 402,234 individuals in this age group. Average age varied across the groups from 65.24 (SD = 12.87) years for EUR to 56.93 (14.94) years for HIS and was lower for migraine cases than controls in all ancestries (p < 0.001).
Genome-wide significant loci
The multi-ancestry meta-analysis (META_C; cases = 87,859, controls = 345,151) across three HARE-defined categories identified 36 genome-wide significant (GWS; p < 5 × 10−8) loci when accounting for linkage disequilibrium (r2 > 0.1) with 40 lead SNPs corresponding to 188 mapped genes. Quantile-quantile plots showed inflation of test statistic (Supplementary Fig. 3-l; GCl = 1.365), and polygenic effects accounted for 85.6% of this inflation based on the LDSC intercept of 1.0634 (0.009). The META_C findings are summarized in the Manhattan plot in Fig. 1A and Supplementary Data 5, which also reports all loci across all other strata. In total, we identified 23 GWS loci in the EUR strata (EUR_C), one in the HIS strata (HIS_C), and two in the AFR (AFR_C) strata. A meta-analysis of men (META_M; 66,083 cases, 325,539 controls) and women (META_W; 21,776 cases, 19,612 controls) revealed 26 and 2 GWS loci, respectively (Supplementary Data 6). In META_M, 3 loci were novel -replicated, and 14 were novel unreplicated. In META_W, two loci were novel unreplicated. There were no GWS loci in the EUR women, AFR men, and HIS women. QQ and Manhattan plots for all strata are presented in Supplementary Fig. 2(a-l) and Supplementary Fig. 3(a-l), respectively. Collectively, we identified 106 GWS loci across all nine GWAS and four meta-analyses, with some loci exhibiting overlap in genomic position while yielding different lead SNPs for various analytic strata. In total, 49 distinct loci across the different strata were identified (Fig. 1B, Supplementary Data 5).
Fig. 1: Genome-Wide Significant Loci.
A Multi-ancestry Meta-Analysis Manhattan plot of GWAS of MVP-Migraine. Green stars indicate loci identified in previous migraine GWAS. Red stars indicate novel loci replicated after Bonferroni adjustment (p < 0.05/36) in the GERA-UKBB cohort. Yellow stars indicate novel loci that did not replicate in the GERA-UKBB cohort. Loci are annotated with selected genes mapped by FUMA. GWS loci were labeled with genes which were positionally mapped to the locus by FUMA (within 10KB of the lead SNP). In cases where more than 1 gene positionally mapped to the lead SNP, the gene with the largest number of positionally mapped GWS SNPs was labeled. GWS loci with no positionally mapped SNPs were not labeled. B Summary of genome-wide significant loci across ethnicity and sex. Dark red squares indicate GWS loci. Color bar along the top row indicates if the loci were known, novel-replicated, or novel-unreplicated.
We identified numerous shared loci across sex and ancestry (see Fig. 1B, Supplementary Data 5, Supplementary Data 6), with 11 loci consistently GWS in EUR_M, EUR_C, META_M, and META_C. These “global” loci can be cross-walked to META_C in Supplementary Data 6. For example, locus global_3 (see META_C_10, Supplementary Fig. 4-f) was novel and unreplicated, with lead SNP rs72712556, while locus global 6 (META_C_14, Supplementary Fig. 4-i) lead SNPs varied by strata (Supplementary Data 6).
To prioritize potentially causal SNPs, we ran fine-mapping on the 23 genome-wide significant loci identified in the European population, using the Sum of Single Effects (SuSiE) model [39]. Credible sets were identified for 21 out of 23 loci (sum of posterior inclusion probability>0.95; Supplementary Data 7). Credible sets contained a median of 9 SNPs (range 1–116). SuSiE identified one locus, EUR_11, with 8 separate credible sets. The presence of multiple credible sets within the same locus indicates allelic heterogeneity and the presence of distinct, statistically independent association signals. Many loci had large credible sets, indicating a diffuse signal that is not well localized. However, one locus (EUR_19), had a single SNP (rs11172113) in the credible set, with PIP = 1.0, indicating strong support for causality. This SNP is in an enhancer region of LRP1, a gene that has been previously associated with migraine [23].
Replication of MVP-migraine genome-wide significant loci
We compared our findings to seven previous migraine studies (from non-overlapping cohorts, available through the GWAS catalog) to classify GWS loci identified in this study as known or novel [21, 23, 25, 26, 28, 30, 40]. GWS loci identified in our study were classified as known if any SNP in the locus around the lead SNP (locus area defined by FUMA’s LD clumping algorithm) was found to be genome-wide significant in a previous migraine study. Otherwise, the GWS loci in our study were classified as novel. Supplementary Data 6 presents all loci, genes, and replication status by strata. Of the 49 GWS loci in the MVP cross-strata analysis (Fig. 1B), 13 had prior associations with migraine, and 36 were new to this study (Fig. 1A; Supplementary Data 6). We used a previous large-scale GWA meta-analysis combining GERA and UKB data [21] to replicate the novel loci. Among the 36 new loci, seven loci contained at least one SNP that was nominally significant (after Bonferroni correction for 36 loci tested: p < 0.05/36). We label these seven loci ‘novel-replicated’. A further 23 loci contained at least one SNP with p < 0.05, but did not remain significant after Bonferroni correction (Supplementary Data 6) [21]. Finally, seven GWS loci were novel to the current study and did not replicate in the GERA-UKBB cohort (all p > 0.05). All seven loci had small (ORs near one) and non-significant effects, with three loci showing concordant effect direction and four showing opposing direction. This suggests that the differences may be due to statistical noise, though other sources of heterogeneity are possible. Within the 36 multi-ancestry meta-analyses (META_C) GWS loci, 12 were known (previous GWAS migraine associations), seven had at least one SNP replicate after Bonferroni correction in the GERA-UKBB cohort, and 17 were novel to this study (Fig. 1A). Locus Zoom plots are provided for each novel-replicated and novel-unreplicated SNPs (Supplementary Fig. 4a-kk) across all strata and are described in more detail below.
Given the potential differences between the study population (Veterans, mainly men), and previous migraine GWAS, we sought to understand which previously identified migraine loci were replicated in our data. We identified all previously identified SNPs associated with migraine in the GWAS catalog and cross-referenced them with the results from the current study (using meta-combined results). There were 180 SNPs associated with migraine in the GWAS catalog which were not overlapping a GWS locus from the current study. Of these 180 SNPs, 24 replicated after Bonferroni correction (p < 0.05/180), and an additional 65 SNPs were nominally significant in the study data (p < 0.05) (Supplementary Data 8). In particular, rs1003194 was Bonferroni-significant in our study (p = 0.0015), and was highlighted in a recent large migraine GWAS, mapped to CALCA/B, and proposed as a target of new migraine therapeutics [23].
Genes and pathways mapped to novel and known loci
In the MVP cross-strata analysis incorporating all individual GWAS and meta-analysis results, we identified 283 genes associated with migraine (see Supplementary Data 9 for 188 genes from the META_C results). Of these, 76 genes mapped to the 13 known loci, 61genes mapped to the seven novel-replicated loci, and 146 genes mapped to the 29 novel-unreplicated loci. Among the 13 known loci were well-documented migraine genes (Supplementary Data 10), including LRP1, TRPM8, PRDM16, ASTN2, and PHACTR1, all found to be disease-associated genes in the DISEASES database migraine gene set [52] and identified in previous migraine GWAS [25, 30]. Among the genes mapped to novel replicated loci were CELF4, CAV2, and FAM167A (Supplementary Data 6 and 10). Notably, seven genes mapped to novel loci had been previously associated with migraine in GWAS, including LINGO2 and HTRA1. In these cases, the loci we identified were novel, but the mapped gene was not novel. We also note that several genes we mapped to known migraine loci had not been previously linked with migraine, including ABCC3 and PARVB (Supplementary Data 10). These differences may reflect differences in gene-mapping strategies.
Functional enrichment analysis of the mapped genes revealed significant association with gene sets from human disease databases, including lipidosis, triglycerides, and obesity (DisGeNet), the mammalian phenotype ontology, including decreasing levels of triglycerides and glucose (MPO), cholesterol metabolic process (GO), and other GWAS traits from UKBB and GWAS catalog, including irritability, neuroticism, and fatty acid levels (Supplementary Data 11, Fig. 2A).
Fig. 2: Tissue Expression and functional enrichment analysis of MVP migraine-mapped genes.
AThe dot plot shows genes in pathways of interest significantly enriched in the genes associated with migraine GWS loci. Genes are shown if they were found in at least two traits selected. ** indicates a gene from a known migraine locus. * Indicates a gene from a novel-replicated locus. The bar chart on the right side depicts the negative log p-value of the enrichment of the pathway with the genes associated with GWS migraine loci in the study cohort. BMAGMA tissue expression results for multi-ancestry meta-analysis for aggregated (inset) and specific tissues using GTEx v8 data sets. The y-axis represents -log10 p values from one-sided t-tests. The dotted line shows Bonferroni-corrected significance. Findings show enrichment in the uterus and in five regions of the brain.
Gene tissue expression and pathway analysis
Tissue-specific enrichment analysis of the multi-ancestry meta-analysis (META_C) using MAGMA revealed significant associations with brain tissues, including the frontal cortex, cortex, anterior cingulate cortex, nucleus accumbens, basal ganglia, and the cerebellar hemisphere (Fig. 2B; Supplementary Data 12). MAGMA tissue results are presented in Supplementary Data 12 for META_C and Supplementary Data 13 for META_EUR, with figures for all strata presented in Supplementary Fig. 5 a-l. Consistent with the MAGMA pathway results, DEPICT (Supplementary Data 14), an alternative method for identifying enriched pathways and gene sets, identified 17 brain regions as significantly enriched, largely consistent with the MAGMA results. Enriched brain regions included the cerebrum/cerebral cortex, parietal lobe, telencephalon, and temporal lobe.
MAGMA identified one significant gene set (at FDR adjusted threshold p = 0.05/19054 gene sets = 2.62 × 10−6) associated with cytotoxic T lymphocyte function and immune regulation (2.32 × 10−08, Supplementary Data 15). While no pathway met significance criteria following FDR adjustment (p = 0.05/14465 gene sets = 3.46 × 10−6), the most enriched DEPICT gene set term was the mammalian phenotype ontology term “increased brain weight” (p = 2.4 × 10−5; Supplementary Data 14). Notably, DEPICT does not consider the direction of effect.
It is noteworthy that none of the arterial tissues achieved nominal significance in MAGMA (Fig. 2B) and DEPICT (Supplementary Data 14) showed no vascular pathways (including NOTCH signaling subnetworks). This finding contrasts with earlier migraine GWAS [23, 30], which reported strong associations with vascular pathways such as NOTCH.
SNP-based heritability
The SNP-based heritability (h2SNP) of MIG-MVP was estimated using LDSC. For the liability scale h2SNP, we used a prevalence of 8.2% for men and 30.1% for women, with a prorated combined lifetime prevalence of 10.0%. Liability scale h2SNP (Supplementary Data 16) was estimated at h2SNP = 0.098 (SE = 0.005) for the combined EUR sample, h2SNP = 0.100 (SE = 0.005) for EUR men, and h2SNP = 0.155 (SE = 0.031) for EUR women.
Sex-stratified GWAS results
The genetic correlation between EUR_M and EUR_W for migraine was estimated at rg = 0.93 (SE = 0.07), indicating high trait similarity across sexes. When evaluating sex-stratified GWAS analyses, we observed six GWS loci specific to META_M (Supplementary Data 5), which may contain sex-specific signatures. Locus META_M_18, (Supplementary Fig. 4-cc) is novel to our study but was nominally replicated in a previous migraine GWAS [21]. The lead SNP in this locus falls in an exonic region of the gene AHNAK. Multiple SNPs in this locus have high CADD scores ( > 20), indicating the potential for impact on gene function. Other novel loci specific to men included the unreplicated META_M_5, (Supplementary Fig. 4-y) and associated with KIF3C, RAB10, EPT1, DRC1, MAPRE3, KHK, SNX17 associated with diabetes, and C-reactive protein function. In addition, unreplicated META_M_24, (Supplementary Fig. 4-ee), is associated with LAMP5 and PAK7, both associated with depression.
The women sample was underpowered (cases = 21,776, controls = 19,612), comprising only 9% of the MVP population and revealing only two novel GWS loci. Both loci were novel and unreplicated (Supplementary Data 6). META_W_2 (Supplementary Fig. 4-ff), is associated with LINGO2, a gene previously associated with migraine [21, 23]. META_W_1 (Supplementary Fig. 4-gg) is associated with CMTM1 and CMTM3, genes not previously associated with migraine. In addition, loci associated with migraine in our and previous GWAS trended towards significance in the meta-analysis of women in the MVP (Supplementary Data 5; META_W [e.g., rs11172113, p = 1.42E-06 on LRP1]).
We evaluated the sex-stratified results of four SNPs reported in the GERA-UKB migraine GWAS [53]. Choquet et al. reported rs1047891(CPS1), rs11718509 (PBRM1), and rs10150336 (SLC25A21), rs7858153 (ASTN2) as significantly associated with migraine in women (P < 5.0 × 10−8) but not men. We evaluated these variants within the MVP men and women cohorts. One of these variants trended toward significance in the MVP META_W sample (rs11718509; p = 0.009) and two towards significance in META_M (rs7858153, p = 0.002; rs1047891, p = 0.059).
Ancestry-stratified results
One locus was specific to AFR Women (AFR_W_1, Supplementary Fig. 4-jj) with lead SNP rs2864065 associated with LSAMP linked to neuronal activity within the limbic system and metabolic syndrome and body mass index. A locus in the AFR Combined strata (AFR_C_1, Supplementary Fig. 4-ii) coding for IRX1 that may be involved in the development of the nervous system. A locus in the HIS men strata (HIS_M_1, Supplementary Fig. 4-ii) was associated with seven genes, including RNF4, previously associated with back pain, TNIP2, associated with inflammation, and GRK4 involved in G protein-coupled receptor signaling and vascular regulation. None of the non-EUR lead variants showed nominal significance in any of the EUR strata (all p > 0.05).
Genetic correlations with psychiatric disorders and brain regions
We evaluated LDSC genetic correlations between the EUR cohorts and meta-analysis with summary statistics from the PGC, TBI, and brain structure imaging regions from ENIGMA. Within the psychiatric domain (Supplementary Data 17, Fig. 3A), significant correlations ranged from rg = 0.27 (SE = 0.05) for Tourette Syndrome to rg = 0.76 (SE = 0.04) for TBI for the MVP EUR_M sample. Within the brain imaging data (Supplementary Data 17, Fig. 3A), significant correlations ranged from rg = −0.28 (SE = 0.08) between EUR_W and brainstem volume to rg = 0.09 (SE = 0.04) in caudate nucleus volume for the EUR_M sample. Significant differences in magnitude between genetic correlations for men and women (Fig. 3A) were observed for MDD (z = 2.34, p = 0.02), PTSD (z = 4.11, p < 0.001), brainstem volume (rg = 0.20, SE = 0.09), and globus pallidus volume (rg = 0.26, SE = 0.12).
Fig. 3: Relationship to other traits.
AGenetic correlations between MVP migraine (men and women) and other traits, including GERA-UKBB migraine, EUR_M, EUR_W, psychiatric disorders, and brain metrics. Error bars indicate 95% CI. Black squares represent EUR_M, and white squares represent EUR_W. BGenomic Structural Equation Modeling (GSEM) path model with standardized estimates and standard errors of the shared genetic architecture of EUR_C migraine, traumatic brain injury (TBI), post-traumatic stress disorder (PTSD), and major depressive disorder (MDD). This path model shows the genetic association between migraine (MIG, in blue) and TBI, PTSD, and MDD. Straight arrows represent partial regression coefficients, and bidirectional curved arrows indicate genetic correlations among TBI, PTSD, and MDD. Dotted lines are non-significant; solid lines are significant at p < 0.001. When modeled together, 56% of the genetic variance in MIG is shared with TBI, PTSD, and MDD, and UMIG captures the unexplained variance in MIG (0.44, corresponding to 44%). C Best-fitting confirmatory factor analysis (CFA) path model and standardized estimates of MVP EUR_C migraine (MIG) and six psychiatric disorders. Multivariate LD-score regression of odd chromosomes informed the CFA model based on the covariance matrix of even chromosomes. ADHD attention-deficit/hyperactivity disorder; TBI traumatic brain injury; PTSD post-traumatic stress disorder; ALCH problematic alcohol use; MDD major depressive disorder; ANX anxiety. Two correlated genetic factors (F1g and F2g) capture the genetic liability shared by the conditions. Straight arrows represent partial regression coefficients and standard errors, capturing the degree of association between a latent genetic factor and each disorder, and bidirectional curved arrows indicate the genetic correlation between latent factors. The proportion of genetic variance in each disorder explained by the latent factors can be calculated by squaring the factor loading (e.g., for MIG = 0.732 = 0.53 or 53%). Genetic variance unexplained by the latent factors in this model is represented by the UMIG oval (e.g., for MIG = 0.47 or 47%). F1g was most strongly associated with TBI, MIG, ADHD, and PTSD, which cross-loaded on both factors. F2g was also associated with ALCH, MDD, and ANX. The model indicated distinct but correlated genetic architecture contributing to these related conditions and suggests that the association with migraine and ADHD, TBI, and PTSD is due to shared genetic variants, while the association with ALCH, MDD, and ANX is at least in part due to the genetic correlation between the two latent factors rather than direct genetic overlap.
To evaluate the correlation between specific cerebral regions and migraine while accounting for ICV, we simultaneously estimated the relationship between volumes of specific regions and migraine using GSEM path models. This approach incorporated the correlation between the brain region and ICV and the direct effect of ICV. Most cerebral regions did not exhibit a statistically significant association with migraine after adjusting for ICV (Supplementary Data 18, Supplementary Fig. 6). However, a notable positive association with migraine was observed for the caudate nucleus (β = 0.107, SE = 0.043, p = 0.012), suggesting that larger CN volume may correlate with an increased risk of migraine.
Genomic structural equation modeling
To further explore the shared genetic architecture of migraine and common Veteran migraine comorbidities of TBI, PTSD, and MDD, we modeled the associations simultaneously using a GSEM path model (Fig. 3B) in EUR_C based on LDSC-derived correlations. This approach included both the genetic associations of TBI, PTSD, and MDD with migraine while accounting for the genetic correlations among these traits. Our findings suggest that MDD and PTSD show marginally significant (β = 0.10, SE = 0.06, p = 0.07) and non-significant (β = −0.07, SE = 0.14, p = 0.644) associations with migraine, respectively, while TBI, even after accounting for MDD and PTSD (which are genetically correlated, rg = 0.64, SE = 0.03), maintains a strong association (β = 0.75, SE = 0.12, p < 0.001). Despite the strong genetic associations between migraine and TBI, PTSD, and MDD (Fig. 3A), only the association with TBI remains influential when modeling these conditions simultaneously.
We conducted EFA and CFA on EUR_C migraine and psychiatric disorders with strong genetic correlations (Fig. 3A). Parallel analysis (Supplementary Fig. 7) indicated the presence of one to two factors, and we evaluated one-, two-, and three-factor EFA factor loadings, comparing the corresponding CFA models for the EUR_C migraine sample. EFA factor loadings for two and three-factor models are presented in Supplementary Data 19A, and CFA model fit statistics in Supplementary Data 20A. The one-factor CFA model provided an adequate fit to the data (df = 14, AIC = 75.818, SRMR = 0.1203), but the two-factor model provided the best fit with a lower AIC and improved SRMR (df = 12, AIC = 43.184, SRMR = 0.0471). The three-factor model introduced an additional not-identified factor with a single trait (PTSD) and only a slight improvement in SRMR but an increase in AIC (df = 11, AIC = 44.032, SRMR = 0.038). Consequently, the two correlated factor model provided the most parsimonious and best-fitting solution. (Fig. 3C) The first latent factor (F1) was associated with migraine and neuropsychiatric disorders (ADHD, PTSD, and TBI), while the second factor was associated with anxiety/stress, mood, and alcohol use disorders (ALCH, MDD, PTSD, and ANX). The two latent factors were correlated at r = 0.61 (SE = 0.06), showing a moderate positive relationship between them. The model explained 53% of the genetic variance in EUR_C migraine. As a sensitivity analysis, we repeated the EFA and CFA using the EUR_M and EUR_W GWAS results, both of which indicated that the two-factor model, with similar factor loadings (Supplementary Data 19B-C), provided the best fit (Supplementary Data 20B-C).
Genetic overlap of migraine with other traits
We conducted an unbiased LDSC screen of genetic correlations with 844 publicly available GWAS using the Complex Trait Genetics Virtual Lab (CTG-VL) [46]. These data include phenotypes from the UK Biobank, GIANT consortium, Psychiatric Genomics Consortium (PGC), FinnGen, and CHARGE, identifying 305 significant genetic correlations with migraine (Bonferroni-corrected p < 5.92 × 10−5; Supplementary Data 21). Notable correlations were observed between migraine and multisite chronic pain (rg = 0.71, SE = 0.02), absence of pain (rg = −0.67, SE = 0.03), back pain (rg = 0.54, SE = 0.03), headache in the last month (rg = 0.71, SE = 0.04), hip pain (rg = 0.51, SE = 0.04), as well as medication use including paracetamol (rg = 0.65, SE = 0.04; rg = 0.68, SE = 0.04), codeine-acetaminophen (rg = 0.72, SE = 0.06), and omeprazole (rg = 0.67, SE = 0.05). Mental health conditions included MDD (rg = 0.53, SE = 0.03) and ADHD (rg = 0.47, SE = 0.03). We observed modest but significant associations with triglycerides (rg = 0.153, SE = 0.033) and circulating calcium (rg = 0.091, SE = 0.021), as well as a negative correlation with HDL cholesterol (rg = −0.156, SE = 0.036), while the correlations with total and LDL cholesterol were not significant. Additionally, our results showed positive genetic correlations between migraine and cardiovascular disease, including stroke (rg = 0.544, SE = 0.124), myocardial infarction (rg = 0.229, SE = 0.045), and angina (rg = 0.327, SE = 0.042). Finally, general health indicators including long-standing illness or disability (rg = 0.52, SE = 0.03), and impairment factors, including the inability to work due to health-related issues (rg = 0.62, SE = 0.04) and financial difficulties arising from illness (rg = 0.55, SE = 0.03), were also associated.
We compared the genetic correlation from the unbiased LDSC screen between the MVP migraine cohort and the GERA-UKBB cohort. The genetic correlation between MVP EUR_C and the GERA-UKB migraine meta-analysis [53] was rg = 0.76 (SE = 0.04). As expected, the MVP EUR_C and GERA-UKB showed similar associations related to headache pain (Fig. 4B; Supplementary Data 21). MVP EUR_C also showed associations with multisite chronic pain (rg = 0.563, 0.055, p = 8.68 × 10−25) and lower back pain (rg = 0.524, 0.060, p = 2.22 × 10−18) while GERA-UKB did not. We discerned differences unique to the MVP cohort, encompassing socioeconomic factors, musculoskeletal characteristics, and pain-related traits (Fig. 4A and B). Consistent with the genetic correlations with PGC data (Supplementary Data 17), the rg data for GERA-UKB (Supplementary Data 17), indicates minimal significant correlations with psychiatric conditions from CTG-VL.
Fig. 4: LDSC regression genetic correlations.
A Average genetic correlation for groups of traits, for MVP (yellow) and blue (GERA-UKBB). Trait groups are sorted by those that differ most between MVP and GERA-UKBB. B A scatterplot showing the genetic correlation for all traits tested with GERA-UKBB migraine (x-axis) and MVP migraine (y-axis). The dotted line indicates x = y. Yellow circles are those traits that are uniquely significant in MVP; blue squares are those traits that are uniquely significant in GERA-UKBB; purple triangles represent traits that are significantly correlated with both GERA-UKBB and MVP-migraine traits, while gray circles are those traits that are significantly correlated with neither GERA-UKBB nor MVP-migraine. C Genetic overlap between MVP-migraine and GERA-UKB-migraine, computed with MiXeR. The Venn diagram depicts the number of causal variants (standard error) related to MVP-migraine (blue circle) and GERA-UKB-migraine trait (orange circle). All GERA-UKBB causal variants are contained within the set of MVP causal variants. Complete results in Supplemental Fig. 8.
Mendelian randomization
Next, we evaluated the potential causality between traits with nine significant genetic correlations with MVP migraine (Fig. 3A; TBI, ADHD, TS, anxiety, PTSD, alcohol dependence, depression, ICV, surface area) using the CAUSE software for MR. We compared null, shared, and causal models using expected log predictive density (ELPD) differences to test for statistical significance, set at p < 0.01. Of the traits tested, none showed evidence of a significant causal relationship on MVP-migraine (Supplementary Data 22), with all p > 0.05. TBI sharing model approached statistical significance when compared to the null model (DELPD = −10.50, SEDELPD = 4.31, z = −2.45, p = 0.007). Conversely, there was no evidence that MVP migraine caused any of the tested traits (DELPD p < 0.05). The causal model did not provide a significantly better fit than shared polygenicity models, thus indicating no evidence that MVP migraine caused any of the tested traits.
Polygenicity identified in MVP migraine, relative to previous migraine GWAS
We extended our comparison analyses between MVP-migraine and GERA-UKB-migraine genetic architecture using MiXeR, a Gaussian mixture modeling approach to estimate polygenicity. Quantification of the polygenic overlap between MVP-migraine and GERA-UKB-migraine revealed that MVP shared all ~1900 GERA-UKB loci, but there were ~7300 loci predicted to be unique to MVP-migraine (Fig. 4C; Supplementary Data 23, Supplemental Fig. 8). The large number of MVP-migraine unique loci leads us to conclude that MVP migraine is much more polygenic than GERA-UKB despite the high genetic correlation (rg = 0.76, SE = 0.04). The additional polygenicity of MVP-migraine could reflect features specific to the MVP, such as the predominantly men Veteran population. The polygenicity comparison of MVP migraine Men_C and Women_C was inconclusive due to the low sample size in the MVP women population.
Drug-class and drug-set enrichment analyses
We evaluated each gene associated with any GWS loci for its presence within a comprehensive database of known drug targets (OpenTargets Platform) [54]. We identified 76 drugs that are known to target at least one gene identified in our study based on FUMA prioritization (Supplementary Data 24). Seven genes from loci previously associated with migraine in prior studies were linked to ten drugs, including monoclonal antibody drugs (tanezumab, fasinumab, fulranumab) that target nerve growth factor (NGF) function to mediate pain signaling, as well as metformin hydrochloride (which targets NDUFAF4), a well-established treatment for type 2 diabetes with possible neuroprotective effects and previous unsuccessful clinical trial (NCT02593097) for migraine, in addition to menthol, which targets TRPM8. Six novel loci from our study were linked to several medications associated with established migraine pathways. These include TLR4 antagonists (eritoran, resatorvid), which play a role in neuroinflammation targeting TLR4; p38 mitogen-activated protein kinase inhibitors (losmapimod, doramapimod, neflamapimod) targeting MAPK14; and peroxisome proliferator-activated receptor agonists (bezafibrate, seladelpar, lanifibranor) targeting PPARD. Nine novel loci mapped to additional drugs, including calcium channel modulators used in migraine treatment, GABA analogs used for pain management, and immunomodulators.
ECB streamlines how it assesses standardised capital and securitisation operations from January 2026
Approval time reduced to two weeks, down from the current three months
Move allows supervisors to focus on more complex assessments
ECB to check that banks do not overly rely on capital benefits from significant risk transfer securitisations
The European Central Bank (ECB) has streamlined how it assesses banks’ capital and securitisation operations. As of January 2026, banks will be able to receive a faster response from the ECB when they seek to reduce their capital by repurchasing shares or other capital instruments, or to reduce their capital requirements after a significant risk transfer.
The faster processes will apply to standardised operations. Despite the faster process, all global standards and European regulation still apply in full. The normal procedure, which entails a more detailed assessment of the operation and of the risks involved, will still apply to operations ineligible for fast-track processing.
The two new fast-track processes aim to cut approval times to two weeks, down from three months currently. In the broader context of streamlining supervision and making it more efficient and effective, these fast-track processes will save time on routine operations and allow supervisors to focus on more complex assessments.
Capital fast track
EU banking rules require banks to get ECB approval before they buy back shares or repurchase other capital instruments, as these operations reduce banks’ capacity to absorb losses. The ECB therefore checks that these transactions comply with all applicable regulatory requirements. The ECB is streamlining the approval process to become more efficient, while maintaining the same prudential standards as before.
A bank’s plan to repurchase capital instruments other than shares may qualify for the fast-track process if its impact on the bank’s capital ratio is below 100 basis points, and if the bank’s capital is estimated to continue exceeding capital requirements and guidance for at least three years.[1]
For share buybacks to be eligible for fast-track processing, additional conditions apply: the bank must be scored medium or low-risk in its capital adequacy assessment, it must retain a sufficient portion of its profits, and it must demonstrate the ability to meet its capital requirements and guidance under severe financial stress.
The ECB is also launching a streamlined process for banks to submit their applications to reduce own funds. Banks will immediately know whether their application is complete and in principle eligible for fast-track processing. The application will then be reviewed and assessed by the Joint Supervisory Team, which retains the discretion to request additional information, if needed.
Securitisation fast track
When banks transfer risks through securitisation, EU banking rules provide that capital requirements on the securitised exposures may be reduced only if the ECB acknowledges that the securitisation transfers a significant amount of risk to third parties – this is known as a “significant risk transfer” (SRT).
The SRT fast-track process applies to standardised securitisations, namely when the securitised portfolio is performing, not concentrated, and does not contain more than 20% of leveraged loans; when the impact on the bank’s capital ratios of the significant risk transfer is lower than 25 basis points; and when standardised contractual early termination clauses are used.
To ensure that the SRT process does not lead to undue risk-taking and a weakening of resilience, it will be accompanied by increased scrutiny of micro- and macroprudential risks. In case the use of securitisation raises prudential concerns, the ECB will take adequate measures. More specifically, supervision will focus on complex cases and on bank-level assessments of securitisations activities. The ECB will continue to assess the adequacy of banks’ internal governance, risk management and capital management frameworks, including stress testing, to prevent them from overly relying on the capital benefits generated by SRT securitisations and from elevated rollover risk created by large-scale use of synthetic securitisations. Furthermore, the granular data collected via the fast-track template will contribute to enhanced monitoring of the securitisations market by the ECB in its supervisory and macroprudential functions.
Further details about the securitisation fast track, including the full set of criteria for securitisations to qualify for the fast-track process, are provided in Section 3.3 of the Guide on the notification of significant risk transfer and implicit support for securitisations.
For media queries, please contact François Peyratout, tel.: +49 172 8632 119.
SRT securitisation issuance over the last 5 years
This table displays total amounts at origination date for securitisations with significant risk transfer (SRT), as reported by significant banks acting as originators.
in billion euros
Synthetic
Traditional
Total
2020
63
24
87
2021
80
54
134
2022
140
26
166
2023
132
18
150
2024
181
29
210
Source: ECB based on significant banks’ supervisory reporting (Corep C14, field 0130). Amendments in EU securitisation regulations make data prior to 2020 not directly comparable.
Commenting on today’s ONS retail sales figures, Oliver Vernon-Harcourt, head of retail at Deloitte, said:
“A second consecutive month of decline in retail sales will be a big disappointment for retailers, particularly with Black Friday and early Christmas shopping captured in these figures. It could be that some consumers opted to delay spending decisions until after the Budget, but the rise in volumes across department stores, clothing and footwear, and household goods suggests that the start of the discounting period could have enticed consumers into purchases at the end of the month. Meanwhile, supermarket sales declining for a fourth consecutive month is a sign of the times, with food inflation still having a significant impact on consumer budgets and purchasing decisions.
“A successful December, to round off a subdued Golden Quarter, will be at the top of retailers’ wish lists. The bigger picture of retail in 2025 showed a prolonged pressure from weak consumer confidence and challenging economic conditions. With interest rates and inflation continuing to fall, retailers will hope this can spur a rebound in consumer spending. On the other hand, if economic uncertainty continues, the apprehension among both consumers and retailers may continue in 2026.”
ISLAMABAD, Dec 17 (APP):The benchmark KSE-100 Index of the Pakistan Stock Exchange (PSX) on Wednesday closed bearish, shedding 133.44 points, a negative change of 0.08 percent, to settle at 170,313.86 points compared to 170,447.30 points on the previous trading day, according to Pakistan Stock Exchange data.
During the session, the ready market witnessed a trading volume of 1,068.512 million shares with a traded value of Rs51.799 billion, against 1,176.638 million shares valuing Rs53.473 billion in the previous session. Market capitalization increased to Rs19.377 trillion from Rs19.370 trillion a day earlier.
Out of 483 active companies in the ready market, 178 advanced, 255 declined, while 50 remained unchanged.
Bank of Punjab topped the volume chart with 90.619 million shares, followed by Hascol Petroleum with 83.922 million shares and TPL Corporation Limited with 53.151 million shares.
The top gainers included Nestle Pakistan Limited, which rose by Rs49.03 to close at Rs8,000.00, and Hafiz Limited, increasing by Rs47.18 to settle at Rs590.07. On the losing side, PIA Holding Company Limited-B declined by Rs130.00 to close at Rs23,070.00, while Unilever Pakistan Foods Limited fell by Rs125.00 to close at Rs29,000.00.
In the futures market, turnover stood at 222.330 million shares with a traded value of Rs12.532 billion, compared to 271.071 million shares worth Rs13.531 billion in the previous session.
Out of 316 futures-market companies, 113 recorded gains, 199 declined, while four remained unchanged. Among futures contracts, BOP-Dec led with 32.678 million shares, followed by PIAHCLA-Dec with 25.874 million shares and SSGC-DecB with 18.075 million shares.
In the futures market, the top gainers included GHNI-DecB, which rose by Rs17.11 to close at Rs839.20, and UBL-DecB, increasing by Rs12.19 to settle at Rs403.13. On the losing side, HUBC-Jan declined by Rs19.51 to close at Rs225.50, while SAZEW-DecB fell by Rs19.50 to close at Rs1,664.03.