Disease consequences of higher adiposity uncoupled from its adverse metabolic effects using Mendelian randomisation

  1. Susan Martin
  2. Jessica Tyrrell
  3. E Louise Thomas
  4. Matthew J Bown
  5. Andrew R Wood
  6. Robin N Beaumont
  7. Lam C Tsoi
  8. Philip E Stuart
  9. James T Elder
  10. Philip Law
  11. Richard Houlston
  12. Christopher Kabrhel
  13. Nikos Papadimitriou
  14. Marc J Gunter
  15. Caroline J Bull
  16. Joshua A Bell
  17. Emma E Vincent
  18. Naveed Sattar
  19. Malcolm G Dunlop
  20. Ian PM Tomlinson
  21. Sara Lindström
  22. INVENT consortium
  23. Jimmy D Bell
  24. Timothy M Frayling  Is a corresponding author
  25. Hanieh Yaghootkar  Is a corresponding author
  1. Institute of Biomedical and Clinical Science, University of Exeter Medical School, Research, Innovation, Learning and Development building, Royal Devon & Exeter Hospital, United Kingdom
  2. Research Centre for Optimal Health, School of Life Sciences, University of Westminster, United Kingdom
  3. Department of Cardiovascular Sciences, University of Leicester, United Kingdom
  4. NIHR Leicester Biomedical Research Centre, United Kingdom
  5. Department of Dermatology, University of Michigan, United States
  6. Ann Arbor Veterans Affairs Hospital, United States
  7. The Institute of Cancer Research, United Kingdom
  8. Department of Emergency Medicine, Massachusetts General Hospital, United States
  9. Department of Emergency Medicine, Harvard Medical School, United States
  10. Nutrition and Metabolism Branch, International Agency for Research on Cancer, France
  11. MRC Integrative Epidemiology Unit at the University of Bristol, United Kingdom
  12. Population Health Sciences, Bristol Medical School, University of Bristol, United Kingdom
  13. School of Cellular and Molecular Medicine, University of Bristol, United Kingdom
  14. Institute of Cardiovascular and Medical Sciences, University of Glasgow, United Kingdom
  15. University of Edinburgh, United Kingdom
  16. Western General Hospital, United Kingdom
  17. Edinburgh Cancer Research Centre, IGMM, University of Edinburgh, United Kingdom
  18. Department of Epidemiology, University of Washington, United States
  19. Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, United States
  20. Centre for Inflammation Research and Translational Medicine (CIRTM), Department of Life Sciences, Brunel University London, United Kingdom

Abstract

Background:

Some individuals living with obesity may be relatively metabolically healthy, whilst others suffer from multiple conditions that may be linked to adverse metabolic effects or other factors. The extent to which the adverse metabolic component of obesity contributes to disease compared to the non-metabolic components is often uncertain. We aimed to use Mendelian randomisation (MR) and specific genetic variants to separately test the causal roles of higher adiposity with and without its adverse metabolic effects on diseases.

Methods:

We selected 37 chronic diseases associated with obesity and genetic variants associated with different aspects of excess weight. These genetic variants included those associated with metabolically ‘favourable adiposity’ (FA) and ‘unfavourable adiposity’ (UFA) that are both associated with higher adiposity but with opposite effects on metabolic risk. We used these variants and two sample MR to test the effects on the chronic diseases.

Results:

MR identified two sets of diseases. First, 11 conditions where the metabolic effect of higher adiposity is the likely primary cause of the disease. Here, MR with the FA and UFA genetics showed opposing effects on risk of disease: coronary artery disease, peripheral artery disease, hypertension, stroke, type 2 diabetes, polycystic ovary syndrome, heart failure, atrial fibrillation, chronic kidney disease, renal cancer, and gout. Second, 9 conditions where the non-metabolic effects of excess weight (e.g. mechanical effect) are likely a cause. Here, MR with the FA genetics, despite leading to lower metabolic risk, and MR with the UFA genetics, both indicated higher disease risk: osteoarthritis, rheumatoid arthritis, osteoporosis, gastro-oesophageal reflux disease, gallstones, adult-onset asthma, psoriasis, deep vein thrombosis, and venous thromboembolism.

Conclusions:

Our results assist in understanding the consequences of higher adiposity uncoupled from its adverse metabolic effects, including the risks to individuals with high body mass index who may be relatively metabolically healthy.

Funding:

Diabetes UK, UK Medical Research Council, World Cancer Research Fund, National Cancer Institute.

Editor's evaluation

The authors have conducted a robust and very comprehensive study using Mendelian randomisation to disentangle metabolic and non-metabolic effects of overweight on a long list of disease outcomes. They have tested if effects of overweight work through either or both effects for a particular condition. This is an important topic and can help us better understand how overweight influences risk of several important outcomes.

https://doi.org/10.7554/eLife.72452.sa0

Introduction

Obesity is associated with a higher risk of many diseases, notably metabolic conditions such as type 2 diabetes, but many individuals are often relatively metabolically healthy compared to others of similar body mass index (BMI). Whilst these metabolically healthier individuals may be at lower risk of some obesity-related conditions, they may be at risk of conditions that are linked to other aspects of obesity, such as the load-bearing effects. The burden of obesity on individuals and health-care systems is very large, and in the absence of a widely applicable, sustainable treatment or effective public health measures, it is important to understand the disease consequences of obesity, and how they may be best alleviated, in more detail.

To better understand the disease consequences of obesity, many previous studies have used the approach of Mendelian randomisation (MR) (Smith and Ebrahim, 2004). These studies used common genetic variants robustly associated with BMI as proxies for obesity to assess the causal effects of higher BMI on many diseases. MR studies have provided strong evidence that higher BMI leads to osteoarthritis (Tachmazidou et al., 2019), colorectal cancer (Thrift et al., 2015; Suzuki et al., 2021; Bull et al., 2020), and psoriasis (Budu-Aggrey et al., 2019), as well as metabolic conditions such as type 2 diabetes, cardiovascular disease (Hägg et al., 2015), and heart failure (Cheng et al., 2019; Corbin et al., 2016; Fall et al., 2013). Other MR studies indicate that higher BMI may lead to lower risk of some diseases, including postmenopausal breast cancer (Guo et al., 2016) and Parkinson’s disease (Noyce et al., 2017).

Obesity is heterogeneous – for example, for a given BMI, people vary widely in their amount of fat versus fat free mass, predominantly muscle, and their distribution of fat, predominantly subcutaneous versus ectopic and upper versus lower body fat. Even when there is strong evidence of causality, obesity may lead to disease through a variety of mechanisms. Despite many MR studies testing the role of higher BMI in disease, few have attempted to separate and test the different mechanisms that could lead from obesity to disease. Some MR studies have investigated the effects of fat distribution using genetic variants associated with waist-hip ratio (WHR) adjusted for BMI and shown that adverse fat distribution (more upper body, less lower body) leads to higher risk of metabolic disease (Emdin et al., 2017), some cancers (Cornish et al., 2020), and gastro-oesophageal reflux disease (Green et al., 2020).

Previous studies have identified genetic variants associated with more specific measures of adiposity. For example, several studies have characterised variants associated with ‘favourable adiposity’ (FA) or reduced adipose storage capacity using a variety of approaches (Ji et al., 2019; Lotta et al., 2017; Kilpeläinen et al., 2011; Huang et al., 2021). We recently identified 36 FA alleles which are collectively associated with a favourable metabolic profile, higher subcutaneous fat but lower ectopic liver fat (Ji et al., 2019; Martin et al., 2021), resembling a polygenic phenotype opposite to lipodystrophy (Semple et al., 2011). We also identified 38 unfavourable adiposity (UFA) alleles which are associated with higher fat in subcutaneous and visceral adipose tissue, and higher ectopic liver and pancreatic fat (Ji et al., 2019; Martin et al., 2021), resembling monogenic obesity (Supplementary file 1a). We performed MR studies and showed that FA and UFA have opposite causal effects on six metabolic conditions (Martin et al., 2021). While both FA and UFA were associated with higher adiposity, FA was causally associated with lower risk of type 2 diabetes, heart disease, hypertension, stroke, polycystic ovary syndrome, and non-alcoholic fatty liver disease. In contrast, as expected, UFA was associated with higher risk of these conditions. These results confirmed the ability of the two sets of adiposity variants to partially separate out the metabolic from the non-metabolic effects of higher adiposity.

In this study, we aimed to investigate the effects of separate components to higher adiposity on risk of additional metabolic diseases and many non-metabolic diseases. We used genetic variants associated with BMI, body fat percentage, FA, and UFA to understand the components of higher adiposity that are the predominant causes of disease risk. Our findings may give guidance on some obesity-related risks which are not dependent on metabolic consequences, thereby guiding appropriate medical care.

Methods

Study design

An overview of our approach is shown in Figure 1. First, we identified diseases by performing a literature search of studies that had used MR to assess the consequences of BMI on outcome phenotypes. We used the search terms ‘BMI and Mendelian randomisation’ and ‘BMI and Mendelian randomization’. We identified 37 diseases associated with BMI and for which MR studies had previously been performed (Supplementary file 1b). We included all diseases regardless of the MR result in the published study. Second, we reperformed MR studies using BMI as an exposure. Third, for those diseases where MR indicated higher BMI was causal, we tested the effects of body fat percentage to confirm that the causal effect was due to fat mass rather than fat-free mass. Fourth, for diseases where MR suggested the BMI effect was due to excess adiposity, we used genetic variants more specific to the metabolic and non-metabolic components of higher adiposity to help understand the extent to which these factors influence disease.

Study design.

Data sources

We used three data sources for disease outcomes: (i) published genome-wide association studies (GWAS; Okada et al., 2014; Nikpay et al., 2015; Jones et al., 2017; Michailidou et al., 2017; Phelan et al., 2017; Scelo et al., 2017; Tsoi et al., 2017; Day et al., 2018; Mahajan et al., 2018; Malik et al., 2018; O’Mara et al., 2018; Roselli et al., 2018; Schumacher et al., 2018; Wray et al., 2018; An et al., 2019; Ferreira et al., 2019; Huyghe et al., 2019; Jansen et al., 2019; Kunkle et al., 2019; Law et al., 2019; Lindström et al., 2019; Morris et al., 2019; Nalls et al., 2019; Shah et al., 2019; Tachmazidou et al., 2019; Tin et al., 2019; Wuttke et al., 2019; Huyghe et al., 2021) and (ii) FinnGen (FinnGen, 2021) as our main results, and (iii) UK Biobank (RRID:SCR_012815; Collins, 2012) as additional validation. FinnGen is a cohort of 176,899 individuals with linked medical records. UK Biobank is a population cohort of >500,000 individuals aged 37–73 years recruited between 2006 and 2010 from across the UK. For the 37 identified diseases, 25 had summary GWAS data available from both a published GWAS consortium and FinnGen, and 12 diseases had GWAS summary data available in FinnGen only. In addition, data from 31 of the 37 diseases were available in the UK Biobank. No GWAS data were available for Barrett’s oesophagus, but we included gastro-oesophageal reflux. The characteristics of the studies and measures, disease outcomes, and the definition of cases and controls are described in Supplementary file 1ci–iii.

GWAS of UK Biobank participants

For the GWAS of 31 diseases available in UK Biobank, we used a linear mixed model implemented in BOLT-LMM to account for population structure and relatedness (Loh et al., 2015). We used age, sex, genotyping platform, study centre, and the first five principal components as covariates in the model.

Genetic variants

We used four sets of genetic variants as proxies of four exposures (Supplementary file 1d).

Body mass index

In the broadest category, we used a set of 73 variants independently associated with BMI at genome-wide significance (p<5 × 10–8). These variants were identified in the GIANT consortium of up to 339,224 individuals of European ancestry (Locke et al., 2015).

Body fat percentage

We used 696 variants from a GWAS in the UK Biobank (Martin et al., 2021). We used bio-impedance measures of body fat % taken by the Tanita BC-418MA body composition analyser in 442,278 individuals of European ancestry.

The BMI and body fat percentage variants were partially overlapping (n = 5 variants), but we used exposure-trait-specific weights for each variant.

FA variants

There are 36 FA variants (Martin et al., 2021). These variants were identified in two steps. First, they were associated (at p<5 × 10–8) with body fat percentage and a composite metabolic phenotype consisting of body fat percentage, HDL-cholesterol, triglycerides, SHBG, alanine transaminase, and aspartate transaminase. Second, in a k-means clustering approach (a hard clustering approach) (Martin et al., 2021), they formed a cluster of variants that were collectively associated with higher HDL-cholesterol, higher SHBG, and lower triglycerides and liver enzymes – resembling a phenotype opposite to lipodystrophy.

UFA variants

There are 38 UFA variants (Martin et al., 2021). These variants were identified in two steps. First, they were associated (at p<5 × 10–8) with body fat percentage and a composite metabolic phenotype as detailed above. Second, in a k-means clustering approach (Martin et al., 2021), they formed a cluster of variants that were collectively associated with lower HDL-cholesterol, lower SHBG, and higher triglycerides and liver enzymes - resembling monogenic obesity.

Mendelian randomisation

We investigated the causal associations between the four exposures (BMI, body fat percentage, FA, and UFA) and 37 disease outcomes by performing two-sample MR analysis (Pierce and Burgess, 2013). We used the inverse-variance weighted (IVW) approach as our main analysis, and MR-Egger and weighted median as sensitivity analyses in order to detect and partially account for unidentified pleiotropy of our genetic instruments. For BMI, we used effect size estimates from the GWAS of BMI (Locke et al., 2015), and for body fat percentage, FA, and UFA, we used effect size estimates from the GWAS of body fat percentage (442,278 European ancestry individuals from the UK Biobank study) (Ji et al., 2019).

To estimate the effects of variants on our disease outcomes, we used two main sources of data: FinnGen GWAS summary results and published GWAS of the same diseases (Supplementary file 1ci–ii). We performed MR within each data source and then meta-analysed the results across the two datasets using a random-effects model with the R package metafor (RRID:SCR_003450; Viechtbauer, 2010), where the data was available in both. For one published GWAS (the GECCO consortium), we only had information for FA and UFA variants. To provide further MR evidence, we used a third source of disease data – disease status in the UK Biobank (Supplementary file 1ciii). We ran the same models but did not meta-analyse with published GWAS and FinnGen because most of the body fat percentage, FA, and UFA variants were identified in the UK Biobank.

We obtained heterogeneity Q statistics for each IVW MR and MR-Egger, and I2 statistics for each MR-Egger analysis using the MendelianRandomization R package (Yavorska and Burgess, 2017). All statistical analyses were conducted using R software (R Development Core Team, 2020). Given the number of tests performed, we used a Benjamini–Hochberg false discovery rate (FDR) procedure and an FDR of 0.1 to define meaningful results for each of the four exposures (Benjamini and Hochberg, 1995).

Results

We identified 37 diseases as associated with obesity and for which MR studies had previously been performed. Of these 37, 5 metabolic conditions were part of our previous study that validated the use of FA and UFA genetic variants as a way of partially separating the metabolic from non-metabolic components of higher adiposity (Martin et al., 2021). Once we had tested BMI and body fat percentage, we further characterised the likely causal component of higher adiposity using FA and UFA variants as follows (Figure 1, step 5): (i) diseases with evidence that the metabolic effect of higher adiposity is causal. Here, MR using the UFA genetic variants indicated that higher adiposity with its adverse metabolic consequences was causal to disease, whilst MR using the FA genetic variants indicated that higher adiposity with favourable metabolic effects was protective (at FDR 0.1). (ii) Diseases with evidence that there is a non-metabolic causal effect (e.g. mechanical effect, psychological/adverse social effect). Here, MR using the FA genetic variants indicated that higher adiposity without its adverse metabolic consequences was likely contributing to the disease, as well as the MR using the UFA genetic variants. (iii) Diseases with evidence that there is a combination of causal effects but with a predominantly metabolic component. Here, MR using the UFA genetic variants indicated that higher adiposity with its adverse metabolic consequences was causal to disease, and MR using the FA genetic variants was directionally consistent with higher adiposity with favourable metabolic effects being protective but FDR > 0.1. (iv) Diseases with evidence that there is a combination of causal effects but with a predominantly non-metabolic component. Here, MR using the UFA genetic variants indicated that higher adiposity without its adverse metabolic consequences was likely contributing to the disease, and MR of the FA genetic variants was directionally consistent with this but FDR > 0.1.

We grouped these disease outcomes into seven major categories – cardiovascular and metabolic conditions, musculoskeletal, gastrointestinal, nervous, integumentary and respiratory systems, and cancer. MR analysis of five conditions (coronary artery disease, hypertension, stroke, type 2 diabetes, and polycystic ovary syndrome) was part of our previous study (Martin et al., 2021). We focused on the MR of body fat percentage if a causal effect of BMI was indicated, and the MR of FA and UFA if a causal effect of BMI and body fat percentage was indicated, but have presented all results in Supplementary file 1e for completeness. Where random-effects meta-analyses were performed, the heterogeneity statistics are given in Supplementary file 1f.

(i) Diseases with evidence that the metabolic effect of higher adiposity is causal

When comparing the MR analyses for FA and UFA, our results provided evidence that the metabolic effect of higher adiposity is contributing causally to coronary artery disease, peripheral artery disease, hypertension, stroke, type 2 diabetes, and gout (Figures 212, Supplementary file 1e). For stroke, our results were consistent when using sub-types of the condition (Figure 3—figure supplement 1, Supplementary file 1g). Our results also indicated that the metabolic effect of higher adiposity is causal to chronic kidney disease, although the results from BMI and body fat percentage were less conclusive (Figure 3).

The inverse-variance weighted (IVW) two-sample MR analysis/meta-analysis of the effects of body mass index (BMI), body fat percentage (BFP), “favourable adiposity” (FA) and “unfavourable adiposity” (UFA) on type 2 diabetes, hypertension, polycystic ovary syndrome and coronary artery disease.

The error bars represent the 95% confidence intervals of the IVW estimates in odds ratio per standard deviation change in genetically determined BMI, body fat percentage, FA and UFA. Italics give our best interpretation of the data using the FDR 0.1 results.

Figure 3 with 1 supplement see all
The inverse-variance weighted (IVW) two-sample MR analysis/meta-analysis of the effects of body mass index (BMI), body fat percentage (BFP), “favourable adiposity” (FA) and “unfavourable adiposity” (UFA) on stroke, peripheral artery disease, heart failure, atrial fibrillation and chronic kidney disease.

The error bars represent the 95% confidence intervals of the IVW estimates in odds ratio per standard deviation change in genetically determined BMI, body fat percentage, FA and UFA. Italics give our best interpretation of the data using the FDR 0.1 results.

The inverse-variance weighted (IVW) two-sample MR analysis/meta-analysis of the effects of body mass index (BMI), body fat percentage (BFP), “favourable adiposity” (FA) and “unfavourable adiposity” (UFA) on venous thromboembolism, deep vein thrombosis, pulmonary embolism and abdominal aneurysm.

The error bars represent the 95% confidence intervals of the IVW estimates in odds ratio per standard deviation change in genetically determined BMI, body fat percentage, FA and UFA. Italics give our best interpretation of the data using the FDR 0.1 results.

Figure 5 with 1 supplement see all
The inverse-variance weighted (IVW) two-sample MR analysis/meta-analysis of the effects of body mass index (BMI), body fat percentage (BFP), “favourable adiposity” (FA) and “unfavourable adiposity” (UFA) on gout, osteoarthritis, osteoporosis and rheumatoid arthritis.

The error bars represent the 95% confidence intervals of the IVW estimates in odds ratio per standard deviation change in genetically determined BMI, body fat percentage, FA and UFA. Italics give our best interpretation of the data using the FDR 0.1 results.

The inverse-variance weighted (IVW) two-sample MR analysis/meta-analysis of the effects of body mass index (BMI), body fat percentage (BFP), “favourable adiposity” (FA) and “unfavourable adiposity” (UFA) on gallstones and gastro-oesophageal reflux disease.

The error bars represent the 95% confidence intervals of the IVW estimates in odds ratio per standard deviation change in genetically determined BMI, body fat percentage, FA and UFA. Italics give our best interpretation of the data using the FDR 0.1 results.

The inverse-variance weighted (IVW) two-sample MR analysis/meta-analysis of the effects of body mass index (BMI), body fat percentage (BFP), “favourable adiposity” (FA) and “unfavourable adiposity” (UFA) on Alzheimer’s disease, depression, multiple sclerosis and Parkinson’s disease.

The error bars represent the 95% confidence intervals of the IVW estimates in odds ratio per standard deviation change in genetically determined BMI, body fat percentage, FA and UFA. Italics give our best interpretation of the data using the FDR 0.1 results.

The inverse-variance weighted (IVW) two-sample MR analysis/meta-analysis of the effects of body mass index (BMI), body fat percentage (BFP), “favourable adiposity” (FA) and “unfavourable adiposity” (UFA) on psoriasis.

The error bars represent the 95% confidence intervals of the IVW estimates in odds ratio per standard deviation change in genetically determined BMI, body fat percentage, FA and UFA. Italics give our best interpretation of the data using the FDR 0.1 results.

Figure 9 with 1 supplement see all
The inverse-variance weighted (IVW) two-sample MR analysis/meta-analysis of the effects of body mass index (BMI), body fat percentage (BFP), “favourable adiposity” (FA) and “unfavourable adiposity” (UFA) on adult-onset asthma.

The error bars represent the 95% confidence intervals of the IVW estimates in odds ratio per standard deviation change in genetically determined BMI, body fat percentage, FA and UFA. Italics give our best interpretation of the data using the FDR 0.1 results.

Figure 10 with 1 supplement see all
The inverse-variance weighted (IVW) two-sample MR analysis/meta-analysis of the effects of body mass index (BMI), body fat percentage (BFP), “favourable adiposity” (FA) and “unfavourable adiposity” (UFA) on Barrett’s oesophagus, breast cancer, cancer myeloma and colorectal cancer.

The error bars represent the 95% confidence intervals of the IVW estimates in odds ratio per standard deviation change in genetically determined BMI, body fat percentage, FA and UFA. Italics give our best interpretation of the data using the FDR 0.1 results.

Figure 11 with 2 supplements see all
The inverse-variance weighted (IVW) two-sample MR analysis/meta-analysis of the effects of body mass index (BMI), body fat percentage (BFP), “favourable adiposity” (FA) and “unfavourable adiposity” (UFA) on endometrial and lung cancer, meningioma and ovarian cancer.

The error bars represent the 95% confidence intervals of the IVW estimates in odds ratio per standard deviation change in genetically determined BMI, body fat percentage, FA and UFA. Italics give our best interpretation of the data using the FDR 0.1 results.

The inverse-variance weighted (IVW) two-sample MR analysis/meta-analysis of the effects of body mass index (BMI), body fat percentage (BFP), “favourable adiposity” (FA) and “unfavourable adiposity” (UFA) on pancreatic, prostate, renal and thyroid cancer.

The error bars represent the 95% confidence intervals of the IVW estimates in odds ratio per standard deviation change in genetically determined BMI, body fat percentage, FA and UFA. Italics give our best interpretation of the data using the FDR 0.1 results.

(ii) Diseases with evidence that there is a non-metabolic causal effect

When comparing the MR analyses for FA and UFA, our results provided evidence that some non-metabolic effect of higher adiposity is contributing causally to venous thromboembolism, deep vein thrombosis, osteoarthritis, and rheumatoid arthritis (Figures 212, Supplementary file 1e). For osteoarthritis, our results were consistent when using sub-types of the condition (Figure 5—figure supplement 1, Supplementary file 1g).

(iii) Diseases with evidence that there is a combination of causal effects but with a predominantly metabolic component

When comparing the MR analyses for FA and UFA, our results provided evidence that the metabolic effect of higher adiposity is the predominate cause of the link between higher BMI and polycystic ovary syndrome, heart failure, and atrial fibrillation. Our results also provided evidence that the metabolic effect of higher adiposity is the predominate cause of the link between higher BMI and a reduced risk of breast cancer and higher risk of renal cancer, although the results from body fat percentage were less conclusive (Figures 212, Supplementary file 1e).

(iv) Diseases with evidence that there is a combination of causal effects but with a predominantly non-metabolic component

When comparing the MR analyses for FA and UFA, our results suggested that some non-metabolic effect of higher adiposity is the predominant cause of the link between higher BMI and gallstones, gastro-oesophageal reflux disease, adult-onset asthma, and psoriasis (Figures 212, Supplementary file 1e). Our results also indicated that some non-metabolic effect of higher adiposity is causal to osteoporosis, although the results from BMI were less conclusive (Figure 5). Our results found no evidence (at p<0.05) of an effect of BMI or adiposity on child-onset asthma (Figure 9—figure supplement 1, Supplementary file 1g).

All other disease outcomes

Fifteen disease outcomes did not fit the criteria for definitions i–iv. For five of these conditions, our MR results indicated a causal effect of higher BMI or adiposity, but results from FA and UFA were inconclusive: pulmonary embolism, depression, endometrial cancer, lung cancer, and prostate cancer (Figures 212, Supplementary file 1e). Additionally, we identified some evidence of a metabolic effect of higher adiposity with colorectal and ovarian cancer, with the MR of FA indicating lower odds of colorectal (0.67 [0.52, 0.85]) and ovarian (0.35 [0.18, 0.70]) cancers, but MR of UFA was consistent with the null (p>0.05). For colorectal and ovarian cancer, our results were consistent when using sub-types of the conditions (Figure 10—figure supplement 1, Figure 11—figure supplements 1 and 2, Supplementary file 1g).

Sensitivity analyses

Out of 82 disease outcomes (including subtypes), weighted median MR results were directionally consistent with IVW analysis for 75 diseases for BMI and 73 for body fat percentage, with 33 and 47 of these having p<0.05, respectively. For FA and UFA, where sub-type colorectal cancer data was available, the total number of diseases was 87, and 76 were directionally consistent for both exposures, with 22 and 39 having p<0.05, respectively.

MR-Egger results were broadly consistent with the primary IVW MR results, indicating that pleiotropy (variants acting on the outcomes through more than one mechanism) appears to have had limited effect on our results. MR-Egger results were directionally consistent with IVW for 71 diseases for BMI and 70 for body fat percentage, with 25 and 38 of these having p<0.05, respectively. For FA and UFA, MR-Egger was directionally consistent for 60 and 67 diseases, with 6 and 15 having p<0.05, respectively (Supplementary file 1g). Of the 31 diseases available in the UK Biobank, the IVW analysis of these was directionally consistent with the FinnGen and/or published GWAS analysis for 28, 27, 24, and 27 traits for BMI, body fat percentage, FA, and UFA, respectively (Supplementary file 1h). Of these, 18, 21, 9, and 16 had p<0.05, respectively.

Discussion

We used a genetic approach to understand the role of higher adiposity uncoupled from its adverse metabolic effects in mechanisms linking obesity to higher risk of disease. We first used MR to provide evidence that higher BMI was causally associated with 21 diseases, broadly consistent with those from previous studies. For the majority (17) of these diseases, our results indicated that the BMI effect was predominantly due to excess adiposity rather than a non-fat mass component to BMI. We then used a more specific approach to test the separate roles of higher adiposity with and without its adverse metabolic effects. We provided genetic evidence that the adverse metabolic consequences of higher BMI lead to coronary artery disease, peripheral artery disease, hypertension, stroke, type 2 diabetes, polycystic ovary syndrome, heart failure, atrial fibrillation, chronic kidney disease, renal cancer, and gout, and the adverse non-metabolic consequences of higher BMI likely contribute to osteoarthritis, rheumatoid arthritis, osteoporosis, gastro-oesophageal reflux disease, gallstones, adult-onset asthma, psoriasis, deep vein thrombosis, and venous thromboembolism.

Understanding the reasons why obesity leads to disease is important in order to better advise health professionals and patients of health risks linked to obesity, whether or not they show metabolic derangements. Many previous studies have used an MR approach to support a causal role of higher BMI in disease, but here we attempted to systematically test many conditions and the role of separate components of higher BMI. We discuss some of the more notable, and potentially clinically important, results below.

Cardiometabolic diseases

Previous studies, including those using MR, have shown that higher BMI leads to many cardiometabolic diseases (Larsson et al., 2020; Riaz et al., 2018; Xu et al., 2020), but our results provide additional insight into the likely mechanisms. In addition to the previously established opposing effects of metabolically FA and UFA for coronary artery disease, stroke, hypertension, and type 2 diabetes (Martin et al., 2021), our results confirmed similarly strong metabolic components to peripheral artery disease and chronic kidney disease. These results are consistent with the well-established adverse metabolic effects of higher BMI on these diseases (contributing to atherosclerotic effects or linked to specific haemodynamic impacts) (Sattar and McGuire, 2018). For two further cardiovascular conditions, heart failure and atrial fibrillation, the results were less certain. For these two conditions, the evidence of a predominantly metabolic effect of higher BMI was very clear – with the MR of UFA consistent with effects at least as strong as those for coronary artery disease. However, in contrast to the results for coronary artery disease, the MR of FA was consistent with no effect. This comparison between the effects of FA and UFA may indicate that there is a partial mechanical, or other non-metabolic component, as well as metabolic effect, perhaps mediated by excess weight of any type placing extra strain on the heart.

In contrast to the results for most of the cardiometabolic diseases, our MR analyses provided evidence for a likely non-metabolic component mediating the effect of higher BMI on venous thromboembolism and deep vein thrombosis (two closely related conditions). This finding is clinically important as it suggests that treating metabolic risk factors associated with obesity without changing weight may not reduce the risk of deep vein thrombosis in individuals with obesity. Possible mechanisms could include higher intra‐abdominal pressure (due to excess fat) and slower blood circulation in the lower limbs (due to a more sedentary lifestyle secondary to obesity, or mechanical occlusion of veins) promoting clot initiation and formation (Lorenzet et al., 2012).

Musculoskeletal diseases

We observed clear differences for the role of higher BMI in different musculoskeletal diseases. For gout, opposing effects of FA and UFA clearly indicated a metabolic effect. Gout is a form of inflammatory arthritis caused by the deposition of urate crystals within the joints (Dalbeth et al., 2016). Weight loss from bariatric surgery is associated with lower serum uric acid and lower risk of gout (Maglio et al., 2017). A previous MR study showed that overall obesity, but not the central location of fat, increased the risk of gout (Larsson et al., 2018). The protective effect of FA could be due to improved insulin sensitivity leading to less insulin-enhanced reabsorption of organic anions such as urate (Choi et al., 2005). In contrast to gout, our MR analysis provided evidence that a non-metabolic effect of higher adiposity is a likely cause of osteoarthritis and rheumatoid arthritis – with both FA and UFA leading to disease. For osteoarthritis, the effect of UFA was stronger than that of FA, indicating both a metabolic and non-metabolic component. This is consistent with a causal association between higher adiposity and higher risk of osteoarthritis in non-weight-bearing joints including hands (Reyes et al., 2016). For rheumatoid arthritis, the effects of FA and UFA were similar, suggesting the non-metabolic effect accentuating, or more readily unmasking, the autoimmune background risk, as the key BMI-related factor, although the confidence intervals were wider than those for osteoarthritis. The UFA variants may potentially influence these conditions by load-bearing mechanisms, and tissue enrichment analysis for the FA and UFA variants previously found that FA and UFA loci are enriched for genes expressed in adipocytes and adipose tissue, and mesenchymal stem cells, respectively (Martin et al., 2021). For osteoporosis, we did not replicate the previous finding of a causal association between higher BMI and risk of osteoporosis (estimated by bone mineral density; Song et al., 2020); however, we observed a causal association between higher body fat percentage and a higher risk of osteoporosis with consistent risk increasing effects of both FA and UFA. This finding adds to the complex relationship between higher BMI and osteoporosis, where higher BMI at earlier ages may increase bone accrual, but in later years results in adverse effects.

Gastrointestinal diseases

We observed differences in the effects of BMI when comparing the two gastrointestinal diseases, although the results are less conclusive than those for the musculoskeletal conditions. Here, our results were consistent with a predominantly non-metabolic effect contributing to the association between higher BMI and higher risk of gallstones. Higher BMI has been shown to be causally associated with higher risk of gallstones (Yuan et al., 2021). There are several possible mechanisms that could explain how higher BMI without its adverse metabolic effects could increase the risk of gallstones. These could include a sedentary lifestyle and gallbladder hypomotility secondary to increased abdominal fat mass (Mathus-Vliegen et al., 2004). Metabolic mechanisms could include hepatic de novo cholesterol synthesis (Ståhlberg et al., 1997; Cruz-Monserrate et al., 2016). For gastro-oesophageal reflux, the consistent direction and effect sizes of higher FA and UFA indicate a non-metabolic component, an effect that may be mechanical and better explained by higher central adiposity rather than overall BMI (Green et al., 2020).

Other diseases

For most of the other diseases tested, it was difficult to draw firm conclusions about the role of metabolically FA and UFA. For some diseases, this was in part due to the lack of MR evidence for a role of any form of higher BMI. For example, our MR analyses provided no evidence for the role of higher BMI in the neurodegenerative diseases Alzheimer’s disease, multiple sclerosis, and Parkinson’s. These results are consistent with some but not all previous studies. For example, higher BMI is listed as a key risk factor for Alzheimer’s disease (Livingston et al., 2020), although with little evidence of causality, including MR studies that failed to show an effect (Larsson et al., 2017; Nordestgaard et al., 2017). In contrast to our results, recent MR studies have indicated that higher BMI is protective of Parkinson’s disease (Noyce et al., 2017) and causally associated with higher risk of multiple sclerosis (Mokry et al., 2016). For the inflammatory skin disorder psoriasis, our results indicated that both higher BMI and higher body fat percentage are causally associated with higher risk, but determining the underlying mechanism from the MR of FA and UFA was difficult. Higher BMI is a known cause of psoriasis (Budu-Aggrey et al., 2019; Iskandar et al., 2015) and weight loss is a recommended treatment (Iskandar et al., 2015). It is possible that both metabolic and non-metabolic pathways are driving the risk. The non-metabolic pathways could include inflammation which is one of the possible causal mechanisms (Sbidian et al., 2017; Dowlatshahi et al., 2013). Further work is required to understand if psoriasis could be effectively treated by targeting the metabolic factors alone, or whether only weight loss will benefit such patients. For cancers, our results do not provide any clear additional insight into the likely mechanisms, with potentially stronger effects for BMI and UFA compared to body fat percentage in some analyses hard to explain biologically. The reasons why higher BMI is associated with cancers is uncertain, although several MR studies indicate that the association with many is causal (Mariosa et al., 2019; Vincent and Yaghootkar, 2020), and that central adiposity may play a role (Jarvis et al., 2016). Exposure to higher insulin levels is a plausible mechanism, and some studies have used MR to test insulin directly (Nead et al., 2015; Shu et al., 2019; Carreras-Torres et al., 2017b; Carreras-Torres et al., 2017a; Johansson et al., 2019). Our MR analysis reproduced the previous finding between higher adiposity and higher risk of endometrial cancer (Painter et al., 2016) and renal cell carcinoma (Johansson et al., 2019), and lower risk of breast cancer (Guo et al., 2016; Shu et al., 2019). In contrast to previous MR studies showing a causal link between higher BMI and higher risk of prostate cancer (Kazmi et al., 2020; Davies et al., 2015), we identified a causal association between higher body fat percentage but lower risk of prostate cancer. The relationship between higher BMI and risk of breast cancer is complicated, with MR studies indicating that higher BMI is protective of postmenopausal breast cancer (Gao et al., 2016). This contrasts with the epidemiological associations but could be explained by effects of childhood BMI (Richardson et al., 2020).

Strengths and limitations

Our study had a number of limitations. First, we do not know all of the potential effects of the FA and UFA genetic variants on intermediary mechanisms. For example, the inflammatory profile of the FA variants needs further characterisation. However, the consistent association of the FA genetic variants with lower risk of a wide range of metabolic conditions – from type 2 diabetes where insulin resistance predominates, to stroke where atherosclerotic and blood pressure mechanisms predominate – indicates that these variants collectively represent a profile of higher adiposity and favourable metabolic factors. Second, for some diseases, we may have not had sufficient power to detect an effect of BMI or to separate the effects, and this could explain some of the null findings, especially for conditions where we might have expected an effect, such as pulmonary embolism and aortic aneurysm, but there were smaller numbers of cases available. Third, in some situations it was harder to interpret the results from the MR FA and UFA analyses, especially when one appeared to show an effect and the other did not. One possibility is that some diseases are a combination of both non-metabolic and metabolic effects. Osteoarthritis was the best example of this potential scenario because both FA and UFA increased the risk of disease, but UFA to a greater extent. However, for other diseases, it could be hard to detect a combined effect because the MR with FA could be protective (if metabolic effects predominate), increase risk (if non-metabolic effects predominate), or null (if the two have similar effects). Finally, we used an FDR of 0.1 as a guide to discussing meaningful results. We observed 21 out of the 37 outcome diseases reaching an FDR of 0.1 (based on the Benjamini–Hochberg procedure) for BMI, and 19, 11, and 20 out of the 21 diseases causally associated with BMI reaching this FDR for body fat percentage, FA, and UFA, respectively. Equivalent numbers for an FDR of 0.05 were 21, 17, 11, and 17. Excluding the five metabolic conditions used in our previous study (which were all causally associated with BMI), these results are 16, 14, 7, and 15 for an FDR of 0.1, and 16, 12, 7, and 12 for an FDR of 0.05. In addition to correcting for multiple tests, we noted that 74 of the 37 × 4 MR tests reached a p-value of <0.05 when we would only expect 7 by chance, suggesting many of the tests that did not reach a strict Bonferroni p<0.05 were meaningful.

In summary, we have used a genetic approach to test the separate roles of higher adiposity with and without its adverse metabolic effects. These results emphasize that many people in the community who are of higher BMI are at risk of multiple chronic conditions that can severely impair their quality of life or cause morbidity or mortality, even if their metabolic parameters appear relatively normal.

Data availability

GWAS data from the outcome diseases studied is available from links published in the original studies (Supplementary File 1ci). FinnGen data is available at: https://finngen.gitbook.io/documentation/, and the list of disease outcomes used is in Supplementary File 1cii. Individual-level UK Biobank data cannot be provided, but it is available by application to the UK Biobank: https://www.ukbiobank.ac.uk, and a list of the traits used is in Supplementary File 1ciii. Code used to conduct this analysis will be made available on GitHub after removing any sensitive information (https://github.com/susiemartin/uncoupling-bmi, copy archived at swh:1:rev:f3472762ad6cb7f313656f684e07c14b8735efe5).

References

    1. Fall T
    2. Hägg S
    3. Mägi R
    4. Ploner A
    5. Fischer K
    6. Horikoshi M
    7. Sarin A-P
    8. Thorleifsson G
    9. Ladenvall C
    10. Kals M
    11. Kuningas M
    12. Draisma HHM
    13. Ried JS
    14. van Zuydam NR
    15. Huikari V
    16. Mangino M
    17. Sonestedt E
    18. Benyamin B
    19. Nelson CP
    20. Rivera NV
    21. Kristiansson K
    22. Shen H-Y
    23. Havulinna AS
    24. Dehghan A
    25. Donnelly LA
    26. Kaakinen M
    27. Nuotio M-L
    28. Robertson N
    29. de Bruijn RFAG
    30. Ikram MA
    31. Amin N
    32. Balmforth AJ
    33. Braund PS
    34. Doney ASF
    35. Döring A
    36. Elliott P
    37. Esko T
    38. Franco OH
    39. Gretarsdottir S
    40. Hartikainen A-L
    41. Heikkilä K
    42. Herzig K-H
    43. Holm H
    44. Hottenga JJ
    45. Hyppönen E
    46. Illig T
    47. Isaacs A
    48. Isomaa B
    49. Karssen LC
    50. Kettunen J
    51. Koenig W
    52. Kuulasmaa K
    53. Laatikainen T
    54. Laitinen J
    55. Lindgren C
    56. Lyssenko V
    57. Läärä E
    58. Rayner NW
    59. Männistö S
    60. Pouta A
    61. Rathmann W
    62. Rivadeneira F
    63. Ruokonen A
    64. Savolainen MJ
    65. Sijbrands EJG
    66. Small KS
    67. Smit JH
    68. Steinthorsdottir V
    69. Syvänen A-C
    70. Taanila A
    71. Tobin MD
    72. Uitterlinden AG
    73. Willems SM
    74. Willemsen G
    75. Witteman J
    76. Perola M
    77. Evans A
    78. Ferrières J
    79. Virtamo J
    80. Kee F
    81. Tregouet D-A
    82. Arveiler D
    83. Amouyel P
    84. Ferrario MM
    85. Brambilla P
    86. Hall AS
    87. Heath AC
    88. Madden PAF
    89. Martin NG
    90. Montgomery GW
    91. Whitfield JB
    92. Jula A
    93. Knekt P
    94. Oostra B
    95. van Duijn CM
    96. Penninx BWJH
    97. Smith GD
    98. Kaprio J
    99. Samani NJ
    100. Gieger C
    101. Peters A
    102. Wichmann HE
    103. Boomsma DI
    104. de Geus EJC
    105. Tuomi T
    106. Power C
    107. Hammond CJ
    108. Spector TD
    109. Lind L
    110. Orho-Melander M
    111. Palmer CNA
    112. Morris AD
    113. Groop L
    114. Järvelin M-R
    115. Salomaa V
    116. Vartiainen E
    117. Hofman A
    118. Ripatti S
    119. Metspalu A
    120. Thorsteinsdottir U
    121. Stefansson K
    122. Pedersen NL
    123. McCarthy MI
    124. Ingelsson E
    125. Prokopenko I
    126. European Network for Genetic and Genomic Epidemiology (ENGAGE) consortium
    (2013) The role of adiposity in cardiometabolic traits: a Mendelian randomization analysis
    PLOS Medicine 10:e1001474.
    https://doi.org/10.1371/journal.pmed.1001474
    1. Huyghe JR
    2. Bien SA
    3. Harrison TA
    4. Kang HM
    5. Chen S
    6. Schmit SL
    7. Conti DV
    8. Qu C
    9. Jeon J
    10. Edlund CK
    11. Greenside P
    12. Wainberg M
    13. Schumacher FR
    14. Smith JD
    15. Levine DM
    16. Nelson SC
    17. Sinnott-Armstrong NA
    18. Albanes D
    19. Alonso MH
    20. Anderson K
    21. Arnau-Collell C
    22. Arndt V
    23. Bamia C
    24. Banbury BL
    25. Baron JA
    26. Berndt SI
    27. Bézieau S
    28. Bishop DT
    29. Boehm J
    30. Boeing H
    31. Brenner H
    32. Brezina S
    33. Buch S
    34. Buchanan DD
    35. Burnett-Hartman A
    36. Butterbach K
    37. Caan BJ
    38. Campbell PT
    39. Carlson CS
    40. Castellví-Bel S
    41. Chan AT
    42. Chang-Claude J
    43. Chanock SJ
    44. Chirlaque M-D
    45. Cho SH
    46. Connolly CM
    47. Cross AJ
    48. Cuk K
    49. Curtis KR
    50. de la Chapelle A
    51. Doheny KF
    52. Duggan D
    53. Easton DF
    54. Elias SG
    55. Elliott F
    56. English DR
    57. Feskens EJM
    58. Figueiredo JC
    59. Fischer R
    60. FitzGerald LM
    61. Forman D
    62. Gala M
    63. Gallinger S
    64. Gauderman WJ
    65. Giles GG
    66. Gillanders E
    67. Gong J
    68. Goodman PJ
    69. Grady WM
    70. Grove JS
    71. Gsur A
    72. Gunter MJ
    73. Haile RW
    74. Hampe J
    75. Hampel H
    76. Harlid S
    77. Hayes RB
    78. Hofer P
    79. Hoffmeister M
    80. Hopper JL
    81. Hsu W-L
    82. Huang W-Y
    83. Hudson TJ
    84. Hunter DJ
    85. Ibañez-Sanz G
    86. Idos GE
    87. Ingersoll R
    88. Jackson RD
    89. Jacobs EJ
    90. Jenkins MA
    91. Joshi AD
    92. Joshu CE
    93. Keku TO
    94. Key TJ
    95. Kim HR
    96. Kobayashi E
    97. Kolonel LN
    98. Kooperberg C
    99. Kühn T
    100. Küry S
    101. Kweon S-S
    102. Larsson SC
    103. Laurie CA
    104. Le Marchand L
    105. Leal SM
    106. Lee SC
    107. Lejbkowicz F
    108. Lemire M
    109. Li CI
    110. Li L
    111. Lieb W
    112. Lin Y
    113. Lindblom A
    114. Lindor NM
    115. Ling H
    116. Louie TL
    117. Männistö S
    118. Markowitz SD
    119. Martín V
    120. Masala G
    121. McNeil CE
    122. Melas M
    123. Milne RL
    124. Moreno L
    125. Murphy N
    126. Myte R
    127. Naccarati A
    128. Newcomb PA
    129. Offit K
    130. Ogino S
    131. Onland-Moret NC
    132. Pardini B
    133. Parfrey PS
    134. Pearlman R
    135. Perduca V
    136. Pharoah PDP
    137. Pinchev M
    138. Platz EA
    139. Prentice RL
    140. Pugh E
    141. Raskin L
    142. Rennert G
    143. Rennert HS
    144. Riboli E
    145. Rodríguez-Barranco M
    146. Romm J
    147. Sakoda LC
    148. Schafmayer C
    149. Schoen RE
    150. Seminara D
    151. Shah M
    152. Shelford T
    153. Shin M-H
    154. Shulman K
    155. Sieri S
    156. Slattery ML
    157. Southey MC
    158. Stadler ZK
    159. Stegmaier C
    160. Su Y-R
    161. Tangen CM
    162. Thibodeau SN
    163. Thomas DC
    164. Thomas SS
    165. Toland AE
    166. Trichopoulou A
    167. Ulrich CM
    168. Van Den Berg DJ
    169. van Duijnhoven FJB
    170. Van Guelpen B
    171. van Kranen H
    172. Vijai J
    173. Visvanathan K
    174. Vodicka P
    175. Vodickova L
    176. Vymetalkova V
    177. Weigl K
    178. Weinstein SJ
    179. White E
    180. Win AK
    181. Wolf CR
    182. Wolk A
    183. Woods MO
    184. Wu AH
    185. Zaidi SH
    186. Zanke BW
    187. Zhang Q
    188. Zheng W
    189. Scacheri PC
    190. Potter JD
    191. Bassik MC
    192. Kundaje A
    193. Casey G
    194. Moreno V
    195. Abecasis GR
    196. Nickerson DA
    197. Gruber SB
    198. Hsu L
    199. Peters U
    (2019) Discovery of common and rare genetic risk variants for colorectal cancer
    Nature Genetics 51:76–87.
    https://doi.org/10.1038/s41588-018-0286-6
    1. Huyghe JR
    2. Harrison TA
    3. Bien SA
    4. Hampel H
    5. Figueiredo JC
    6. Schmit SL
    7. Conti DV
    8. Chen S
    9. Qu C
    10. Lin Y
    11. Barfield R
    12. Baron JA
    13. Cross AJ
    14. Diergaarde B
    15. Duggan D
    16. Harlid S
    17. Imaz L
    18. Kang HM
    19. Levine DM
    20. Perduca V
    21. Perez-Cornago A
    22. Sakoda LC
    23. Schumacher FR
    24. Slattery ML
    25. Toland AE
    26. van Duijnhoven FJB
    27. Van Guelpen B
    28. Agudo A
    29. Albanes D
    30. Alonso MH
    31. Anderson K
    32. Arnau-Collell C
    33. Arndt V
    34. Banbury BL
    35. Bassik MC
    36. Berndt SI
    37. Bézieau S
    38. Bishop DT
    39. Boehm J
    40. Boeing H
    41. Boutron-Ruault M-C
    42. Brenner H
    43. Brezina S
    44. Buch S
    45. Buchanan DD
    46. Burnett-Hartman A
    47. Caan BJ
    48. Campbell PT
    49. Carr PR
    50. Castells A
    51. Castellví-Bel S
    52. Chan AT
    53. Chang-Claude J
    54. Chanock SJ
    55. Curtis KR
    56. de la Chapelle A
    57. Easton DF
    58. English DR
    59. Feskens EJM
    60. Gala M
    61. Gallinger SJ
    62. Gauderman WJ
    63. Giles GG
    64. Goodman PJ
    65. Grady WM
    66. Grove JS
    67. Gsur A
    68. Gunter MJ
    69. Haile RW
    70. Hampe J
    71. Hoffmeister M
    72. Hopper JL
    73. Hsu W-L
    74. Huang W-Y
    75. Hudson TJ
    76. Jenab M
    77. Jenkins MA
    78. Joshi AD
    79. Keku TO
    80. Kooperberg C
    81. Kühn T
    82. Küry S
    83. Le Marchand L
    84. Lejbkowicz F
    85. Li CI
    86. Li L
    87. Lieb W
    88. Lindblom A
    89. Lindor NM
    90. Männistö S
    91. Markowitz SD
    92. Milne RL
    93. Moreno L
    94. Murphy N
    95. Nassir R
    96. Offit K
    97. Ogino S
    98. Panico S
    99. Parfrey PS
    100. Pearlman R
    101. Pharoah PDP
    102. Phipps AI
    103. Platz EA
    104. Potter JD
    105. Prentice RL
    106. Qi L
    107. Raskin L
    108. Rennert G
    109. Rennert HS
    110. Riboli E
    111. Schafmayer C
    112. Schoen RE
    113. Seminara D
    114. Song M
    115. Su Y-R
    116. Tangen CM
    117. Thibodeau SN
    118. Thomas DC
    119. Trichopoulou A
    120. Ulrich CM
    121. Visvanathan K
    122. Vodicka P
    123. Vodickova L
    124. Vymetalkova V
    125. Weigl K
    126. Weinstein SJ
    127. White E
    128. Wolk A
    129. Woods MO
    130. Wu AH
    131. Abecasis GR
    132. Nickerson DA
    133. Scacheri PC
    134. Kundaje A
    135. Casey G
    136. Gruber SB
    137. Hsu L
    138. Moreno V
    139. Hayes RB
    140. Newcomb PA
    141. Peters U
    (2021) Genetic architectures of proximal and distal colorectal cancer are partly distinct
    Gut 70:1325–1334.
    https://doi.org/10.1136/gutjnl-2020-321534
    1. Jones GT
    2. Tromp G
    3. Kuivaniemi H
    4. Gretarsdottir S
    5. Baas AF
    6. Giusti B
    7. Strauss E
    8. Van’t Hof FNG
    9. Webb TR
    10. Erdman R
    11. Ritchie MD
    12. Elmore JR
    13. Verma A
    14. Pendergrass S
    15. Kullo IJ
    16. Ye Z
    17. Peissig PL
    18. Gottesman O
    19. Verma SS
    20. Malinowski J
    21. Rasmussen-Torvik LJ
    22. Borthwick KM
    23. Smelser DT
    24. Crosslin DR
    25. de Andrade M
    26. Ryer EJ
    27. McCarty CA
    28. Böttinger EP
    29. Pacheco JA
    30. Crawford DC
    31. Carrell DS
    32. Gerhard GS
    33. Franklin DP
    34. Carey DJ
    35. Phillips VL
    36. Williams MJA
    37. Wei W
    38. Blair R
    39. Hill AA
    40. Vasudevan TM
    41. Lewis DR
    42. Thomson IA
    43. Krysa J
    44. Hill GB
    45. Roake J
    46. Merriman TR
    47. Oszkinis G
    48. Galora S
    49. Saracini C
    50. Abbate R
    51. Pulli R
    52. Pratesi C
    53. Saratzis A
    54. Verissimo AR
    55. Bumpstead S
    56. Badger SA
    57. Clough RE
    58. Cockerill G
    59. Hafez H
    60. Scott DJA
    61. Futers TS
    62. Romaine SPR
    63. Bridge K
    64. Griffin KJ
    65. Bailey MA
    66. Smith A
    67. Thompson MM
    68. van Bockxmeer FM
    69. Matthiasson SE
    70. Thorleifsson G
    71. Thorsteinsdottir U
    72. Blankensteijn JD
    73. Teijink JAW
    74. Wijmenga C
    75. de Graaf J
    76. Kiemeney LA
    77. Lindholt JS
    78. Hughes A
    79. Bradley DT
    80. Stirrups K
    81. Golledge J
    82. Norman PE
    83. Powell JT
    84. Humphries SE
    85. Hamby SE
    86. Goodall AH
    87. Nelson CP
    88. Sakalihasan N
    89. Courtois A
    90. Ferrell RE
    91. Eriksson P
    92. Folkersen L
    93. Franco-Cereceda A
    94. Eicher JD
    95. Johnson AD
    96. Betsholtz C
    97. Ruusalepp A
    98. Franzén O
    99. Schadt EE
    100. Björkegren JLM
    101. Lipovich L
    102. Drolet AM
    103. Verhoeven EL
    104. Zeebregts CJ
    105. Geelkerken RH
    106. van Sambeek MR
    107. van Sterkenburg SM
    108. de Vries J-P
    109. Stefansson K
    110. Thompson JR
    111. de Bakker PIW
    112. Deloukas P
    113. Sayers RD
    114. Harrison SC
    115. van Rij AM
    116. Samani NJ
    117. Bown MJ
    (2017) Meta-Analysis of Genome-Wide Association Studies for Abdominal Aortic Aneurysm Identifies Four New Disease-Specific Risk Loci
    Circulation Research 120:341–353.
    https://doi.org/10.1161/CIRCRESAHA.116.308765
    1. Kunkle BW
    2. Grenier-Boley B
    3. Sims R
    4. Bis JC
    5. Damotte V
    6. Naj AC
    7. Boland A
    8. Vronskaya M
    9. van der Lee SJ
    10. Amlie-Wolf A
    11. Bellenguez C
    12. Frizatti A
    13. Chouraki V
    14. Martin ER
    15. Sleegers K
    16. Badarinarayan N
    17. Jakobsdottir J
    18. Hamilton-Nelson KL
    19. Moreno-Grau S
    20. Olaso R
    21. Raybould R
    22. Chen Y
    23. Kuzma AB
    24. Hiltunen M
    25. Morgan T
    26. Ahmad S
    27. Vardarajan BN
    28. Epelbaum J
    29. Hoffmann P
    30. Boada M
    31. Beecham GW
    32. Garnier J-G
    33. Harold D
    34. Fitzpatrick AL
    35. Valladares O
    36. Moutet M-L
    37. Gerrish A
    38. Smith AV
    39. Qu L
    40. Bacq D
    41. Denning N
    42. Jian X
    43. Zhao Y
    44. Del Zompo M
    45. Fox NC
    46. Choi S-H
    47. Mateo I
    48. Hughes JT
    49. Adams HH
    50. Malamon J
    51. Sanchez-Garcia F
    52. Patel Y
    53. Brody JA
    54. Dombroski BA
    55. Naranjo MCD
    56. Daniilidou M
    57. Eiriksdottir G
    58. Mukherjee S
    59. Wallon D
    60. Uphill J
    61. Aspelund T
    62. Cantwell LB
    63. Garzia F
    64. Galimberti D
    65. Hofer E
    66. Butkiewicz M
    67. Fin B
    68. Scarpini E
    69. Sarnowski C
    70. Bush WS
    71. Meslage S
    72. Kornhuber J
    73. White CC
    74. Song Y
    75. Barber RC
    76. Engelborghs S
    77. Sordon S
    78. Voijnovic D
    79. Adams PM
    80. Vandenberghe R
    81. Mayhaus M
    82. Cupples LA
    83. Albert MS
    84. De Deyn PP
    85. Gu W
    86. Himali JJ
    87. Beekly D
    88. Squassina A
    89. Hartmann AM
    90. Orellana A
    91. Blacker D
    92. Rodriguez-Rodriguez E
    93. Lovestone S
    94. Garcia ME
    95. Doody RS
    96. Munoz-Fernadez C
    97. Sussams R
    98. Lin H
    99. Fairchild TJ
    100. Benito YA
    101. Holmes C
    102. Karamujić-Čomić H
    103. Frosch MP
    104. Thonberg H
    105. Maier W
    106. Roshchupkin G
    107. Ghetti B
    108. Giedraitis V
    109. Kawalia A
    110. Li S
    111. Huebinger RM
    112. Kilander L
    113. Moebus S
    114. Hernández I
    115. Kamboh MI
    116. Brundin R
    117. Turton J
    118. Yang Q
    119. Katz MJ
    120. Concari L
    121. Lord J
    122. Beiser AS
    123. Keene CD
    124. Helisalmi S
    125. Kloszewska I
    126. Kukull WA
    127. Koivisto AM
    128. Lynch A
    129. Tarraga L
    130. Larson EB
    131. Haapasalo A
    132. Lawlor B
    133. Mosley TH
    134. Lipton RB
    135. Solfrizzi V
    136. Gill M
    137. Longstreth WT
    138. Montine TJ
    139. Frisardi V
    140. Diez-Fairen M
    141. Rivadeneira F
    142. Petersen RC
    143. Deramecourt V
    144. Alvarez I
    145. Salani F
    146. Ciaramella A
    147. Boerwinkle E
    148. Reiman EM
    149. Fievet N
    150. Rotter JI
    151. Reisch JS
    152. Hanon O
    153. Cupidi C
    154. Andre Uitterlinden AG
    155. Royall DR
    156. Dufouil C
    157. Maletta RG
    158. de Rojas I
    159. Sano M
    160. Brice A
    161. Cecchetti R
    162. George-Hyslop PS
    163. Ritchie K
    164. Tsolaki M
    165. Tsuang DW
    166. Dubois B
    167. Craig D
    168. Wu C-K
    169. Soininen H
    170. Avramidou D
    171. Albin RL
    172. Fratiglioni L
    173. Germanou A
    174. Apostolova LG
    175. Keller L
    176. Koutroumani M
    177. Arnold SE
    178. Panza F
    179. Gkatzima O
    180. Asthana S
    181. Hannequin D
    182. Whitehead P
    183. Atwood CS
    184. Caffarra P
    185. Hampel H
    186. Quintela I
    187. Carracedo Á
    188. Lannfelt L
    189. Rubinsztein DC
    190. Barnes LL
    191. Pasquier F
    192. Frölich L
    193. Barral S
    194. McGuinness B
    195. Beach TG
    196. Johnston JA
    197. Becker JT
    198. Passmore P
    199. Bigio EH
    200. Schott JM
    201. Bird TD
    202. Warren JD
    203. Boeve BF
    204. Lupton MK
    205. Bowen JD
    206. Proitsi P
    207. Boxer A
    208. Powell JF
    209. Burke JR
    210. Kauwe JSK
    211. Burns JM
    212. Mancuso M
    213. Buxbaum JD
    214. Bonuccelli U
    215. Cairns NJ
    216. McQuillin A
    217. Cao C
    218. Livingston G
    219. Carlson CS
    220. Bass NJ
    221. Carlsson CM
    222. Hardy J
    223. Carney RM
    224. Bras J
    225. Carrasquillo MM
    226. Guerreiro R
    227. Allen M
    228. Chui HC
    229. Fisher E
    230. Masullo C
    231. Crocco EA
    232. DeCarli C
    233. Bisceglio G
    234. Dick M
    235. Ma L
    236. Duara R
    237. Graff-Radford NR
    238. Evans DA
    239. Hodges A
    240. Faber KM
    241. Scherer M
    242. Fallon KB
    243. Riemenschneider M
    244. Fardo DW
    245. Heun R
    246. Farlow MR
    247. Kölsch H
    248. Ferris S
    249. Leber M
    250. Foroud TM
    251. Heuser I
    252. Galasko DR
    253. Giegling I
    254. Gearing M
    255. Hüll M
    256. Geschwind DH
    257. Gilbert JR
    258. Morris J
    259. Green RC
    260. Mayo K
    261. Growdon JH
    262. Feulner T
    263. Hamilton RL
    264. Harrell LE
    265. Drichel D
    266. Honig LS
    267. Cushion TD
    268. Huentelman MJ
    269. Hollingworth P
    270. Hulette CM
    271. Hyman BT
    272. Marshall R
    273. Jarvik GP
    274. Meggy A
    275. Abner E
    276. Menzies GE
    277. Jin L-W
    278. Leonenko G
    279. Real LM
    280. Jun GR
    281. Baldwin CT
    282. Grozeva D
    283. Karydas A
    284. Russo G
    285. Kaye JA
    286. Kim R
    287. Jessen F
    288. Kowall NW
    289. Vellas B
    290. Kramer JH
    291. Vardy E
    292. LaFerla FM
    293. Jöckel K-H
    294. Lah JJ
    295. Dichgans M
    296. Leverenz JB
    297. Mann D
    298. Levey AI
    299. Pickering-Brown S
    300. Lieberman AP
    301. Klopp N
    302. Lunetta KL
    303. Wichmann H-E
    304. Lyketsos CG
    305. Morgan K
    306. Marson DC
    307. Brown K
    308. Martiniuk F
    309. Medway C
    310. Mash DC
    311. Nöthen MM
    312. Masliah E
    313. Hooper NM
    314. McCormick WC
    315. Daniele A
    316. McCurry SM
    317. Bayer A
    318. McDavid AN
    319. Gallacher J
    320. McKee AC
    321. van den Bussche H
    322. Mesulam M
    323. Brayne C
    324. Miller BL
    325. Riedel-Heller S
    326. Miller CA
    327. Miller JW
    328. Al-Chalabi A
    329. Morris JC
    330. Shaw CE
    331. Myers AJ
    332. Wiltfang J
    333. O’Bryant S
    334. Olichney JM
    335. Alvarez V
    336. Parisi JE
    337. Singleton AB
    338. Paulson HL
    339. Collinge J
    340. Perry WR
    341. Mead S
    342. Peskind E
    343. Cribbs DH
    344. Rossor M
    345. Pierce A
    346. Ryan NS
    347. Poon WW
    348. Nacmias B
    349. Potter H
    350. Sorbi S
    351. Quinn JF
    352. Sacchinelli E
    353. Raj A
    354. Spalletta G
    355. Raskind M
    356. Caltagirone C
    357. Bossù P
    358. Orfei MD
    359. Reisberg B
    360. Clarke R
    361. Reitz C
    362. Smith AD
    363. Ringman JM
    364. Warden D
    365. Roberson ED
    366. Wilcock G
    367. Rogaeva E
    368. Bruni AC
    369. Rosen HJ
    370. Gallo M
    371. Rosenberg RN
    372. Ben-Shlomo Y
    373. Sager MA
    374. Mecocci P
    375. Saykin AJ
    376. Pastor P
    377. Cuccaro ML
    378. Vance JM
    379. Schneider JA
    380. Schneider LS
    381. Slifer S
    382. Seeley WW
    383. Smith AG
    384. Sonnen JA
    385. Spina S
    386. Stern RA
    387. Swerdlow RH
    388. Tang M
    389. Tanzi RE
    390. Trojanowski JQ
    391. Troncoso JC
    392. Van Deerlin VM
    393. Van Eldik LJ
    394. Vinters HV
    395. Vonsattel JP
    396. Weintraub S
    397. Welsh-Bohmer KA
    398. Wilhelmsen KC
    399. Williamson J
    400. Wingo TS
    401. Woltjer RL
    402. Wright CB
    403. Yu C-E
    404. Yu L
    405. Saba Y
    406. Pilotto A
    407. Bullido MJ
    408. Peters O
    409. Crane PK
    410. Bennett D
    411. Bosco P
    412. Coto E
    413. Boccardi V
    414. De Jager PL
    415. Lleo A
    416. Warner N
    417. Lopez OL
    418. Ingelsson M
    419. Deloukas P
    420. Cruchaga C
    421. Graff C
    422. Gwilliam R
    423. Fornage M
    424. Goate AM
    425. Sanchez-Juan P
    426. Kehoe PG
    427. Amin N
    428. Ertekin-Taner N
    429. Berr C
    430. Debette S
    431. Love S
    432. Launer LJ
    433. Younkin SG
    434. Dartigues J-F
    435. Corcoran C
    436. Ikram MA
    437. Dickson DW
    438. Nicolas G
    439. Campion D
    440. Tschanz J
    441. Schmidt H
    442. Hakonarson H
    443. Clarimon J
    444. Munger R
    445. Schmidt R
    446. Farrer LA
    447. Van Broeckhoven C
    448. C O’Donovan M
    449. DeStefano AL
    450. Jones L
    451. Haines JL
    452. Deleuze J-F
    453. Owen MJ
    454. Gudnason V
    455. Mayeux R
    456. Escott-Price V
    457. Psaty BM
    458. Ramirez A
    459. Wang L-S
    460. Ruiz A
    461. van Duijn CM
    462. Holmans PA
    463. Seshadri S
    464. Williams J
    465. Amouyel P
    466. Schellenberg GD
    467. Lambert J-C
    468. Pericak-Vance MA
    469. Alzheimer Disease Genetics Consortium (ADGC)
    470. European Alzheimer’s Disease Initiative (EADI)
    471. Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (CHARGE)
    472. Genetic and Environmental Risk in AD/Defining Genetic, Polygenic and Environmental Risk for Alzheimer’s Disease Consortium (GERAD/PERADES)
    (2019) Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing
    Nature Genetics 51:414–430.
    https://doi.org/10.1038/s41588-019-0358-2
    1. Locke AE
    2. Kahali B
    3. Berndt SI
    4. Justice AE
    5. Pers TH
    6. Day FR
    7. Powell C
    8. Vedantam S
    9. Buchkovich ML
    10. Yang J
    11. Croteau-Chonka DC
    12. Esko T
    13. Fall T
    14. Ferreira T
    15. Gustafsson S
    16. Kutalik Z
    17. Luan J
    18. Mägi R
    19. Randall JC
    20. Winkler TW
    21. Wood AR
    22. Workalemahu T
    23. Faul JD
    24. Smith JA
    25. Zhao JH
    26. Zhao W
    27. Chen J
    28. Fehrmann R
    29. Hedman ÅK
    30. Karjalainen J
    31. Schmidt EM
    32. Absher D
    33. Amin N
    34. Anderson D
    35. Beekman M
    36. Bolton JL
    37. Bragg-Gresham JL
    38. Buyske S
    39. Demirkan A
    40. Deng G
    41. Ehret GB
    42. Feenstra B
    43. Feitosa MF
    44. Fischer K
    45. Goel A
    46. Gong J
    47. Jackson AU
    48. Kanoni S
    49. Kleber ME
    50. Kristiansson K
    51. Lim U
    52. Lotay V
    53. Mangino M
    54. Leach IM
    55. Medina-Gomez C
    56. Medland SE
    57. Nalls MA
    58. Palmer CD
    59. Pasko D
    60. Pechlivanis S
    61. Peters MJ
    62. Prokopenko I
    63. Shungin D
    64. Stančáková A
    65. Strawbridge RJ
    66. Sung YJ
    67. Tanaka T
    68. Teumer A
    69. Trompet S
    70. van der Laan SW
    71. van Setten J
    72. Van Vliet-Ostaptchouk JV
    73. Wang Z
    74. Yengo L
    75. Zhang W
    76. Isaacs A
    77. Albrecht E
    78. Ärnlöv J
    79. Arscott GM
    80. Attwood AP
    81. Bandinelli S
    82. Barrett A
    83. Bas IN
    84. Bellis C
    85. Bennett AJ
    86. Berne C
    87. Blagieva R
    88. Blüher M
    89. Böhringer S
    90. Bonnycastle LL
    91. Böttcher Y
    92. Boyd HA
    93. Bruinenberg M
    94. Caspersen IH
    95. Chen Y-DI
    96. Clarke R
    97. Daw EW
    98. de Craen AJM
    99. Delgado G
    100. Dimitriou M
    101. Doney ASF
    102. Eklund N
    103. Estrada K
    104. Eury E
    105. Folkersen L
    106. Fraser RM
    107. Garcia ME
    108. Geller F
    109. Giedraitis V
    110. Gigante B
    111. Go AS
    112. Golay A
    113. Goodall AH
    114. Gordon SD
    115. Gorski M
    116. Grabe H-J
    117. Grallert H
    118. Grammer TB
    119. Gräßler J
    120. Grönberg H
    121. Groves CJ
    122. Gusto G
    123. Haessler J
    124. Hall P
    125. Haller T
    126. Hallmans G
    127. Hartman CA
    128. Hassinen M
    129. Hayward C
    130. Heard-Costa NL
    131. Helmer Q
    132. Hengstenberg C
    133. Holmen O
    134. Hottenga J-J
    135. James AL
    136. Jeff JM
    137. Johansson Å
    138. Jolley J
    139. Juliusdottir T
    140. Kinnunen L
    141. Koenig W
    142. Koskenvuo M
    143. Kratzer W
    144. Laitinen J
    145. Lamina C
    146. Leander K
    147. Lee NR
    148. Lichtner P
    149. Lind L
    150. Lindström J
    151. Lo KS
    152. Lobbens S
    153. Lorbeer R
    154. Lu Y
    155. Mach F
    156. Magnusson PKE
    157. Mahajan A
    158. McArdle WL
    159. McLachlan S
    160. Menni C
    161. Merger S
    162. Mihailov E
    163. Milani L
    164. Moayyeri A
    165. Monda KL
    166. Morken MA
    167. Mulas A
    168. Müller G
    169. Müller-Nurasyid M
    170. Musk AW
    171. Nagaraja R
    172. Nöthen MM
    173. Nolte IM
    174. Pilz S
    175. Rayner NW
    176. Renstrom F
    177. Rettig R
    178. Ried JS
    179. Ripke S
    180. Robertson NR
    181. Rose LM
    182. Sanna S
    183. Scharnagl H
    184. Scholtens S
    185. Schumacher FR
    186. Scott WR
    187. Seufferlein T
    188. Shi J
    189. Smith AV
    190. Smolonska J
    191. Stanton AV
    192. Steinthorsdottir V
    193. Stirrups K
    194. Stringham HM
    195. Sundström J
    196. Swertz MA
    197. Swift AJ
    198. Syvänen A-C
    199. Tan S-T
    200. Tayo BO
    201. Thorand B
    202. Thorleifsson G
    203. Tyrer JP
    204. Uh H-W
    205. Vandenput L
    206. Verhulst FC
    207. Vermeulen SH
    208. Verweij N
    209. Vonk JM
    210. Waite LL
    211. Warren HR
    212. Waterworth D
    213. Weedon MN
    214. Wilkens LR
    215. Willenborg C
    216. Wilsgaard T
    217. Wojczynski MK
    218. Wong A
    219. Wright AF
    220. Zhang Q
    221. LifeLines Cohort Study
    222. Brennan EP
    223. Choi M
    224. Dastani Z
    225. Drong AW
    226. Eriksson P
    227. Franco-Cereceda A
    228. Gådin JR
    229. Gharavi AG
    230. Goddard ME
    231. Handsaker RE
    232. Huang J
    233. Karpe F
    234. Kathiresan S
    235. Keildson S
    236. Kiryluk K
    237. Kubo M
    238. Lee J-Y
    239. Liang L
    240. Lifton RP
    241. Ma B
    242. McCarroll SA
    243. McKnight AJ
    244. Min JL
    245. Moffatt MF
    246. Montgomery GW
    247. Murabito JM
    248. Nicholson G
    249. Nyholt DR
    250. Okada Y
    251. Perry JRB
    252. Dorajoo R
    253. Reinmaa E
    254. Salem RM
    255. Sandholm N
    256. Scott RA
    257. Stolk L
    258. Takahashi A
    259. Tanaka T
    260. van ’t Hooft FM
    261. Vinkhuyzen AAE
    262. Westra H-J
    263. Zheng W
    264. Zondervan KT
    265. ADIPOGen Consortium
    266. AGEN-BMI Working Group
    267. CARDIOGRAMplusC4D Consortium
    268. CKDGen Consortium
    269. GLGC
    270. ICBP
    271. MAGIC Investigators
    272. MuTHER Consortium
    273. MIGen Consortium
    274. PAGE Consortium
    275. ReproGen Consortium
    276. GENIE Consortium
    277. International Endogene Consortium
    278. Heath AC
    279. Arveiler D
    280. Bakker SJL
    281. Beilby J
    282. Bergman RN
    283. Blangero J
    284. Bovet P
    285. Campbell H
    286. Caulfield MJ
    287. Cesana G
    288. Chakravarti A
    289. Chasman DI
    290. Chines PS
    291. Collins FS
    292. Crawford DC
    293. Cupples LA
    294. Cusi D
    295. Danesh J
    296. de Faire U
    297. den Ruijter HM
    298. Dominiczak AF
    299. Erbel R
    300. Erdmann J
    301. Eriksson JG
    302. Farrall M
    303. Felix SB
    304. Ferrannini E
    305. Ferrières J
    306. Ford I
    307. Forouhi NG
    308. Forrester T
    309. Franco OH
    310. Gansevoort RT
    311. Gejman PV
    312. Gieger C
    313. Gottesman O
    314. Gudnason V
    315. Gyllensten U
    316. Hall AS
    317. Harris TB
    318. Hattersley AT
    319. Hicks AA
    320. Hindorff LA
    321. Hingorani AD
    322. Hofman A
    323. Homuth G
    324. Hovingh GK
    325. Humphries SE
    326. Hunt SC
    327. Hyppönen E
    328. Illig T
    329. Jacobs KB
    330. Jarvelin M-R
    331. Jöckel K-H
    332. Johansen B
    333. Jousilahti P
    334. Jukema JW
    335. Jula AM
    336. Kaprio J
    337. Kastelein JJP
    338. Keinanen-Kiukaanniemi SM
    339. Kiemeney LA
    340. Knekt P
    341. Kooner JS
    342. Kooperberg C
    343. Kovacs P
    344. Kraja AT
    345. Kumari M
    346. Kuusisto J
    347. Lakka TA
    348. Langenberg C
    349. Marchand LL
    350. Lehtimäki T
    351. Lyssenko V
    352. Männistö S
    353. Marette A
    354. Matise TC
    355. McKenzie CA
    356. McKnight B
    357. Moll FL
    358. Morris AD
    359. Morris AP
    360. Murray JC
    361. Nelis M
    362. Ohlsson C
    363. Oldehinkel AJ
    364. Ong KK
    365. Madden PAF
    366. Pasterkamp G
    367. Peden JF
    368. Peters A
    369. Postma DS
    370. Pramstaller PP
    371. Price JF
    372. Qi L
    373. Raitakari OT
    374. Rankinen T
    375. Rao DC
    376. Rice TK
    377. Ridker PM
    378. Rioux JD
    379. Ritchie MD
    380. Rudan I
    381. Salomaa V
    382. Samani NJ
    383. Saramies J
    384. Sarzynski MA
    385. Schunkert H
    386. Schwarz PEH
    387. Sever P
    388. Shuldiner AR
    389. Sinisalo J
    390. Stolk RP
    391. Strauch K
    392. Tönjes A
    393. Trégouët D-A
    394. Tremblay A
    395. Tremoli E
    396. Virtamo J
    397. Vohl M-C
    398. Völker U
    399. Waeber G
    400. Willemsen G
    401. Witteman JC
    402. Zillikens MC
    403. Adair LS
    404. Amouyel P
    405. Asselbergs FW
    406. Assimes TL
    407. Bochud M
    408. Boehm BO
    409. Boerwinkle E
    410. Bornstein SR
    411. Bottinger EP
    412. Bouchard C
    413. Cauchi S
    414. Chambers JC
    415. Chanock SJ
    416. Cooper RS
    417. de Bakker PIW
    418. Dedoussis G
    419. Ferrucci L
    420. Franks PW
    421. Froguel P
    422. Groop LC
    423. Haiman CA
    424. Hamsten A
    425. Hui J
    426. Hunter DJ
    427. Hveem K
    428. Kaplan RC
    429. Kivimaki M
    430. Kuh D
    431. Laakso M
    432. Liu Y
    433. Martin NG
    434. März W
    435. Melbye M
    436. Metspalu A
    437. Moebus S
    438. Munroe PB
    439. Njølstad I
    440. Oostra BA
    441. Palmer CNA
    442. Pedersen NL
    443. Perola M
    444. Pérusse L
    445. Peters U
    446. Power C
    447. Quertermous T
    448. Rauramaa R
    449. Rivadeneira F
    450. Saaristo TE
    451. Saleheen D
    452. Sattar N
    453. Schadt EE
    454. Schlessinger D
    455. Slagboom PE
    456. Snieder H
    457. Spector TD
    458. Thorsteinsdottir U
    459. Stumvoll M
    460. Tuomilehto J
    461. Uitterlinden AG
    462. Uusitupa M
    463. van der Harst P
    464. Walker M
    465. Wallaschofski H
    466. Wareham NJ
    467. Watkins H
    468. Weir DR
    469. Wichmann H-E
    470. Wilson JF
    471. Zanen P
    472. Borecki IB
    473. Deloukas P
    474. Fox CS
    475. Heid IM
    476. O’Connell JR
    477. Strachan DP
    478. Stefansson K
    479. van Duijn CM
    480. Abecasis GR
    481. Franke L
    482. Frayling TM
    483. McCarthy MI
    484. Visscher PM
    485. Scherag A
    486. Willer CJ
    487. Boehnke M
    488. Mohlke KL
    489. Lindgren CM
    490. Beckmann JS
    491. Barroso I
    492. North KE
    493. Ingelsson E
    494. Hirschhorn JN
    495. Loos RJF
    496. Speliotes EK
    (2015) Genetic studies of body mass index yield new insights for obesity biology
    Nature 518:197–206.
    https://doi.org/10.1038/nature14177
    1. Malik R
    2. Chauhan G
    3. Traylor M
    4. Sargurupremraj M
    5. Okada Y
    6. Mishra A
    7. Rutten-Jacobs L
    8. Giese A-K
    9. van der Laan SW
    10. Gretarsdottir S
    11. Anderson CD
    12. Chong M
    13. Adams HHH
    14. Ago T
    15. Almgren P
    16. Amouyel P
    17. Ay H
    18. Bartz TM
    19. Benavente OR
    20. Bevan S
    21. Boncoraglio GB
    22. Brown RD
    23. Butterworth AS
    24. Carrera C
    25. Carty CL
    26. Chasman DI
    27. Chen W-M
    28. Cole JW
    29. Correa A
    30. Cotlarciuc I
    31. Cruchaga C
    32. Danesh J
    33. de Bakker PIW
    34. DeStefano AL
    35. den Hoed M
    36. Duan Q
    37. Engelter ST
    38. Falcone GJ
    39. Gottesman RF
    40. Grewal RP
    41. Gudnason V
    42. Gustafsson S
    43. Haessler J
    44. Harris TB
    45. Hassan A
    46. Havulinna AS
    47. Heckbert SR
    48. Holliday EG
    49. Howard G
    50. Hsu F-C
    51. Hyacinth HI
    52. Ikram MA
    53. Ingelsson E
    54. Irvin MR
    55. Jian X
    56. Jiménez-Conde J
    57. Johnson JA
    58. Jukema JW
    59. Kanai M
    60. Keene KL
    61. Kissela BM
    62. Kleindorfer DO
    63. Kooperberg C
    64. Kubo M
    65. Lange LA
    66. Langefeld CD
    67. Langenberg C
    68. Launer LJ
    69. Lee J-M
    70. Lemmens R
    71. Leys D
    72. Lewis CM
    73. Lin W-Y
    74. Lindgren AG
    75. Lorentzen E
    76. Magnusson PK
    77. Maguire J
    78. Manichaikul A
    79. McArdle PF
    80. Meschia JF
    81. Mitchell BD
    82. Mosley TH
    83. Nalls MA
    84. Ninomiya T
    85. O’Donnell MJ
    86. Psaty BM
    87. Pulit SL
    88. Rannikmäe K
    89. Reiner AP
    90. Rexrode KM
    91. Rice K
    92. Rich SS
    93. Ridker PM
    94. Rost NS
    95. Rothwell PM
    96. Rotter JI
    97. Rundek T
    98. Sacco RL
    99. Sakaue S
    100. Sale MM
    101. Salomaa V
    102. Sapkota BR
    103. Schmidt R
    104. Schmidt CO
    105. Schminke U
    106. Sharma P
    107. Slowik A
    108. Sudlow CLM
    109. Tanislav C
    110. Tatlisumak T
    111. Taylor KD
    112. Thijs VNS
    113. Thorleifsson G
    114. Thorsteinsdottir U
    115. Tiedt S
    116. Trompet S
    117. Tzourio C
    118. van Duijn CM
    119. Walters M
    120. Wareham NJ
    121. Wassertheil-Smoller S
    122. Wilson JG
    123. Wiggins KL
    124. Yang Q
    125. Yusuf S
    126. AFGen Consortium
    127. Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium
    128. International Genomics of Blood Pressure (iGEN-BP) Consortium
    129. INVENT Consortium
    130. STARNET
    131. Bis JC
    132. Pastinen T
    133. Ruusalepp A
    134. Schadt EE
    135. Koplev S
    136. Björkegren JLM
    137. Codoni V
    138. Civelek M
    139. Smith NL
    140. Trégouët DA
    141. Christophersen IE
    142. Roselli C
    143. Lubitz SA
    144. Ellinor PT
    145. Tai ES
    146. Kooner JS
    147. Kato N
    148. He J
    149. van der Harst P
    150. Elliott P
    151. Chambers JC
    152. Takeuchi F
    153. Johnson AD
    154. BioBank Japan Cooperative Hospital Group
    155. COMPASS Consortium
    156. EPIC-CVD Consortium
    157. EPIC-InterAct Consortium
    158. International Stroke Genetics Consortium (ISGC)
    159. METASTROKE Consortium
    160. Neurology Working Group of the CHARGE Consortium
    161. NINDS Stroke Genetics Network (SiGN)
    162. UK Young Lacunar DNA Study
    163. MEGASTROKE Consortium
    164. Sanghera DK
    165. Melander O
    166. Jern C
    167. Strbian D
    168. Fernandez-Cadenas I
    169. Longstreth WT
    170. Rolfs A
    171. Hata J
    172. Woo D
    173. Rosand J
    174. Pare G
    175. Hopewell JC
    176. Saleheen D
    177. Stefansson K
    178. Worrall BB
    179. Kittner SJ
    180. Seshadri S
    181. Fornage M
    182. Markus HS
    183. Howson JMM
    184. Kamatani Y
    185. Debette S
    186. Dichgans M
    (2018) Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes
    Nature Genetics 50:524–537.
    https://doi.org/10.1038/s41588-018-0058-3
    1. Michailidou K
    2. Lindström S
    3. Dennis J
    4. Beesley J
    5. Hui S
    6. Kar S
    7. Lemaçon A
    8. Soucy P
    9. Glubb D
    10. Rostamianfar A
    11. Bolla MK
    12. Wang Q
    13. Tyrer J
    14. Dicks E
    15. Lee A
    16. Wang Z
    17. Allen J
    18. Keeman R
    19. Eilber U
    20. French JD
    21. Qing Chen X
    22. Fachal L
    23. McCue K
    24. McCart Reed AE
    25. Ghoussaini M
    26. Carroll JS
    27. Jiang X
    28. Finucane H
    29. Adams M
    30. Adank MA
    31. Ahsan H
    32. Aittomäki K
    33. Anton-Culver H
    34. Antonenkova NN
    35. Arndt V
    36. Aronson KJ
    37. Arun B
    38. Auer PL
    39. Bacot F
    40. Barrdahl M
    41. Baynes C
    42. Beckmann MW
    43. Behrens S
    44. Benitez J
    45. Bermisheva M
    46. Bernstein L
    47. Blomqvist C
    48. Bogdanova NV
    49. Bojesen SE
    50. Bonanni B
    51. Børresen-Dale A-L
    52. Brand JS
    53. Brauch H
    54. Brennan P
    55. Brenner H
    56. Brinton L
    57. Broberg P
    58. Brock IW
    59. Broeks A
    60. Brooks-Wilson A
    61. Brucker SY
    62. Brüning T
    63. Burwinkel B
    64. Butterbach K
    65. Cai Q
    66. Cai H
    67. Caldés T
    68. Canzian F
    69. Carracedo A
    70. Carter BD
    71. Castelao JE
    72. Chan TL
    73. David Cheng T-Y
    74. Seng Chia K
    75. Choi J-Y
    76. Christiansen H
    77. Clarke CL
    78. NBCS Collaborators
    79. Collée M
    80. Conroy DM
    81. Cordina-Duverger E
    82. Cornelissen S
    83. Cox DG
    84. Cox A
    85. Cross SS
    86. Cunningham JM
    87. Czene K
    88. Daly MB
    89. Devilee P
    90. Doheny KF
    91. Dörk T
    92. Dos-Santos-Silva I
    93. Dumont M
    94. Durcan L
    95. Dwek M
    96. Eccles DM
    97. Ekici AB
    98. Eliassen AH
    99. Ellberg C
    100. Elvira M
    101. Engel C
    102. Eriksson M
    103. Fasching PA
    104. Figueroa J
    105. Flesch-Janys D
    106. Fletcher O
    107. Flyger H
    108. Fritschi L
    109. Gaborieau V
    110. Gabrielson M
    111. Gago-Dominguez M
    112. Gao Y-T
    113. Gapstur SM
    114. García-Sáenz JA
    115. Gaudet MM
    116. Georgoulias V
    117. Giles GG
    118. Glendon G
    119. Goldberg MS
    120. Goldgar DE
    121. González-Neira A
    122. Grenaker Alnæs GI
    123. Grip M
    124. Gronwald J
    125. Grundy A
    126. Guénel P
    127. Haeberle L
    128. Hahnen E
    129. Haiman CA
    130. Håkansson N
    131. Hamann U
    132. Hamel N
    133. Hankinson S
    134. Harrington P
    135. Hart SN
    136. Hartikainen JM
    137. Hartman M
    138. Hein A
    139. Heyworth J
    140. Hicks B
    141. Hillemanns P
    142. Ho DN
    143. Hollestelle A
    144. Hooning MJ
    145. Hoover RN
    146. Hopper JL
    147. Hou M-F
    148. Hsiung C-N
    149. Huang G
    150. Humphreys K
    151. Ishiguro J
    152. Ito H
    153. Iwasaki M
    154. Iwata H
    155. Jakubowska A
    156. Janni W
    157. John EM
    158. Johnson N
    159. Jones K
    160. Jones M
    161. Jukkola-Vuorinen A
    162. Kaaks R
    163. Kabisch M
    164. Kaczmarek K
    165. Kang D
    166. Kasuga Y
    167. Kerin MJ
    168. Khan S
    169. Khusnutdinova E
    170. Kiiski JI
    171. Kim S-W
    172. Knight JA
    173. Kosma V-M
    174. Kristensen VN
    175. Krüger U
    176. Kwong A
    177. Lambrechts D
    178. Le Marchand L
    179. Lee E
    180. Lee MH
    181. Lee JW
    182. Neng Lee C
    183. Lejbkowicz F
    184. Li J
    185. Lilyquist J
    186. Lindblom A
    187. Lissowska J
    188. Lo W-Y
    189. Loibl S
    190. Long J
    191. Lophatananon A
    192. Lubinski J
    193. Luccarini C
    194. Lux MP
    195. Ma ESK
    196. MacInnis RJ
    197. Maishman T
    198. Makalic E
    199. Malone KE
    200. Kostovska IM
    201. Mannermaa A
    202. Manoukian S
    203. Manson JE
    204. Margolin S
    205. Mariapun S
    206. Martinez ME
    207. Matsuo K
    208. Mavroudis D
    209. McKay J
    210. McLean C
    211. Meijers-Heijboer H
    212. Meindl A
    213. Menéndez P
    214. Menon U
    215. Meyer J
    216. Miao H
    217. Miller N
    218. Taib NAM
    219. Muir K
    220. Mulligan AM
    221. Mulot C
    222. Neuhausen SL
    223. Nevanlinna H
    224. Neven P
    225. Nielsen SF
    226. Noh D-Y
    227. Nordestgaard BG
    228. Norman A
    229. Olopade OI
    230. Olson JE
    231. Olsson H
    232. Olswold C
    233. Orr N
    234. Pankratz VS
    235. Park SK
    236. Park-Simon T-W
    237. Lloyd R
    238. Perez JIA
    239. Peterlongo P
    240. Peto J
    241. Phillips K-A
    242. Pinchev M
    243. Plaseska-Karanfilska D
    244. Prentice R
    245. Presneau N
    246. Prokofyeva D
    247. Pugh E
    248. Pylkäs K
    249. Rack B
    250. Radice P
    251. Rahman N
    252. Rennert G
    253. Rennert HS
    254. Rhenius V
    255. Romero A
    256. Romm J
    257. Ruddy KJ
    258. Rüdiger T
    259. Rudolph A
    260. Ruebner M
    261. Rutgers EJT
    262. Saloustros E
    263. Sandler DP
    264. Sangrajrang S
    265. Sawyer EJ
    266. Schmidt DF
    267. Schmutzler RK
    268. Schneeweiss A
    269. Schoemaker MJ
    270. Schumacher F
    271. Schürmann P
    272. Scott RJ
    273. Scott C
    274. Seal S
    275. Seynaeve C
    276. Shah M
    277. Sharma P
    278. Shen C-Y
    279. Sheng G
    280. Sherman ME
    281. Shrubsole MJ
    282. Shu X-O
    283. Smeets A
    284. Sohn C
    285. Southey MC
    286. Spinelli JJ
    287. Stegmaier C
    288. Stewart-Brown S
    289. Stone J
    290. Stram DO
    291. Surowy H
    292. Swerdlow A
    293. Tamimi R
    294. Taylor JA
    295. Tengström M
    296. Teo SH
    297. Beth Terry M
    298. Tessier DC
    299. Thanasitthichai S
    300. Thöne K
    301. Tollenaar RAEM
    302. Tomlinson I
    303. Tong L
    304. Torres D
    305. Truong T
    306. Tseng C-C
    307. Tsugane S
    308. Ulmer H-U
    309. Ursin G
    310. Untch M
    311. Vachon C
    312. van Asperen CJ
    313. Van Den Berg D
    314. van den Ouweland AMW
    315. van der Kolk L
    316. van der Luijt RB
    317. Vincent D
    318. Vollenweider J
    319. Waisfisz Q
    320. Wang-Gohrke S
    321. Weinberg CR
    322. Wendt C
    323. Whittemore AS
    324. Wildiers H
    325. Willett W
    326. Winqvist R
    327. Wolk A
    328. Wu AH
    329. Xia L
    330. Yamaji T
    331. Yang XR
    332. Har Yip C
    333. Yoo K-Y
    334. Yu J-C
    335. Zheng W
    336. Zheng Y
    337. Zhu B
    338. Ziogas A
    339. Ziv E
    340. ABCTB Investigators
    341. ConFab/AOCS Investigators
    342. Lakhani SR
    343. Antoniou AC
    344. Droit A
    345. Andrulis IL
    346. Amos CI
    347. Couch FJ
    348. Pharoah PDP
    349. Chang-Claude J
    350. Hall P
    351. Hunter DJ
    352. Milne RL
    353. García-Closas M
    354. Schmidt MK
    355. Chanock SJ
    356. Dunning AM
    357. Edwards SL
    358. Bader GD
    359. Chenevix-Trench G
    360. Simard J
    361. Kraft P
    362. Easton DF
    (2017) Association analysis identifies 65 new breast cancer risk loci
    Nature 551:92–94.
    https://doi.org/10.1038/nature24284