Height is a classical complex trait influenced by genetic and early-life environmental factors. Despite the nonmodifiable nature of adult height, evaluating the effects of height on noncommunicable disease risk can give insights into the etiology of adulthood diseases (Emerging Risk Factors Collaboration, 2012; Davey Smith et al., 2000). Two major groupings of diseases, cardiovascular disease and cancer, have divergent associations with height (Emerging Risk Factors Collaboration, 2012; Davey Smith et al., 2000; Stefan et al., 2016). Taller people are less likely to develop cardiovascular disease, including coronary heart disease (CHD) (Emerging Risk Factors Collaboration, 2012; Nelson et al., 2015; Nüesch et al., 2016; Hebert et al., 1993; Batty et al., 2009; Marouli et al., 2019) and stroke (Njølstad et al., 1996), but more likely to be diagnosed with cancer (Emerging Risk Factors Collaboration, 2012; Green et al., 2011; Zhang et al., 2015; Thrift et al., 2015; Dixon-Suen et al., 2018; Batty et al., 2006; Gunnell et al., 2001; Perkins et al., 2016). The mechanisms via which height influences disease risks are unclear. The association between height and cardiovascular disease may be mediated via favorable lipid profiles (Emerging Risk Factors Collaboration, 2012; Nelson et al., 2015), lower systolic blood pressure (SBP) (Emerging Risk Factors Collaboration, 2012; Langenberg et al., 2003), lung function (Marouli et al., 2019; Gunnell et al., 2003), lower heart rate (Smulyan et al., 1998), and coronary artery vessel dimension (O’Connor et al., 1996). The increased cancer incidence among taller individuals could relate to early-life exposure to hormones such as insulin-like growth factor 1 (IGF-1) (Renehan et al., 2004; Clayton et al., 2011) or the increased number of cells in taller individuals (Stefan et al., 2016; Green et al., 2011; Albanes and Winick, 1988). However, although overall cancer risk is higher among taller individuals (Green et al., 2011; Batty et al., 2006; Gunnell et al., 2001), there is some evidence for heterogeneity across cancer subtypes with null or inverse associations observed between height and risk of stomach, oropharyngeal, and esophageal cancers (Green et al., 2011; Batty et al., 2006; Gunnell et al., 2001; Perkins et al., 2016).

Height is highly heritable, but the average height across the European populations has increased over the last hundred years (Hatton, 2013), illustrating the effects of early-life environmental factors such as nutrition and childhood infections. The associations of height with adulthood diseases and relevant biomarkers could reflect the biomechanical effects relating to increased stature (e.g., number of cells or larger arteries; O’Connor et al., 1996) or could reflect confounding by early-life environmental factors that influence both height and later-life health such as parental socioeconomic position. For example, wealthier parents may provide their offspring with better nutrition, leading to increased adult height, and a better education, potentially leading to improved health in adulthood (Perkins et al., 2016). Thus, it is unclear whether height has a causal effect on the risk of cardiovascular disease and cancer or if a confounding factor influences both height and disease risk.

Mendelian randomization (Smith and Ebrahim, 2003) analyses, using genetic variants associated with height as a proxy for observed height, have been used to strengthen the evidence for causal effects of height on adulthood diseases (Nelson et al., 2015; Nüesch et al., 2016; Zhang et al., 2015; Thrift et al., 2015; Dixon-Suen et al., 2018). The underlying premise being that genetic variants associated with height, unlike height itself, are unlikely to be associated with potential confounders such as childhood nutrition. However, there is growing evidence that estimates from genetic epidemiological studies using unrelated individuals may capture effects relating to demography (population stratification, assortative mating) and familial effects (e.g., indirect genetic effects of relatives where parental genotype influences offspring phenotypes) (Barton and Hermisson, 2019; Berg et al., 2019; Sohail et al., 2019; Ruby et al., 2018; Haworth et al., 2019; Brumpton et al., 2019; Lee et al., 2018; Kong et al., 2018; Young et al., 2018). Indeed, recent articles have illustrated the potential for genetic analyses of height to be affected by these biases (Berg et al., 2019; Sohail et al., 2019), including a Mendelian randomization study of height on education (Brumpton et al., 2019). One approach to overcome these potential biases is to use data from siblings (Brumpton et al., 2019; Davies et al., 2019) and exploit the shared early-life environment of siblings and the random segregation of alleles during meiosis (Smith and Ebrahim, 2003). Indeed, true Mendelian randomization was initially proposed as existing within a parent-offspring design (Smith and Ebrahim, 2003; Davey Smith et al., 2020; Figure 1).

Figure 1

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Here, we used data from 40,275 siblings from UK Biobank (Bycroft et al., 2018) and 38,723 siblings from the Norwegian HUNT study (Krokstad et al., 2013) to estimate the effects of adulthood height on CHD, cancer risk, and relevant biomarkers. Study-level information is contained in Table 1. We report the estimates of the effects of height on CHD and cancer from both phenotypic models and Mendelian randomization, with and without accounting for family structure.

In this study, we used sibling data from two large biobanks to estimate the effects of height on CHD, cancer, and relevant biomarkers. We found consistent evidence across all models, including within-sibship Mendelian randomization, that taller height is protective against CHD but increases the risk of cancers. We found less consistent evidence for the effects of height on biomarkers; population and within-sibship phenotypic models as well as population Mendelian randomization models suggested modest effects of taller height on SBP, LDL-C, and HDL-C. However, the confidence intervals for within-family Mendelian randomization of height and biomarkers were too wide to draw strong conclusions.

Our findings are largely consistent with previous studies (Emerging Risk Factors Collaboration, 2012; Nelson et al., 2015; Nüesch et al., 2016; Hebert et al., 1993; Marouli et al., 2019; Green et al., 2011; Zhang et al., 2015; Thrift et al., 2015; Dixon-Suen et al., 2018; Carslake et al., 2013) that used nonsibling designs, and with the hypothesis that height affects CHD and cancer risk. However, previous studies were potentially susceptible to bias relating to geographic and socioeconomic variation in height and height genetic variants (Barton and Hermisson, 2019; Sohail et al., 2019; Lee et al., 2018). Indeed, a recent within-sibship Mendelian randomization study found that the previously reported effects of height and body mass index on educational attainment were greatly attenuated when using siblings (Brumpton et al., 2019). Here, we provided robust evidence for individual-level effects of height by demonstrating that the previous evidence for effects of height on adulthood disease risk is unlikely to have been confounded by demography or indirect genetic effects. The major strengths of our work are the use of within-sibship Mendelian randomization (Davies et al., 2019) and the triangulation (Lawlor et al., 2016) of evidence from across phenotypic, genetic, and within-sibship models.

A limitation of our analyses is that because of limited sibling data and the statistical inefficiency of within-family models, we have limited statistical power to investigate the effects of height on disease subtypes,further explore the mechanisms using multivariable Mendelian randomization (Burgess and Thompson, 2015), and perform sensitivity analyses to evaluate horizontal pleiotropy. An additional limitation is that our study may have been susceptible to selection and survival biases relating to nonrandom participation in UK Biobank and/or HUNT and the requirement of at least two siblings to survive to be recruited. Indeed, cancer and CHD are both leading worldwide causes of mortality and so cases for one disease may have a reduced likelihood of developing the other disease due to increased mortality. Therefore, our study may have been susceptible to survival bias relating to competing risks. We mitigated this by defining cases for both diseases using both nonfatal and fatal events. Our study analyzed families with two or more siblings jointly participating in a cohort; nevertheless, further research is required to investigate the impact of selection bias on family studies.

Adulthood height is nonmodifiable, and the interpretation of causality is nuanced because it is unclear whether biological effects relate to stature itself, increased childhood growth, or to factors highly correlated with height such as lung function (Marouli et al., 2019; Gunnell et al., 2003) and artery length (Palmer et al., 1990). Previous studies Gunnell et al., 2001; Langenberg et al., 2003; Gunnell et al., 2003; Regnault et al., 2014 have explored the possibility that associations may relate to dimensions of height, with evidence that blood pressure is associated with trunk but not leg length (Regnault et al., 2014). Here, we found that the effects of height on disease risk due to leg or trunk length were similar. We found consistent effects of increased height across etiologically heterogeneous cancer subtypes, which implies that the mechanism could relate to the larger number of cells in taller individuals or a generalized growth phenotype. Notably there is minimal evidence of a correlation between the size of an organism and cancer risk (Peto’s paradox), suggesting that the number of cells hypothesis could influence cancer risk in humans but would not explain variation in cancer risk across different organisms (Caulin and Maley, 2011). Our Mendelian randomization estimates for the effects of height on a subset of cancers not strongly phenotypically associated with height (Green et al., 2011) were consistent with the combined cancer estimates, although we had limited power in this dataset because of the modest prevalence of the cancer subtypes.

The estimated effects of height on disease risk were relatively consistent between the Norwegian HUNT and UK Biobank studies. Contrastingly, the heterogeneity between UK Biobank and HUNT for analyses involving SBP and LDL-C suggests that some effects of height could be population specific. Alternatively, heterogeneity could relate to the variance in associations between adulthood height and early-life environmental confounders across countries (Perkins et al., 2016). Additional explanations could relate to the differences in biomarker measurement between studies (e.g., measuring LDL-C directly or using the Friedewald formula, differences in fasting level before samples were taken), selection bias (Munafò et al., 2016), or differences between the cohorts in terms of recruitment and participation. Further work is required to investigate if our findings generalize to non-European populations; biological mechanisms could be expected to be largely consistent across populations but context-specific (e.g., social) mechanisms could lead to geographic heterogeneity.

To conclude, using within-sibship Mendelian randomization, we showed that height has individual-level effects on risk of CHD and cancers as well as several biomarkers. Larger family datasets and additional analyses including two-step (Relton and Davey Smith, 2012) and multivariable Mendelian randomization (Burgess and Thompson, 2015) could be used to investigate the potential mediators of these relationships.

We used individual level data from the UK Biobank and HUNT cohorts. Participants in these studies have consented to the use of their data in medical research and so these data are not publicly available. Data access can be applied for by qualified researchers. For access to UK Biobank individual level participant data, please send enquiries to access@ukbiobank.ac.uk and see information on the UK Biobank website http://www.ukbiobank.ac.uk. UK Biobank access generally involves submitting project proposals which are evaluated by the study data access committee. Researchers associated with Norwegian research institutes can apply for the use of HUNT data and samples with approval by the Regional Committee for Medical and Health Research Ethics. HUNT data is governed by Norwegian law, therefore researchers from other countries may apply if collaborating with a Norwegian Principal Investigator. Detailed information on the data access procedure of HUNT can be found at https://www.ntnu.edu/hunt/data. Statistical code for population and within-sibship models used in the manuscript is available on GitHub https://github.com/LaurenceHowe/WithinSibshipModels/ (copy archived at swh:1:rev:44d2435d841bc424b56eee2d8534d52dd4adf763).

1. 1. Albanes D
   2. Winick M

   (1988) Are Cell Number and Cell Proliferation Risk Factors for Cancer?1

   Journal of the National Cancer Institute 80:772–774.

   https://doi.org/10.1093/jnci/80.10.772

   • PubMed
   • Google Scholar
2. 1. Altman DG
   2. Bland JM

   (2003) Interaction revisited: the difference between two estimates

   BMJ (Clinical Research Ed.) 326:219.

   https://doi.org/10.1136/bmj.326.7382.219

   • PubMed
   • Google Scholar
3. 1. Barton N
   2. Hermisson J

   (2019) Why structure matters

   eLife 8:e45380.

   https://doi.org/10.7554/eLife.45380

   • Google Scholar
4. 1. Batty GD
   2. Shipley MJ
   3. Langenberg C
   4. Marmot MG
   5. Davey Smith G

   (2006) Adult height in relation to mortality from 14 cancer sites in men in London (UK): evidence from the original Whitehall study

   Annals of Oncology 17:157–166.

   https://doi.org/10.1093/annonc/mdj018

   • PubMed
   • Google Scholar
5. 1. Batty GD
   2. Shipley MJ
   3. Gunnell D
   4. Huxley R
   5. Kivimaki M
   6. Woodward M
   7. Lee CMY
   8. Smith GD

   (2009) Height, wealth, and health: an overview with new data from three longitudinal studies

   Economics and Human Biology 7:137–152.

   https://doi.org/10.1016/j.ehb.2009.06.004

   • PubMed
   • Google Scholar
6. 1. Berg JJ
   2. Harpak A
   3. Sinnott-Armstrong N

   (2019) Reduced signal for polygenic adaptation of height in UK Biobank

   eLife 8:e39725.

   https://doi.org/10.7554/eLife.39725

   • Google Scholar
7. 1. Brumpton B
   2. Sanderson E
   3. Hartwig FP
   4. Harrison S
   5. Vie GÅ
   6. Cho Y
   7. Howe LD
   8. Hughes A
   9. Boomsma DI
   10. Havdahl A
   11. Hopper J
   12. Neale M
   13. Nivard MG
   14. Pedersen NL
   15. Reynolds CA
   16. Tucker-Drob EM
   17. Grotzinger A
   18. Howe L
   19. Morris T
   20. Li S
   21. Chen WM
   22. Bjørngaard JH
   23. Hveem K
   24. Willer C
   25. Evans DM
   26. Kaprio J
   27. Åsvol BO
   28. Smith GD
   29. Åsvold BO
   30. Hemani G
   31. Davies NM

   (2019) MR within-family Consortium Within-family studies for Mendelian randomization: avoiding dynastic, assortative mating, and population stratification biases

   Genetics 2020:602516.

   https://doi.org/10.1101/602516

   • Google Scholar
8. 1. Burgess S
   2. Thompson SG

   (2015) Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects

   American Journal of Epidemiology 181:251–260.

   https://doi.org/10.1093/aje/kwu283

   • PubMed
   • Google Scholar
9. 1. Bycroft C
   2. Freeman C
   3. Petkova D
   4. Band G
   5. Elliott LT
   6. Sharp K
   7. Motyer A
   8. Vukcevic D
   9. Delaneau O
   10. O’Connell J
   11. Cortes A
   12. Welsh S
   13. Young A
   14. Effingham M
   15. McVean G
   16. Leslie S
   17. Allen N
   18. Donnelly P
   19. Marchini J

   (2018) The UK Biobank resource with deep phenotyping and genomic data

   Nature 562:203–209.

   https://doi.org/10.1038/s41586-018-0579-z

   • PubMed
   • Google Scholar
10. 1. Carslake D
    2. Fraser A
    3. Davey Smith G
    4. May M
    5. Palmer T
    6. Sterne J
    7. Silventoinen K
    8. Tynelius P
    9. Lawlor DA
    10. Rasmussen F

    (2013) Associations of mortality with own height using son’s height as an instrumental variable

    Economics and Human Biology 11:351–359.

    https://doi.org/10.1016/j.ehb.2012.04.003

    • PubMed
    • Google Scholar
11. 1. Caulin AF
    2. Maley CC

    (2011) Peto’s Paradox: evolution’s prescription for cancer prevention

    Trends in Ecology & Evolution 26:175–182.

    https://doi.org/10.1016/j.tree.2011.01.002

    • PubMed
    • Google Scholar
12. 1. Clayton PE
    2. Banerjee I
    3. Murray PG
    4. Renehan AG

    (2011) Growth hormone, the insulin-like growth factor axis, insulin and cancer risk

    Nature Reviews. Endocrinology 7:11–24.

    https://doi.org/10.1038/nrendo.2010.171

    • PubMed
    • Google Scholar
13. 1. Das S
    2. Forer L
    3. Schönherr S
    4. Sidore C
    5. Locke AE
    6. Kwong A
    7. Vrieze SI
    8. Chew EY
    9. Levy S
    10. McGue M
    11. Schlessinger D
    12. Stambolian D
    13. Loh P-R
    14. Iacono WG
    15. Swaroop A
    16. Scott LJ
    17. Cucca F
    18. Kronenberg F
    19. Boehnke M
    20. Abecasis GR
    21. Fuchsberger C

    (2016) Next-generation genotype imputation service and methods

    Nature Genetics 48:1284–1287.

    https://doi.org/10.1038/ng.3656

    • PubMed
    • Google Scholar
14. 1. Davey Smith G
    2. Hart C
    3. Upton M
    4. Hole D
    5. Gillis C
    6. Watt G
    7. Hawthorne V

    (2000) Height and risk of death among men and women: aetiological implications of associations with cardiorespiratory disease and cancer mortality

    Journal of Epidemiology and Community Health 54:97–103.

    https://doi.org/10.1136/jech.54.2.97

    • PubMed
    • Google Scholar
15. 1. Davey Smith G
    2. Holmes MV
    3. Davies NM
    4. Ebrahim S

    (2020) Mendel’s laws, Mendelian randomization and causal inference in observational data: substantive and nomenclatural issues

    European Journal of Epidemiology 35:99–111.

    https://doi.org/10.1007/s10654-020-00622-7

    • PubMed
    • Google Scholar
16. 1. Davies NM
    2. Howe LJ
    3. Brumpton B
    4. Havdahl A
    5. Evans DM
    6. Davey Smith G

    (2019) Within family Mendelian randomization studies

    Human Molecular Genetics 28:R170–R179.

    https://doi.org/10.1093/hmg/ddz204

    • PubMed
    • Google Scholar
17. 1. Didelez V
    2. Sheehan N

    (2007) Mendelian randomization as an instrumental variable approach to causal inference

    Statistical Methods in Medical Research 16:309–330.

    https://doi.org/10.1177/0962280206077743

    • PubMed
    • Google Scholar
18. 1. Dixon-Suen SC
    2. Nagle CM
    3. Thrift AP
    4. Pharoah PDP
    5. Ewing A
    6. Pearce CL
    7. Zheng W
    8. Australian Ovarian Cancer Study Group
    9. Chenevix-Trench G
    10. Fasching PA
    11. Beckmann MW
    12. Lambrechts D
    13. Vergote I
    14. Lambrechts S
    15. Van Nieuwenhuysen E
    16. Rossing MA
    17. Doherty JA
    18. Wicklund KG
    19. Chang-Claude J
    20. Jung AY
    21. Moysich KB
    22. Odunsi K
    23. Goodman MT
    24. Wilkens LR
    25. Thompson PJ
    26. Shvetsov YB
    27. Dörk T
    28. Park-Simon T-W
    29. Hillemanns P
    30. Bogdanova N
    31. Butzow R
    32. Nevanlinna H
    33. Pelttari LM
    34. Leminen A
    35. Modugno F
    36. Ness RB
    37. Edwards RP
    38. Kelley JL
    39. Heitz F
    40. du Bois A
    41. Harter P
    42. Schwaab I
    43. Karlan BY
    44. Lester J
    45. Orsulic S
    46. Rimel BJ
    47. Kjær SK
    48. Høgdall E
    49. Jensen A
    50. Goode EL
    51. Fridley BL
    52. Cunningham JM
    53. Winham SJ
    54. Giles GG
    55. Bruinsma F
    56. Milne RL
    57. Southey MC
    58. Hildebrandt MAT
    59. Wu X
    60. Lu KH
    61. Liang D
    62. Levine DA
    63. Bisogna M
    64. Schildkraut JM
    65. Berchuck A
    66. Cramer DW
    67. Terry KL
    68. Bandera EV
    69. Olson SH
    70. Salvesen HB
    71. Thomsen LCV
    72. Kopperud RK
    73. Bjorge L
    74. Kiemeney LA
    75. Massuger LFAG
    76. Pejovic T
    77. Bruegl A
    78. Cook LS
    79. Le ND
    80. Swenerton KD
    81. Brooks-Wilson A
    82. Kelemen LE
    83. Lubiński J
    84. Huzarski T
    85. Gronwald J
    86. Menkiszak J
    87. Wentzensen N
    88. Brinton L
    89. Yang H
    90. Lissowska J
    91. Høgdall CK
    92. Lundvall L
    93. Song H
    94. Tyrer JP
    95. Campbell I
    96. Eccles D
    97. Paul J
    98. Glasspool R
    99. Siddiqui N
    100. Whittemore AS
    101. Sieh W
    102. McGuire V
    103. Rothstein JH
    104. Narod SA
    105. Phelan C
    106. Risch HA
    107. McLaughlin JR
    108. Anton-Culver H
    109. Ziogas A
    110. Menon U
    111. Gayther SA
    112. Ramus SJ
    113. Gentry-Maharaj A
    114. Wu AH
    115. Pike MC
    116. Tseng C-C
    117. Kupryjanczyk J
    118. Dansonka-Mieszkowska A
    119. Budzilowska A
    120. Rzepecka IK
    121. Webb PM
    122. Ovarian Cancer Association Consortium

    (2018) Adult height is associated with increased risk of ovarian cancer: a Mendelian randomisation study

    British Journal of Cancer 118:1123–1129.

    https://doi.org/10.1038/s41416-018-0011-3

    • PubMed
    • Google Scholar
19. 1. Emerging Risk Factors Collaboration

    (2012) Adult height and the risk of cause-specific death and vascular morbidity in 1 million people: individual participant meta-analysis

    International Journal of Epidemiology 41:1419–1433.

    https://doi.org/10.1093/ije/dys086

    • PubMed
    • Google Scholar
20. 1. Friedewald WT
    2. Levy RI
    3. Fredrickson DS

    (1972) Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge

    Clinical Chemistry 18:499–502.

    https://doi.org/10.1093/clinchem/18.6.499

    • PubMed
    • Google Scholar
21. 1. Genomes Project Consortium

    (2015) A global reference for human genetic variation

    Nature 526:68–74.

    https://doi.org/10.1038/nature15393

    • Google Scholar
22. 1. Green J
    2. Cairns BJ
    3. Casabonne D
    4. Wright FL
    5. Reeves G
    6. Beral V
    7. Million Women Study collaborators

    (2011) Height and cancer incidence in the Million Women Study: prospective cohort, and meta-analysis of prospective studies of height and total cancer risk

    The Lancet. Oncology 12:785–794.

    https://doi.org/10.1016/S1470-2045(11)70154-1

    • PubMed
    • Google Scholar
23. 1. Gunnell D
    2. Okasha M
    3. Smith GD
    4. Oliver SE
    5. Sandhu J
    6. Holly JM

    (2001) Height, leg length, and cancer risk: a systematic review

    Epidemiologic Reviews 23:313–342.

    https://doi.org/10.1093/oxfordjournals.epirev.a000809

    • PubMed
    • Google Scholar
24. 1. Gunnell D
    2. Whitley E
    3. Upton MN
    4. McConnachie A
    5. Smith GD
    6. Watt GCM

    (2003) Associations of height, leg length, and lung function with cardiovascular risk factors in the Midspan Family Study

    Journal of Epidemiology and Community Health 57:141–146.

    https://doi.org/10.1136/jech.57.2.141

    • PubMed
    • Google Scholar
25. 1. Hatton TJ

    (2013) How have Europeans grown so tall

    Oxford Economic Papers 66:349–372.

    https://doi.org/10.1093/oep/gpt030

    • Google Scholar
26. 1. Haworth S
    2. Mitchell R
    3. Corbin L
    4. Wade KH
    5. Dudding T
    6. Budu-Aggrey A
    7. Carslake D
    8. Hemani G
    9. Paternoster L
    10. Smith GD
    11. Davies N
    12. Lawson DJ
    13. J. Timpson N

    (2019) Apparent latent structure within the UK Biobank sample has implications for epidemiological analysis

    Nature Communications 10:8219.

    https://doi.org/10.1038/s41467-018-08219-1

    • PubMed
    • Google Scholar
27. 1. Haycock PC
    2. Burgess S
    3. Wade KH
    4. Bowden J
    5. Relton C
    6. Davey Smith G

    (2016) Best (but oft-forgotten) practices: the design, analysis, and interpretation of Mendelian randomization studies

    The American Journal of Clinical Nutrition 103:965–978.

    https://doi.org/10.3945/ajcn.115.118216

    • PubMed
    • Google Scholar
28. 1. Hebert PR
    2. Rich-Edwards JW
    3. Manson JE
    4. Ridker PM
    5. Cook NR
    6. O’Connor GT
    7. Buring JE
    8. Hennekens CH

    (1993) Height and incidence of cardiovascular disease in male physicians

    Circulation 88:1437–1443.

    https://doi.org/10.1161/01.cir.88.4.1437

    • PubMed
    • Google Scholar
29. 1. Hill WG
    2. Weir BS

    (2011) Variation in actual relationship as a consequence of Mendelian sampling and linkage

    Genetics Research 93:47–64.

    https://doi.org/10.1017/S0016672310000480

    • PubMed
    • Google Scholar
30. 1. Holmen J
    2. Midthjell K
    3. Krüger Ø

    (2003)

    The Nord-Trøndelag Health Study 1995–97 (HUNT 2): objectives, contents, methods and participation

    Norsk Epidemiologi 13:19–32.

    • Google Scholar
31. Preprint

    1. Howe LJ
    2. Nivard MG
    3. Morris TT
    4. Hansen AF
    5. Rasheed H
    6. Cho Y
    7. Chittoor G
    8. Lind PA
    9. Palviainen T
    10. van der Zee MD
    11. Cheesman R
    12. Mangino M
    13. Wang Y
    14. Li S
    15. Klaric L
    16. Ratliff SM
    17. Bielak LF
    18. Nygaard M
    19. Reynolds CA
    20. Balbona JV
    21. Bauer CR
    22. Boomsma DI
    23. Baras A
    24. Campbell A
    25. Campbell H
    26. Chen Z
    27. Christofidou P
    28. Dahm CC
    29. Dokuru DR
    30. Evans LM
    31. de Geus EJ
    32. Giddaluru S
    33. Gordon SD
    34. Harden KP
    35. Havdahl A
    36. Hill WD
    37. Kerr SM
    38. Kim Y
    39. Kweon H
    40. Latvala A
    41. Li L
    42. Lin K
    43. Martikainen P
    44. Magnusson PK
    45. Mills MC
    46. Lawlor DA
    47. Overton JD
    48. Pedersen NL
    49. Porteous DJ
    50. Reid J
    51. Silventoinen K
    52. Southey MC
    53. Mallard TT
    54. Tucker-Drob EM
    55. Wright MJ
    56. Hewitt JK
    57. Keller MC
    58. Stallings MC
    59. Christensen K
    60. Kardia SL
    61. Peyser PA
    62. Smith JA
    63. Wilson JF
    64. Hopper JL
    65. Hägg S
    66. Spector TD
    67. Pingault JB
    68. Plomin R
    69. Bartels M
    70. Martin NG
    71. Justice AE
    72. Millwood IY
    73. Hveem K
    74. Naess Ø
    75. Willer CJ
    76. Åsvold BO
    77. Koellinger PD
    78. Kaprio J
    79. Medland SE
    80. Walters RG
    81. Benjamin DJ
    82. Turley P
    83. Evans DM
    84. Smith GD
    85. Hayward C
    86. Brumpton B
    87. Hemani G
    88. Davies NM

    (2021) Within-sibship GWAS improve estimates of direct genetic effects

    bioRxiv.

    https://doi.org/10.1101/2021.03.05.433935

    • Google Scholar
32. Software

    1. Howe LJ

    (2022) WithinSibshipModels, version v1.0

    GitHub.

    https://github.com/LaurenceHowe/WithinSibshipModels
33. 1. Kong A
    2. Thorleifsson G
    3. Frigge ML
    4. Vilhjalmsson BJ
    5. Young AI
    6. Thorgeirsson TE
    7. Benonisdottir S
    8. Oddsson A
    9. Halldorsson BV
    10. Masson G
    11. Gudbjartsson DF
    12. Helgason A
    13. Bjornsdottir G
    14. Thorsteinsdottir U
    15. Stefansson K

    (2018) The nature of nurture: Effects of parental genotypes

    Science (New York, N.Y.) 359:424–428.

    https://doi.org/10.1126/science.aan6877

    • PubMed
    • Google Scholar
34. 1. Krokstad S
    2. Langhammer A
    3. Hveem K
    4. Holmen TL
    5. Midthjell K
    6. Stene TR
    7. Bratberg G
    8. Heggland J
    9. Holmen J

    (2013) Cohort Profile: the HUNT Study, Norway

    International Journal of Epidemiology 42:968–977.

    https://doi.org/10.1093/ije/dys095

    • PubMed
    • Google Scholar
35. 1. Langenberg C
    2. Hardy R
    3. Kuh D
    4. Wadsworth ME

    (2003) Influence of height, leg and trunk length on pulse pressure, systolic and diastolic blood pressure

    Journal of Hypertension 21:537–543.

    https://doi.org/10.1097/00004872-200303000-00019

    • PubMed
    • Google Scholar
36. 1. Lawlor D
    2. Harbord RM
    3. Sterne JAC
    4. Timpson N
    5. Davey Smith G

    (2008) Mendelian randomization: using genes as instruments for making causal inferences in epidemiology

    Statistics in Medicine 27:1133–1163.

    https://doi.org/10.1002/sim.3034

    • PubMed
    • Google Scholar
37. 1. Lawlor D.A
    2. Tilling K
    3. Davey Smith G

    (2016) Triangulation in aetiological epidemiology

    International Journal of Epidemiology 45:1866–1886.

    https://doi.org/10.1093/ije/dyw314

    • PubMed
    • Google Scholar
38. 1. Lee JJ
    2. Wedow R
    3. Okbay A
    4. Kong E
    5. Maghzian O
    6. Zacher M
    7. Nguyen-Viet TA
    8. Bowers P
    9. Sidorenko J
    10. Karlsson Linnér R
    11. Fontana MA
    12. Kundu T
    13. Lee C
    14. Li H
    15. Li R
    16. Royer R
    17. Timshel PN
    18. Walters RK
    19. Willoughby EA
    20. Yengo L
    21. Alver M
    22. Bao Y
    23. Clark DW
    24. Day FR
    25. Furlotte NA
    26. Joshi PK
    27. Kemper KE
    28. Kleinman A
    29. Langenberg C
    30. Mägi R
    31. Trampush JW
    32. Verma SS
    33. Wu Y
    34. Lam M
    35. Zhao JH
    36. Zheng Z
    37. Boardman JD
    38. Campbell H
    39. Freese J
    40. Harris KM
    41. Hayward C
    42. Herd P
    43. Kumari M
    44. Lencz T
    45. Luan J
    46. Malhotra AK
    47. Metspalu A
    48. Milani L
    49. Ong KK
    50. Perry JRB
    51. Porteous DJ
    52. Ritchie MD
    53. Smart MC
    54. Smith BH
    55. Tung JY
    56. Wareham NJ
    57. Wilson JF
    58. Beauchamp JP
    59. Conley DC
    60. Esko T
    61. Lehrer SF
    62. Magnusson PKE
    63. Oskarsson S
    64. Pers TH
    65. Robinson MR
    66. Thom K
    67. Watson C
    68. Chabris CF
    69. Meyer MN
    70. Laibson DI
    71. Yang J
    72. Johannesson M
    73. Koellinger PD
    74. Turley P
    75. Visscher PM
    76. Benjamin DJ
    77. Cesarini D
    78. 23andMe Research Team
    79. Social Science Genetic Association Consortium

    (2018) Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals

    Nature Genetics 50:1112–1121.

    https://doi.org/10.1038/s41588-018-0147-3

    • PubMed
    • Google Scholar
39. 1. Manichaikul A
    2. Mychaleckyj JC
    3. Rich SS
    4. Daly K
    5. Sale M
    6. Chen W-M

    (2010) Robust relationship inference in genome-wide association studies

    Bioinformatics (Oxford, England) 26:2867–2873.

    https://doi.org/10.1093/bioinformatics/btq559

    • PubMed
    • Google Scholar
40. 1. Marouli E
    2. Del Greco MF
    3. Astley CM
    4. Yang J
    5. Ahmad S
    6. Berndt SI
    7. Caulfield MJ
    8. Evangelou E
    9. McKnight B
    10. Medina-Gomez C
    11. van Vliet-Ostaptchouk JV
    12. Warren HR
    13. Zhu Z
    14. Hirschhorn JN
    15. Loos RJF
    16. Kutalik Z
    17. Deloukas P

    (2019) Mendelian randomisation analyses find pulmonary factors mediate the effect of height on coronary artery disease

    Communications Biology 2:119.

    https://doi.org/10.1038/s42003-019-0361-2

    • PubMed
    • Google Scholar
41. 1. McCarthy S
    2. Das S
    3. Kretzschmar W

    (2016) A reference panel of 64,976 haplotypes for genotype imputation

    Nature Genetics 48:1279–1283.

    https://doi.org/10.1038/ng.3643

    • PubMed
    • Google Scholar
42. 1. Munafò MR
    2. Tilling K
    3. Taylor AE
    4. Evans DM
    5. Davey Smith G

    (2016) Collider scope: when selection bias can substantially influence observed associations

    International Journal of Epidemiology 47:226–235.

    https://doi.org/10.1093/ije/dyx206

    • Google Scholar
43. 1. Nelson CP
    2. Hamby SE
    3. Saleheen D
    4. Hopewell JC
    5. Zeng L
    6. Assimes TL
    7. Kanoni S
    8. Willenborg C
    9. Burgess S
    10. Amouyel P
    11. Anand S
    12. Blankenberg S
    13. Boehm BO
    14. Clarke RJ
    15. Collins R
    16. Dedoussis G
    17. Farrall M
    18. Franks PW
    19. Groop L
    20. Hall AS
    21. Hamsten A
    22. Hengstenberg C
    23. Hovingh GK
    24. Ingelsson E
    25. Kathiresan S
    26. Kee F
    27. König IR
    28. Kooner J
    29. Lehtimäki T
    30. März W
    31. McPherson R
    32. Metspalu A
    33. Nieminen MS
    34. O’Donnell CJ
    35. Palmer CNA
    36. Peters A
    37. Perola M
    38. Reilly MP
    39. Ripatti S
    40. Roberts R
    41. Salomaa V
    42. Shah SH
    43. Schreiber S
    44. Siegbahn A
    45. Thorsteinsdottir U
    46. Veronesi G
    47. Wareham N
    48. Willer CJ
    49. Zalloua PA
    50. Erdmann J
    51. Deloukas P
    52. Watkins H
    53. Schunkert H
    54. Danesh J
    55. Thompson JR
    56. Samani NJ
    57. CARDIoGRAM+C4D Consortium

    (2015) Genetically determined height and coronary artery disease

    The New England Journal of Medicine 372:1608–1618.

    https://doi.org/10.1056/NEJMoa1404881

    • PubMed
    • Google Scholar
44. 1. Njølstad I
    2. Arnesen E
    3. Lund-Larsen PG

    (1996) Body height, cardiovascular risk factors, and risk of stroke in middle-aged men and women. A 14-year follow-up of the Finnmark Study

    Circulation 94:2877–2882.

    https://doi.org/10.1161/01.cir.94.11.2877

    • PubMed
    • Google Scholar
45. 1. Nüesch E
    2. Dale C
    3. Palmer TM
    4. White J
    5. Keating BJ
    6. van Iperen EP
    7. Goel A
    8. Padmanabhan S
    9. Asselbergs FW
    10. EPIC-Netherland Investigators
    11. Verschuren WM
    12. Wijmenga C
    13. Van der Schouw YT
    14. Onland-Moret NC
    15. Lange LA
    16. Hovingh GK
    17. Sivapalaratnam S
    18. Morris RW
    19. Whincup PH
    20. Wannamethe GS
    21. Gaunt TR
    22. Ebrahim S
    23. Steel L
    24. Nair N
    25. Reiner AP
    26. Kooperberg C
    27. Wilson JF
    28. Bolton JL
    29. McLachlan S
    30. Price JF
    31. Strachan MW
    32. Robertson CM
    33. Kleber ME
    34. Delgado G
    35. März W
    36. Melander O
    37. Dominiczak AF
    38. Farrall M
    39. Watkins H
    40. Leusink M
    41. Maitland-van der Zee AH
    42. de Groot MC
    43. Dudbridge F
    44. Hingorani A
    45. Ben-Shlomo Y
    46. Lawlor DA
    47. UCLEB Investigators
    48. Amuzu A
    49. Caufield M
    50. Cavadino A
    51. Cooper J
    52. Davies TL
    53. IN Day
    54. Drenos F
    55. Engmann J
    56. Finan C
    57. Giambartolomei C
    58. Hardy R
    59. Humphries SE
    60. Hypponen E
    61. Kivimaki M
    62. Kuh D
    63. Kumari M
    64. Ong K
    65. Plagnol V
    66. Power C
    67. Richards M
    68. Shah S
    69. Shah T
    70. Sofat R
    71. Talmud PJ
    72. Wareham N
    73. Warren H
    74. Whittaker JC
    75. Wong A
    76. Zabaneh D
    77. Davey Smith G
    78. Wells JC
    79. Leon DA
    80. Holmes MV
    81. Casas JP

    (2016) Adult height, coronary heart disease and stroke: a multi-locus Mendelian randomization meta-analysis

    International Journal of Epidemiology 45:1927–1937.

    https://doi.org/10.1093/ije/dyv074

    • PubMed
    • Google Scholar
46. 1. O’Connell J
    2. Sharp K
    3. Shrine N

    (2016) Haplotype estimation for biobank-scale data sets

    Nature Genetics 48:817–820.

    https://doi.org/10.1038/ng.3583

    • Google Scholar
47. 1. O’Connor NJ
    2. Morton JR
    3. Birkmeyer JD
    4. Olmstead EM
    5. O’Connor GT

    (1996) Effect of coronary artery diameter in patients undergoing coronary bypass surgery

    Circulation 93:652–655.

    https://doi.org/10.1161/01.CIR.93.4.652

    • PubMed
    • Google Scholar
48. 1. Palmer JR
    2. Rosenberg L
    3. Shapiro S

    (1990) Stature and the risk of myocardial infarction in women

    American Journal of Epidemiology 132:27–32.

    https://doi.org/10.1093/oxfordjournals.aje.a115639

    • PubMed
    • Google Scholar
49. 1. Perkins JM
    2. Subramanian SV
    3. Davey Smith G
    4. Özaltin E

    (2016) Adult height, nutrition, and population health

    Nutrition Reviews 74:149–165.

    https://doi.org/10.1093/nutrit/nuv105

    • PubMed
    • Google Scholar
50. 1. Purcell S
    2. Neale B
    3. Todd-Brown K
    4. Thomas L
    5. Ferreira MAR
    6. Bender D
    7. Maller J
    8. Sklar P
    9. de Bakker PIW
    10. Daly MJ
    11. Sham PC

    (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses

    American Journal of Human Genetics 81:559–575.

    https://doi.org/10.1086/519795

    • PubMed
    • Google Scholar
51. 1. Regnault N
    2. Kleinman KP
    3. Rifas-Shiman SL
    4. Langenberg C
    5. Lipshultz SE
    6. Gillman MW

    (2014) Components of height and blood pressure in childhood

    International Journal of Epidemiology 43:149–159.

    https://doi.org/10.1093/ije/dyt248

    • PubMed
    • Google Scholar
52. 1. Relton CL
    2. Davey Smith G

    (2012) Two-step epigenetic Mendelian randomization: a strategy for establishing the causal role of epigenetic processes in pathways to disease

    International Journal of Epidemiology 41:161–176.

    https://doi.org/10.1093/ije/dyr233

    • PubMed
    • Google Scholar
53. 1. Renehan AG
    2. Zwahlen M
    3. Minder C
    4. O’Dwyer ST
    5. Shalet SM
    6. Egger M

    (2004) Insulin-like growth factor (IGF)-I, IGF binding protein-3, and cancer risk: systematic review and meta-regression analysis

    Lancet (London, England) 363:1346–1353.

    https://doi.org/10.1016/S0140-6736(04)16044-3

    • PubMed
    • Google Scholar
54. 1. Ruby JG
    2. Wright KM
    3. Rand KA

    (2018) Estimates of the Heritability of Human Longevity Are Substantially Inflated due to Assortative Mating

    Genetics 210:1109–1124.

    https://doi.org/10.1534/genetics.118.301613

    • Google Scholar
55. 1. Smith GD
    2. Ebrahim S

    (2003) Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease

    International Journal of Epidemiology 32:1–22.

    https://doi.org/10.1093/ije/dyg070

    • PubMed
    • Google Scholar
56. 1. Smulyan H
    2. Marchais SJ
    3. Pannier B
    4. Guerin AP
    5. Safar ME
    6. London GM

    (1998) Influence of body height on pulsatile arterial hemodynamic data

    Journal of the American College of Cardiology 31:1103–1109.

    https://doi.org/10.1016/s0735-1097(98)00056-4

    • PubMed
    • Google Scholar
57. 1. Sohail M
    2. Maier RM
    3. Ganna A

    (2019) Polygenic adaptation on height is overestimated due to uncorrected stratification in genome-wide association studies

    eLife 8:39702.

    https://doi.org/10.7554/eLife.39702

    • Google Scholar
58. 1. Stefan N
    2. Häring H-U
    3. Hu FB
    4. Schulze MB

    (2016) Divergent associations of height with cardiometabolic disease and cancer: epidemiology, pathophysiology, and global implications

    The Lancet. Diabetes & Endocrinology 4:457–467.

    https://doi.org/10.1016/S2213-8587(15)00474-X

    • PubMed
    • Google Scholar
59. 1. Sudlow C
    2. Gallacher J
    3. Allen N
    4. Beral V
    5. Burton P
    6. Danesh J
    7. Downey P
    8. Elliott P
    9. Green J
    10. Landray M
    11. Liu B
    12. Matthews P
    13. Ong G
    14. Pell J
    15. Silman A
    16. Young A
    17. Sprosen T
    18. Peakman T
    19. Collins R

    (2015) UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age

    PLOS Medicine 12:e1001779.

    https://doi.org/10.1371/journal.pmed.1001779

    • PubMed
    • Google Scholar
60. 1. Thrift AP
    2. Gong J
    3. Peters U
    4. Chang-Claude J
    5. Rudolph A
    6. Slattery ML
    7. Chan AT
    8. Esko T
    9. Wood AR
    10. Yang J
    11. Vedantam S
    12. Gustafsson S
    13. Pers TH
    14. GIANT Consortium
    15. Baron JA
    16. Bezieau S
    17. Küry S
    18. Ogino S
    19. Berndt SI
    20. Casey G
    21. Haile RW
    22. Du M
    23. Harrison TA
    24. Thornquist M
    25. Duggan DJ
    26. Le Marchand L
    27. Lemire M
    28. Lindor NM
    29. Seminara D
    30. Song M
    31. Thibodeau SN
    32. Cotterchio M
    33. Win AK
    34. Jenkins MA
    35. Hopper JL
    36. Ulrich CM
    37. Potter JD
    38. Newcomb PA
    39. Schoen RE
    40. Hoffmeister M
    41. Brenner H
    42. White E
    43. Hsu L
    44. Campbell PT

    (2015) Mendelian randomization study of height and risk of colorectal cancer

    International Journal of Epidemiology 44:662–672.

    https://doi.org/10.1093/ije/dyv082

    • PubMed
    • Google Scholar
61. 1. Walter K
    2. Min JL
    3. Huang J
    4. Crooks L
    5. Memari Y
    6. McCarthy S
    7. Perry JRB
    8. Xu C
    9. Futema M
    10. Lawson D
    11. Iotchkova V
    12. Schiffels S
    13. Hendricks AE
    14. Danecek P
    15. Li R
    16. Floyd J
    17. Wain LV
    18. Barroso I
    19. Humphries SE
    20. Hurles ME
    21. Zeggini E
    22. Barrett JC
    23. Plagnol V
    24. Brent Richards J
    25. Greenwood CMT
    26. Timpson NJ
    27. Durbin R
    28. Soranzo N
    29. Bala S
    30. Clapham P
    31. Coates G
    32. Cox T
    33. Daly A
    34. Danecek P
    35. Du Y
    36. Durbin R
    37. Edkins S
    38. Ellis P
    39. Flicek P
    40. Guo X
    41. Guo X
    42. Huang L
    43. Jackson DK
    44. Joyce C
    45. Keane T
    46. Kolb-Kokocinski A
    47. Langford C
    48. Li Y
    49. Liang J
    50. Lin H
    51. Liu R
    52. Maslen J
    53. McCarthy S
    54. Muddyman D
    55. Quail MA
    56. Stalker J
    57. Sun J
    58. Tian J
    59. Wang G
    60. Wang J
    61. Wang Y
    62. Wong K
    63. Zhang P
    64. Barroso I
    65. Birney E
    66. Boustred C
    67. Chen L
    68. Clement G
    69. Cocca M
    70. Danecek P
    71. Davey Smith G
    72. Day INM
    73. Day-Williams A
    74. Down T
    75. Dunham I
    76. Durbin R
    77. Evans DM
    78. Gaunt TR
    79. Geihs M
    80. Greenwood CMT
    81. Hart D
    82. Hendricks AE
    83. Howie B
    84. Huang J
    85. Hubbard T
    86. Hysi P
    87. Iotchkova V
    88. Jamshidi Y
    89. Karczewski KJ
    90. Kemp JP
    91. Lachance G
    92. Lawson D
    93. Lek M
    94. Lopes M
    95. MacArthur DG
    96. Marchini J
    97. Mangino M
    98. Mathieson I
    99. McCarthy S
    100. Memari Y
    101. Metrustry S
    102. Min JL
    103. Moayyeri A
    104. Muddyman D
    105. Northstone K
    106. Panoutsopoulou K
    107. Paternoster L
    108. Perry JRB
    109. Quaye L
    110. Brent Richards J
    111. Ring S
    112. Ritchie GRS
    113. Schiffels S
    114. Shihab HA
    115. Shin SY
    116. Small KS
    117. Soler Artigas M
    118. Soranzo N
    119. Southam L
    120. Spector TD
    121. St Pourcain B
    122. Surdulescu G
    123. Tachmazidou I
    124. Timpson NJ
    125. Tobin MD
    126. Valdes AM
    127. Visscher PM
    128. Wain LV
    129. Walter K
    130. Ward K
    131. Wilson SG
    132. Wong K
    133. Yang J
    134. Zeggini E
    135. Zhang F
    136. Zheng HF
    137. Anney R
    138. Ayub M
    139. Barrett JC
    140. Blackwood D
    141. Bolton PF
    142. Breen G
    143. Collier DA
    144. Craddock N
    145. Crooks L
    146. Curran S
    147. Curtis D
    148. Durbin R
    149. Gallagher L
    150. Geschwind D
    151. Gurling H
    152. Holmans P
    153. Lee I
    154. Lönnqvist J
    155. McCarthy S
    156. McGuffin P
    157. McIntosh AM
    158. McKechanie AG
    159. McQuillin A
    160. Morris J
    161. Muddyman D
    162. O’Donovan MC
    163. Owen MJ
    164. Palotie A
    165. Parr JR
    166. Paunio T
    167. Pietilainen O
    168. Rehnström K
    169. Sharp SI
    170. Skuse D
    171. St Clair D
    172. Suvisaari J
    173. Walters JTR
    174. Williams HJ
    175. Barroso I
    176. Bochukova E
    177. Bounds R
    178. Dominiczak A
    179. Durbin R
    180. Farooqi IS
    181. Hendricks AE
    182. Keogh J
    183. Marenne G
    184. McCarthy S
    185. Morris A
    186. Muddyman D
    187. O’Rahilly S
    188. Porteous DJ
    189. Smith BH
    190. Tachmazidou I
    191. Wheeler E
    192. Zeggini E
    193. Al Turki S
    194. Anderson CA
    195. Antony D
    196. Barroso I
    197. Beales P
    198. Bentham J
    199. Bhattacharya S
    200. Calissano M
    201. Carss K
    202. Chatterjee K
    203. Cirak S
    204. Cosgrove C
    205. Durbin R
    206. Fitzpatrick DR
    207. Floyd J
    208. Reghan Foley A
    209. Franklin CS
    210. Futema M
    211. Grozeva D
    212. Humphries SE
    213. Hurles ME
    214. McCarthy S
    215. Mitchison HM
    216. Muddyman D
    217. Muntoni F
    218. O’Rahilly S
    219. Onoufriadis A
    220. Parker V
    221. Payne F
    222. Plagnol V
    223. Lucy Raymond F
    224. Roberts N
    225. Savage DB
    226. Scambler P
    227. Schmidts M
    228. Schoenmakers N
    229. Semple RK
    230. Serra E
    231. Spasic-Boskovic O
    232. Stevens E
    233. van Kogelenberg M
    234. Vijayarangakannan P
    235. Walter K
    236. Williamson KA
    237. Wilson C
    238. Whyte T
    239. Ciampi A
    240. Greenwood CMT
    241. Hendricks AE
    242. Li R
    243. Metrustry S
    244. Oualkacha K
    245. Tachmazidou I
    246. Xu C
    247. Zeggini E
    248. Bobrow M
    249. Bolton PF
    250. Durbin R
    251. Fitzpatrick DR
    252. Griffin H
    253. Hurles ME
    254. Kaye J
    255. Kennedy K
    256. Kent A
    257. Muddyman D
    258. Muntoni F
    259. Lucy Raymond F
    260. Semple RK
    261. Smee C
    262. Spector TD
    263. Timpson NJ
    264. Charlton R
    265. Ekong R
    266. Futema M
    267. Humphries SE
    268. Khawaja F
    269. Lopes LR
    270. Migone N
    271. Payne SJ
    272. Plagnol V
    273. Pollitt RC
    274. Povey S
    275. Ridout CK
    276. Robinson RL
    277. Scott RH
    278. Shaw A
    279. Syrris P
    280. Taylor R
    281. Vandersteen AM
    282. Barrett JC
    283. Barroso I
    284. Davey Smith G
    285. Durbin R
    286. Farooqi IS
    287. Fitzpatrick DR
    288. Hurles ME
    289. Kaye J
    290. Kennedy K
    291. Langford C
    292. McCarthy S
    293. Muddyman D
    294. Owen MJ
    295. Palotie A
    296. Brent Richards J
    297. Soranzo N
    298. Spector TD
    299. Stalker J
    300. Timpson NJ
    301. Zeggini E
    302. Amuzu A
    303. Pablo Casas J
    304. Chambers JC
    305. Cocca M
    306. Dedoussis G
    307. Gambaro G
    308. Gasparini P
    309. Gaunt TR
    310. Huang J
    311. Iotchkova V
    312. Isaacs A
    313. Johnson J
    314. Kleber ME
    315. Kooner JS
    316. Langenberg C
    317. Luan J
    318. Malerba G
    319. März W
    320. Matchan A
    321. Min JL
    322. Morris R
    323. Nordestgaard BG
    324. Benn M
    325. Ring S
    326. Scott RA
    327. Soranzo N
    328. Southam L
    329. Timpson NJ
    330. Toniolo D
    331. Traglia M
    332. Tybjaerg-Hansen A
    333. van Duijn CM
    334. van Leeuwen EM
    335. Varbo A
    336. Whincup P
    337. Zaza G
    338. Zeggini E
    339. Zhang W
    340. The UK10K Consortium
    341. Writing group
    342. Production group
    343. Cohorts group
    344. Neurodevelopmental disorders group
    345. Obesity group
    346. Rare disease group
    347. Statistics group
    348. Ethics group
    349. Incidental findings group
    350. Management committee
    351. Lipid meta-analysis group
    352. The UCLEB Consortium

    (2015) The UK10K project identifies rare variants in health and disease

    Nature 526:82–90.

    https://doi.org/10.1038/nature14962

    • PubMed
    • Google Scholar
62. 1. Wood AR
    2. Esko T
    3. Yang J
    4. Vedantam S
    5. Pers TH
    6. Gustafsson S
    7. Chu AY
    8. Estrada K
    9. Luan J
    10. Kutalik Z
    11. Amin N
    12. Buchkovich ML
    13. Croteau-Chonka DC
    14. Day FR
    15. Duan Y
    16. Fall T
    17. Fehrmann R
    18. Ferreira T
    19. Jackson AU
    20. Karjalainen J
    21. Lo KS
    22. Locke AE
    23. Mägi R
    24. Mihailov E
    25. Porcu E
    26. Randall JC
    27. Scherag A
    28. Vinkhuyzen AAE
    29. Westra HJ
    30. Winkler TW
    31. Workalemahu T
    32. Zhao JH
    33. Absher D
    34. Albrecht E
    35. Anderson D
    36. Baron J
    37. Beekman M
    38. Demirkan A
    39. Ehret GB
    40. Feenstra B
    41. Feitosa MF
    42. Fischer K
    43. Fraser RM
    44. Goel A
    45. Gong J
    46. Justice AE
    47. Kanoni S
    48. Kleber ME
    49. Kristiansson K
    50. Lim U
    51. Lotay V
    52. Lui JC
    53. Mangino M
    54. Mateo Leach I
    55. Medina-Gomez C
    56. Nalls MA
    57. Nyholt DR
    58. Palmer CD
    59. Pasko D
    60. Pechlivanis S
    61. Prokopenko I
    62. Ried JS
    63. Ripke S
    64. Shungin D
    65. Stancáková A
    66. Strawbridge RJ
    67. Sung YJ
    68. Tanaka T
    69. Teumer A
    70. Trompet S
    71. van der Laan SW
    72. van Setten J
    73. Van Vliet-Ostaptchouk JV
    74. Wang Z
    75. Yengo L
    76. Zhang W
    77. Afzal U
    78. Arnlöv J
    79. Arscott GM
    80. Bandinelli S
    81. Barrett A
    82. Bellis C
    83. Bennett AJ
    84. Berne C
    85. Blüher M
    86. Bolton JL
    87. Böttcher Y
    88. Boyd HA
    89. Bruinenberg M
    90. Buckley BM
    91. Buyske S
    92. Caspersen IH
    93. Chines PS
    94. Clarke R
    95. Claudi-Boehm S
    96. Cooper M
    97. Daw EW
    98. De Jong PA
    99. Deelen J
    100. Delgado G
    101. Denny JC
    102. Dhonukshe-Rutten R
    103. Dimitriou M
    104. Doney ASF
    105. Dörr M
    106. Eklund N
    107. Eury E
    108. Folkersen L
    109. Garcia ME
    110. Geller F
    111. Giedraitis V
    112. Go AS
    113. Grallert H
    114. Grammer TB
    115. Gräßler J
    116. Grönberg H
    117. de Groot L
    118. Groves CJ
    119. Haessler J
    120. Hall P
    121. Haller T
    122. Hallmans G
    123. Hannemann A
    124. Hartman CA
    125. Hassinen M
    126. Hayward C
    127. Heard-Costa NL
    128. Helmer Q
    129. Hemani G
    130. Henders AK
    131. Hillege HL
    132. Hlatky MA
    133. Hoffmann W
    134. Hoffmann P
    135. Holmen O
    136. Houwing-Duistermaat JJ
    137. Illig T
    138. Isaacs A
    139. James AL
    140. Jeff J
    141. Johansen B
    142. Johansson Å
    143. Jolley J
    144. Juliusdottir T
    145. Junttila J
    146. Kho AN
    147. Kinnunen L
    148. Klopp N
    149. Kocher T
    150. Kratzer W
    151. Lichtner P
    152. Lind L
    153. Lindström J
    154. Lobbens S
    155. Lorentzon M
    156. Lu Y
    157. Lyssenko V
    158. Magnusson PKE
    159. Mahajan A
    160. Maillard M
    161. McArdle WL
    162. McKenzie CA
    163. McLachlan S
    164. McLaren PJ
    165. Menni C
    166. Merger S
    167. Milani L
    168. Moayyeri A
    169. Monda KL
    170. Morken MA
    171. Müller G
    172. Müller-Nurasyid M
    173. Musk AW
    174. Narisu N
    175. Nauck M
    176. Nolte IM
    177. Nöthen MM
    178. Oozageer L
    179. Pilz S
    180. Rayner NW
    181. Renstrom F
    182. Robertson NR
    183. Rose LM
    184. Roussel R
    185. Sanna S
    186. Scharnagl H
    187. Scholtens S
    188. Schumacher FR
    189. Schunkert H
    190. Scott RA
    191. Sehmi J
    192. Seufferlein T
    193. Shi J
    194. Silventoinen K
    195. Smit JH
    196. Smith AV
    197. Smolonska J
    198. Stanton AV
    199. Stirrups K
    200. Stott DJ
    201. Stringham HM
    202. Sundström J
    203. Swertz MA
    204. Syvänen AC
    205. Tayo BO
    206. Thorleifsson G
    207. Tyrer JP
    208. van Dijk S
    209. van Schoor NM
    210. van der Velde N
    211. van Heemst D
    212. van Oort FVA
    213. Vermeulen SH
    214. Verweij N
    215. Vonk JM
    216. Waite LL
    217. Waldenberger M
    218. Wennauer R
    219. Wilkens LR
    220. Willenborg C
    221. Wilsgaard T
    222. Wojczynski MK
    223. Wong A
    224. Wright AF
    225. Zhang Q
    226. Arveiler D
    227. Bakker SJL
    228. Beilby J
    229. Bergman RN
    230. Bergmann S
    231. Biffar R
    232. Blangero J
    233. Boomsma DI
    234. Bornstein SR
    235. Bovet P
    236. Brambilla P
    237. Brown MJ
    238. Campbell H
    239. Caulfield MJ
    240. Chakravarti A
    241. Collins R
    242. Collins FS
    243. Crawford DC
    244. Cupples LA
    245. Danesh J
    246. de Faire U
    247. den Ruijter HM
    248. Erbel R
    249. Erdmann J
    250. Eriksson JG
    251. Farrall M
    252. Ferrannini E
    253. Ferrières J
    254. Ford I
    255. Forouhi NG
    256. Forrester T
    257. Gansevoort RT
    258. Gejman PV
    259. Gieger C
    260. Golay A
    261. Gottesman O
    262. Gudnason V
    263. Gyllensten U
    264. Haas DW
    265. Hall AS
    266. Harris TB
    267. Hattersley AT
    268. Heath AC
    269. Hengstenberg C
    270. Hicks AA
    271. Hindorff LA
    272. Hingorani AD
    273. Hofman A
    274. Hovingh GK
    275. Humphries SE
    276. Hunt SC
    277. Hypponen E
    278. Jacobs KB
    279. Jarvelin MR
    280. Jousilahti P
    281. Jula AM
    282. Kaprio J
    283. Kastelein JJP
    284. Kayser M
    285. Kee F
    286. Keinanen-Kiukaanniemi SM
    287. Kiemeney LA
    288. Kooner JS
    289. Kooperberg C
    290. Koskinen S
    291. Kovacs P
    292. Kraja AT
    293. Kumari M
    294. Kuusisto J
    295. Lakka TA
    296. Langenberg C
    297. Le Marchand L
    298. Lehtimäki T
    299. Lupoli S
    300. Madden PAF
    301. Männistö S
    302. Manunta P
    303. Marette A
    304. Matise TC
    305. McKnight B
    306. Meitinger T
    307. Moll FL
    308. Montgomery GW
    309. Morris AD
    310. Morris AP
    311. Murray JC
    312. Nelis M
    313. Ohlsson C
    314. Oldehinkel AJ
    315. Ong KK
    316. Ouwehand WH
    317. Pasterkamp G
    318. Peters A
    319. Pramstaller PP
    320. Price JF
    321. Qi L
    322. Raitakari OT
    323. Rankinen T
    324. Rao DC
    325. Rice TK
    326. Ritchie M
    327. Rudan I
    328. Salomaa V
    329. Samani NJ
    330. Saramies J
    331. Sarzynski MA
    332. Schwarz PEH
    333. Sebert S
    334. Sever P
    335. Shuldiner AR
    336. Sinisalo J
    337. Steinthorsdottir V
    338. Stolk RP
    339. Tardif JC
    340. Tönjes A
    341. Tremblay A
    342. Tremoli E
    343. Virtamo J
    344. Vohl MC
    345. Amouyel P
    346. Asselbergs FW
    347. Assimes TL
    348. Bochud M
    349. Boehm BO
    350. Boerwinkle E
    351. Bottinger EP
    352. Bouchard C
    353. Cauchi S
    354. Chambers JC
    355. Chanock SJ
    356. Cooper RS
    357. de Bakker PIW
    358. Dedoussis G
    359. Ferrucci L
    360. Franks PW
    361. Froguel P
    362. Groop LC
    363. Haiman CA
    364. Hamsten A
    365. Hayes MG
    366. Hui J
    367. Hunter DJ
    368. Hveem K
    369. Jukema JW
    370. Kaplan RC
    371. Kivimaki M
    372. Kuh D
    373. Laakso M
    374. Liu Y
    375. Martin NG
    376. März W
    377. Melbye M
    378. Moebus S
    379. Munroe PB
    380. Njølstad I
    381. Oostra BA
    382. Palmer CNA
    383. Pedersen NL
    384. Perola M
    385. Pérusse L
    386. Peters U
    387. Powell JE
    388. Power C
    389. Quertermous T
    390. Rauramaa R
    391. Reinmaa E
    392. Ridker PM
    393. Rivadeneira F
    394. Rotter JI
    395. Saaristo TE
    396. Saleheen D
    397. Schlessinger D
    398. Slagboom PE
    399. Snieder H
    400. Spector TD
    401. Strauch K
    402. Stumvoll M
    403. Tuomilehto J
    404. Uusitupa M
    405. van der Harst P
    406. Völzke H
    407. Walker M
    408. Wareham NJ
    409. Watkins H
    410. Wichmann HE
    411. Wilson JF
    412. Zanen P
    413. Deloukas P
    414. Heid IM
    415. Lindgren CM
    416. Mohlke KL
    417. Speliotes EK
    418. Thorsteinsdottir U
    419. Barroso I
    420. Fox CS
    421. North KE
    422. Strachan DP
    423. Beckmann JS
    424. Berndt SI
    425. Boehnke M
    426. Borecki IB
    427. McCarthy MI
    428. Metspalu A
    429. Stefansson K
    430. Uitterlinden AG
    431. van Duijn CM
    432. Franke L
    433. Willer CJ
    434. Price AL
    435. Lettre G
    436. Loos RJF
    437. Weedon MN
    438. Ingelsson E
    439. O’Connell JR
    440. Abecasis GR
    441. Chasman DI
    442. Goddard ME
    443. Visscher PM
    444. Hirschhorn JN
    445. Frayling TM
    446. PAGEGE Consortium
    447. LifeLines Cohort Study

    (2014) Defining the role of common variation in the genomic and biological architecture of adult human height

    Nature Genetics 46:1173–1186.

    https://doi.org/10.1038/ng.3097

    • PubMed
    • Google Scholar
63. 1. Young AI
    2. Frigge ML
    3. Gudbjartsson DF
    4. Thorleifsson G
    5. Bjornsdottir G
    6. Sulem P
    7. Masson G
    8. Thorsteinsdottir U
    9. Stefansson K
    10. Kong A

    (2018) Relatedness disequilibrium regression estimates heritability without environmental bias

    Nature Genetics 50:1304–1310.

    https://doi.org/10.1038/s41588-018-0178-9

    • PubMed
    • Google Scholar
64. 1. Zhang B
    2. Shu X-O
    3. Delahanty RJ
    4. Zeng C
    5. Michailidou K
    6. Bolla MK
    7. Wang Q
    8. Dennis J
    9. Wen W
    10. Long J
    11. Li C
    12. Dunning AM
    13. Chang-Claude J
    14. Shah M
    15. Perkins BJ
    16. Czene K
    17. Darabi H
    18. Eriksson M
    19. Bojesen SE
    20. Nordestgaard BG
    21. Nielsen SF
    22. Flyger H
    23. Lambrechts D
    24. Neven P
    25. Wildiers H
    26. Floris G
    27. Schmidt MK
    28. Rookus MA
    29. van den Hurk K
    30. de Kort WLAM
    31. Couch FJ
    32. Olson JE
    33. Hallberg E
    34. Vachon C
    35. Rudolph A
    36. Seibold P
    37. Flesch-Janys D
    38. Peto J
    39. Dos-Santos-Silva I
    40. Fletcher O
    41. Johnson N
    42. Nevanlinna H
    43. Muranen TA
    44. Aittomäki K
    45. Blomqvist C
    46. Li J
    47. Humphreys K
    48. Brand J
    49. Guénel P
    50. Truong T
    51. Cordina-Duverger E
    52. Menegaux F
    53. Burwinkel B
    54. Marme F
    55. Yang R
    56. Surowy H
    57. Benitez J
    58. Zamora MP
    59. Perez JIA
    60. Cox A
    61. Cross SS
    62. Reed MWR
    63. Andrulis IL
    64. Knight JA
    65. Glendon G
    66. Tchatchou S
    67. Sawyer EJ
    68. Tomlinson I
    69. Kerin MJ
    70. Miller N
    71. Chenevix-Trench G
    72. kConFab Investigators, Australian Ovarian Study Group
    73. Haiman CA
    74. Henderson BE
    75. Schumacher F
    76. Marchand LL
    77. Lindblom A
    78. Margolin S
    79. Hooning MJ
    80. Martens JWM
    81. Tilanus-Linthorst MMA
    82. Collée JM
    83. Hopper JL
    84. Southey MC
    85. Tsimiklis H
    86. Apicella C
    87. Slager S
    88. Toland AE
    89. Ambrosone CB
    90. Yannoukakos D
    91. Giles GG
    92. Milne RL
    93. McLean C
    94. Fasching PA
    95. Haeberle L
    96. Ekici AB
    97. Beckmann MW
    98. Brenner H
    99. Dieffenbach AK
    100. Arndt V
    101. Stegmaier C
    102. Swerdlow AJ
    103. Ashworth A
    104. Orr N
    105. Jones M
    106. Figueroa J
    107. Garcia-Closas M
    108. Brinton L
    109. Lissowska J
    110. Dumont M
    111. Winqvist R
    112. Pylkäs K
    113. Jukkola-Vuorinen A
    114. Grip M
    115. Brauch H
    116. Brüning T
    117. Ko Y-D
    118. Peterlongo P
    119. Manoukian S
    120. Bonanni B
    121. Radice P
    122. Bogdanova N
    123. Antonenkova N
    124. Dörk T
    125. Mannermaa A
    126. Kataja V
    127. Kosma V-M
    128. Hartikainen JM
    129. Devilee P
    130. Seynaeve C
    131. Van Asperen CJ
    132. Jakubowska A
    133. Lubiński J
    134. Jaworska-Bieniek K
    135. Durda K
    136. Hamann U
    137. Torres D
    138. Schmutzler RK
    139. Neuhausen SL
    140. Anton-Culver H
    141. Kristensen VN
    142. Grenaker Alnæs GI
    143. DRIVE Project
    144. Pierce BL
    145. Kraft P
    146. Peters U
    147. Lindstrom S
    148. Seminara D
    149. Burgess S
    150. Ahsan H
    151. Whittemore AS
    152. John EM
    153. Gammon MD
    154. Malone KE
    155. Tessier DC
    156. Vincent D
    157. Bacot F
    158. Luccarini C
    159. Baynes C
    160. Ahmed S
    161. Maranian M
    162. Healey CS
    163. González-Neira A
    164. Pita G
    165. Alonso MR
    166. Álvarez N
    167. Herrero D
    168. Pharoah PDP
    169. Simard J
    170. Hall P
    171. Hunter DJ
    172. Easton DF
    173. Zheng W

    (2015) Height and Breast Cancer Risk: Evidence From Prospective Studies and Mendelian Randomization

    Journal of the National Cancer Institute 107:djv219.

    https://doi.org/10.1093/jnci/djv219

    • PubMed
    • Google Scholar
65. 1. Zhou W
    2. Fritsche LG
    3. Das S
    4. Zhang H
    5. Nielsen JB
    6. Holmen OL
    7. Chen J
    8. Lin M
    9. Elvestad MB
    10. Hveem K
    11. Abecasis GR
    12. Kang HM
    13. Willer CJ

    (2017) Improving power of association tests using multiple sets of imputed genotypes from distributed reference panels

    Genetic Epidemiology 41:744–755.

    https://doi.org/10.1002/gepi.22067

    • PubMed
    • Google Scholar

Decision letter after peer review:

Thank you for submitting your article "Taller height and risk of coronary heart disease and cancer: a within-sibship Mendelian randomization study" for consideration by eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and a Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Sara Hägg (Reviewer #2).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this letter to help you prepare a revised submission.

Essential revisions:

1. A major source of bias in studies of cause and effect is selection (or collider) bias. The study also needs to consider possible selection bias from inevitably only selecting survivors to recruitment of their height (measured or genetically endowed), the disease of interest and any competing risk of the disease of interest. Whether, the sibling design, by requiring at least two siblings to survive to recruitment, is more open to selection bias than a population-based design could also be considered.

2. The study provides an interesting comparison of four different methods. Height increasing cancer risk is plausible because of the potential mechanism given, i.e., IGF1, the consistency with well accepted biological theories from evolutionary biology, and that cancer deaths tend to occur at younger ages than cardiovascular disease deaths. The results for coronary artery disease are possible but, could be overstated Greater consideration should be also be given to as to how "an individual-level causal effect" observed largely in Europeans generalizes.

3. Whether the findings for height decreasing the risk of coronary heart disease could be partly due to an artefact of survivorship bias (https://en.wikipedia.org/wiki/Survivorship_bias) arising from taller people dying of cancer before they have a chance to get coronary heart disease should be considered in more detail.

4. Please explain more clearly about adjustment for age, does it account for the fact that risk of disease goes up exponentially with age? Please also consider whether cohort is also relevant. Cohort may affect both height and disease risk. In the UK Biobank the recruitment period is quite short, so cohort should not be an issue. Please clarify over what time period the HUNT study was recruited and whether cohort should be considered as well as age.

5. Adhere to eLife title standards. Also, not clear that both observational and MR estimates are calculated on individual level data from HUNT and UKB. I think the title should be modified and make more justice to the study.

Same thing with abstract. Lots of background added but not the phenotypic associations. By the way, why call it phenotypic- the usual phrase is observational association? Biomarker results not described either.

6. Add more discussion on the CVD-cancer paradox that has been discussed in literature.

Reviewer #1 (Recommendations for the authors):

Methods

Please explain more clearly about adjustment for age, does it account for the fact that risk of disease goes up exponentially with age? Please also consider whether cohort is also relevant. Cohort may affect both height and disease risk. In the UK Biobank the recruitment period is quite short, so cohort should not be an issue. Please clarify over what time period the HUNT study was recruited and whether cohort should be considered as well as age.

Is a p-value cutoff given for SNP selection into the height PGS?

Please clarify whether prevalent cases were included as well as incident cases. Given, genetics are a lifelong exposure, it might be better to include prevalent cases, so as to avoid time related biases, as explained here[1].

Results

Please give a table describing the baseline characteristics of the two studies, particularly in terms of age and sex, and possibly recruitment period.

Discussion

Please consider the limitations of this study in more detail, specifically.

1. Using a PGS for the exposure increases power but means it is difficult to check for any pleotropic effects. How might this affect interpretation of the study?

2. This study is carefully designed to avoid use of overlapping samples for exposure and outcome. Is the study used for height unbiased?

3. The possibility of selection bias from survivorship. This study only includes people who have survived to recruitment, so it will be missing people who died before recruitment because of their height or height-related genetic endowment, the disease of interest or a competing risk of the disease of interest. Missing these people can attenuate or reverse estimates for exposures that affect survival. If cancer causes death at earlier ages than coronary heart disease then the results for coronary heart disease could be an artefact of the competing risk of previous death from cancer.

4. Please clarify whether the results presented are specific to the UK Biobank and HUNT studies or explain their generalizability and transportability.

5. Could differences between UK Biobank and HUNT be due to differences to design?

6. Are both the comparisons between the sibling and population estimates for cancer and coronary heart disease in the expected directions?

[1] Hernán MA, Sauer BC, Hernández-Díaz S, Platt R, Shrier I: Specifying a target trial prevents immortal time bias and other self-inflicted injuries in observational analyses. J Clin Epidemiol 2016, 79:70-75.

Reviewer #2 (Recommendations for the authors):

How likely is it that the earlier MR results are misleading? Biased by demographics etc? It relates to the novelty of the paper because the MR association has been shown before multiple times.

What is the rationale for doing the biomarker analysis? Would it not be more informative to investigate if they mediate the effect from height on CHD?

Table 1 should be sample characteristics in HUNT and UKB.

What about sex stratifications? Is it possible to do for same-sex sib pairs? Is it of importance in the population at large regardless of sib ship pairs? For the traits used here it should matter.

All figures and tables should contain information so that they are stand alone items.

Essential revisions:

1. A major source of bias in studies of cause and effect is selection (or collider) bias. The study also needs to consider possible selection bias from inevitably only selecting survivors to recruitment of their height (measured or genetically endowed), the disease of interest and any competing risk of the disease of interest. Whether, the sibling design, by requiring at least two siblings to survive to recruitment, is more open to selection bias than a population-based design could also be considered.

We have added the possibility of selection (collider) bias to the limitations section of the discussion. We have also noted that further work is required to understand the susceptibility of family studies to selection bias.

“An additional limitation is that our study may have been susceptible to selection and survival biases relating to non-random participation in UK Biobank and/or HUNT and the requirement of at least two siblings to survive to be recruited. Indeed, cancer and coronary heart disease are both leading worldwide causes of mortality and so cases for one disease may have reduced likelihood of developing the other disease due to increased mortality. Therefore, our study may have been susceptible to survival bias relating to competing risks. We mitigated this by defining cases for both diseases using both non-fatal and fatal events. Our study analysed families with two or more siblings jointly participating in a cohort; nevertheless, further research is required to investigate the impact of selection bias on family studies.”

2. The study provides an interesting comparison of four different methods. Height increasing cancer risk is plausible because of the potential mechanism given, i.e., IGF1, the consistency with well accepted biological theories from evolutionary biology, and that cancer deaths tend to occur at younger ages than cardiovascular disease deaths. The results for coronary artery disease are possible but, could be overstated Greater consideration should be also be given to as to how "an individual-level causal effect" observed largely in Europeans generalizes.

We have added to the discussion how our conclusions are based on individuals of recent European descent and so may not generalise to non-European populations if the mechanisms are context-specific.

“Further work is required to investigate if our findings generalise to non-European populations; biological mechanisms could be expected to be largely consistent across populations but context-specific (e.g., social) mechanisms could lead to geographical heterogeneity.”

3. Whether the findings for height decreasing the risk of coronary heart disease could be partly due to an artefact of survivorship bias (https://en.wikipedia.org/wiki/Survivorship_bias) arising from taller people dying of cancer before they have a chance to get coronary heart disease should be considered in more detail.

We have added discussion of competing risks between the two diseases to the discussion.

“An additional limitation is that our study may have been susceptible to selection and survival biases relating to non-random participation in UK Biobank and/or HUNT and the requirement of at least two siblings to survive to be recruited. Indeed, cancer and coronary heart disease are both leading worldwide causes of mortality and so cases for one disease may have reduced likelihood of developing the other disease due to increased mortality. Therefore, our study may have been susceptible to survival bias relating to competing risks. We mitigated this by defining cases for both diseases using both non-fatal and fatal events. Our study analysed families with two or more siblings jointly participating in a cohort; nevertheless, further research is required to investigate the impact of selection bias on family studies.”

4. Please explain more clearly about adjustment for age, does it account for the fact that risk of disease goes up exponentially with age? Please also consider whether cohort is also relevant. Cohort may affect both height and disease risk. In the UK Biobank the recruitment period is quite short, so cohort should not be an issue. Please clarify over what time period the HUNT study was recruited and whether cohort should be considered as well as age.

We adjusted for age (birth year / birth cohort) to capture phenotypic variation in both height and the disease/biomarker outcomes (disease risk increasing with age). Age / birth year is a potential confounder of the relationship between measured height and an outcome. However, age/birth year is very unlikely to confound the relationship between height genetic variants and an outcome because these factors cannot plausibly influence genotype. Selection on height could influence allele frequencies over time leading to variation by birth year but any changes in the context of this study would be minimal.

Similarly, cohort (HUNT 2 or HUNT 3) could be a potential confounder of the phenotypic analyses but is very unlikely to confound the MR analyses. The primary focus of our manuscript is on the within-sibship MR analyses and the triangulation of evidence across different study designs. We generally found consistent effect estimates across both phenotypic and MR analyses suggesting that non-linear age and cohort effects are unlikely to have explained our results.

At the request of the reviewers, we have added Table 1 which contains information on time periods of recruitment and other study-level information.

5. Adhere to eLife title standards. Also, not clear that both observational and MR estimates are calculated on individual level data from HUNT and UKB. I think the title should be modified and make more justice to the study.

The eLife guide to authors requires titles to be less than 120 characters, gives the following example:

Predicting the effect of statins on cancer risk using genetic variants from a Mendelian randomization study in the UK Biobank (125 characters)

We have removed the colon from our title to read:

“Taller height and risk of coronary heart disease and cancer, a within-sibship Mendelian randomization study” (107 characters)

We cannot include the study names (UK Biobank and HUNT) because the title would become too long. However, we are happy to consider alternative titles.

Same thing with abstract. Lots of background added but not the phenotypic associations. By the way, why call it phenotypic- the usual phrase is observational association? Biomarker results not described either.

We previously did not mention the phenotypic associations in the abstract in the interests of word count and brevity. We have now shortened the background section and added information on the phenotypic associations.

We used “phenotypic” over “observational” to describe the non-genetic analyses because the term “observational” is somewhat misleading because genetic data is also observational. We believe “phenotypic” makes it clearer that the analysis is non-genetic and only using phenotype data. However, we are happy to modify the terminology to “observational” at the discretion of the editors.

6. Add more discussion on the CVD-cancer paradox that has been discussed in literature.

Peto’s paradox notes that cancer risk correlates with body size within a species but cancer risk between species does not correlate with body size. We have added discussion of this to the manuscript.

“Notably there is minimal evidence of a correlation between the size of an organism and cancer risk (Peto’s paradox) suggesting that the number of cells hypothesis could influence cancer risk in humans but would not explain variation in cancer risk across different organisms ⁶³.”

Reviewer #1 (Recommendations for the authors):

Methods

Please explain more clearly about adjustment for age, does it account for the fact that risk of disease goes up exponentially with age? Please also consider whether cohort is also relevant. Cohort may affect both height and disease risk. In the UK Biobank the recruitment period is quite short, so cohort should not be an issue. Please clarify over what time period the HUNT study was recruited and whether cohort should be considered as well as age.

We adjusted for age (birth year) to capture phenotypic variation in both height and the disease/biomarker outcomes (disease risk increasing with age). Age is a potential confounder of the relationship between measured height and an outcome. However, age/birth year is very unlikely to confound the relationship between height genetic variants and an outcome because these factors cannot plausibly influence genotype. Selection on height could influence allele frequencies over time leading to variation by birth year but any changes in the context of this study would be minimal.

Similarly, cohort (HUNT 2 or HUNT 3) could be a potential confounder of the phenotypic analyses but is very unlikely to confound the MR analyses. The primary focus of our manuscript is on the within-sibship MR analyses and the triangulation of evidence across different study designs. We generally found consistent effect estimates across both phenotypic and MR analyses suggesting that non-linear age and cohort effects are unlikely to have influenced our results.

At the request of the reviewers, we have added Table 1 which contains information on time periods of recruitment and other study-level information.

Is a p-value cutoff given for SNP selection into the height PGS?

We have modified the text to clarify that the height PGS was constructed using genome-wide significant variants. “The height PGS was constructed in PLINK ⁵² using 372 independent (LD clumping: 250 kb, r2 < 0.01, P < 5x10^-8)”

Please clarify whether prevalent cases were included as well as incident cases. Given, genetics are a lifelong exposure, it might be better to include prevalent cases, so as to avoid time related biases, as explained here[1].

We have clarified in the methods that both prevalent and incident cases were included in analyses in UK Biobank (cancer/CHD) and HUNT (CHD). Cancer in HUNT is an exception as this data was extracted from questionnaires at study baseline and so only prevalent cases were included, and there was no longitudinal follow-up.

Height is largely non-modifiable from early adulthood and height genetic variants are fixed at conception, while coronary heart disease and cancer cases (both prevalent and incident) are likely to arise decades later. Therefore, we believe that the inclusion of both prevalent and incident cases to maximise power is appropriate.

Results

Please give a table describing the baseline characteristics of the two studies, particularly in terms of age and sex, and possibly recruitment period.

We have added a table detailing the baseline characteristics of the UK Biobank and HUNT study populations to the manuscript (Table 1).

Discussion

Please consider the limitations of this study in more detail, specifically.

1. Using a PGS for the exposure increases power but means it is difficult to check for any pleotropic effects. How might this affect interpretation of the study?

Horizontal pleiotropy is a potential bias in MR studies. Here, we focused on tackling other potential biases in MR studies (population stratification, assortative mating, and indirect genetic effects) using a within-sibship design. In theory, pleiotropy can also be evaluated in within-sibship MR. However, within family analyses generally have far lower statistical power than MR using data from unrelated individuals. Other studies have investigated whether horizontal pleiotropy can explain this relationship using unrelated individuals, these studies have generally found little evidence that horizontal pleiotropy explains these relationships. For example, see: Marouli E, Del Greco MF, Astley CM, et al. Mendelian randomisation analyses find pulmonary factors mediate the effect of height on coronary artery disease. Communications biology. We have added to the limitations, that with limited power, we did not perform sensitivity analyses for horizontal pleiotropy.

“A limitation of our analyses is that because of limited sibling data and the statistical inefficiency of within-family models, we have limited statistical power to investigate effects of height on disease subtypes, to further explore mechanisms using multivariable Mendelian randomization ⁶⁰ and to perform sensitivity analyses to evaluate horizontal pleiotropy.”

2. This study is carefully designed to avoid use of overlapping samples for exposure and outcome. Is the study used for height unbiased?

We used summary statistics from the GIANT-consortium height GWAS of unrelated individuals to extract information on height SNPs. These summary data are likely to be affected by population stratification, assortative and indirect genetic effects. We included only genome-wide significant variants which, although potentially affected by these sources of associations, are still likely to be valid genetic instruments for height. Indeed, we found that the height PGS constructed from these SNPs was strongly associated with height in the within-sibship model in both studies.

3. The possibility of selection bias from survivorship. This study only includes people who have survived to recruitment, so it will be missing people who died before recruitment because of their height or height-related genetic endowment, the disease of interest or a competing risk of the disease of interest. Missing these people can attenuate or reverse estimates for exposures that affect survival. If cancer causes death at earlier ages than coronary heart disease then the results for coronary heart disease could be an artefact of the competing risk of previous death from cancer.

We have added discussion of competing risks between the two diseases to the discussion.

“An additional limitation is that our study may have been susceptible to selection and survival biases relating to non-random participation in UK Biobank and/or HUNT and the requirement of at least two siblings to survive to be recruited. Indeed, cancer and coronary heart disease are both leading worldwide causes of mortality and so cases for one disease may have reduced likelihood of developing the other disease due to increased mortality. Therefore, our study may have been susceptible to survival bias relating to competing risks. We mitigated this by defining cases for both diseases using both non-fatal and fatal events. Our study analysed families with two or more siblings jointly participating in a cohort; nevertheless, further research is required to investigate the impact of selection bias on family studies.”

4. Please clarify whether the results presented are specific to the UK Biobank and HUNT studies or explain their generalizability and transportability.

We have added to the discussion how our conclusions are based on individuals of recent European descent and so may not generalise to non-European populations if the mechanisms are context-specific.

“Further work is required to investigate if our findings generalise to non-European populations; biological mechanisms could be expected to be largely consistent across populations but context-specific (e.g., social) mechanisms could lead to geographical heterogeneity.”

5. Could differences between UK Biobank and HUNT be due to differences to design?

We have added this as a possible explanation for the heterogeneity between the studies for the biomarker analyses in the discussion.

“Additional explanations could relate to differences in biomarker measurement between studies (e.g. measuring LDL-C directly or using the Friedewald formula, differences in fasting level before samples were taken), selection bias ⁶⁴ or differences between the cohorts in terms of recruitment and participation.”

6. Are both the comparisons between the sibling and population estimates for cancer and coronary heart disease in the expected directions?

We believe the reviewer is referring to the estimates in Figure 2. If so, the sibling and population estimates were highly consistent in terms of directionality and magnitude for both cancer and coronary heart disease.

[1] Hernán MA, Sauer BC, Hernández-Díaz S, Platt R, Shrier I: Specifying a target trial prevents immortal time bias and other self-inflicted injuries in observational analyses. J Clin Epidemiol 2016, 79:70-75.

Reviewer #2 (Recommendations for the authors):

How likely is it that the earlier MR results are misleading? Biased by demographics etc? It relates to the novelty of the paper because the MR association has been shown before multiple times.

As the reviewer notes, several previous MR studies have provided evidence of the height cancer and height CHD relationships using data from unrelated individuals. Similarly previous MR studies of unrelated individuals also provided consistent evidence that taller height and lower BMI increase educational attainment. However, a recent within-sibship MR study (Brumpton et al. 2020 Nature Communications) found that the height/BMI on education effects completely attenuate in within-sibship models suggesting that the previous MR estimates from unrelated individuals were biased. Therefore, it was certainly possible that the previous height cancer/CHD MR estimates from unrelated individuals could also have been biased.

Furthermore, previous studies in eLife (Berg et al., Sohail et al. 2019) also reported evidence that population stratification inflated estimates of polygenic adaptation on taller height, suggesting that height may be a phenotype particularly susceptible to bias from demographic factors. This further highlights the importance of within-sibship studies for height analyses.

What is the rationale for doing the biomarker analysis? Would it not be more informative to investigate if they mediate the effect from height on CHD?

We included analyses with biomarkers relevant to cancer and coronary heart disease to determine if height-biomarker estimates were consistent with the height-disease estimates. Biomarkers are continuous so analyses would likely have higher statistical power than for the binary disease outcomes. Statistical power was limited to perform multivariable MR analyses to investigate if these biomarkers mediate the effects of height.

Table 1 should be sample characteristics in HUNT and UKB.

We have added a table of sample characteristics for UKB and HUNT as suggested in Table 1.

What about sex stratifications? Is it possible to do for same-sex sib pairs? Is it of importance in the population at large regardless of sib ship pairs? For the traits used here it should matter.

Sex stratification would be possible in theory but would substantially reduce the sample size and statistical power because it would remove all families without same-sex siblings (e.g. a sibling pair with 1 male and 1 female) and then split the remaining dataset into two. Therefore, in practice with available data, we believe that a sex-stratified analysis would be far too underpowered to detect heterogeneity.

All figures and tables should contain information so that they are stand alone items.

We have been through the manuscript and ensured that table and figure descriptions provide enough detail. In particular, we have added descriptions for Tables 1 (new) and 2 and all of the supplemental tables.

Author details

1. Laurence J Howe

   1. Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, United Kingdom
   2. Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, United Kingdom
   3. K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway

   Contribution

   Conceptualization, Formal analysis, Investigation, Methodology, Software, Writing – original draft, Writing – review and editing

   For correspondence

   laurence.howe@bristol.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2819-9686

2. Ben Brumpton

   1. K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
   2. HUNT Research Center, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, Norway
   3. Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway

   Contribution

   Data curation, Funding acquisition, Supervision, Writing – review and editing

   Competing interests

   No competing interests declared

3. Humaira Rasheed

   1. Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, United Kingdom
   2. K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway

   Contribution

   Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3331-5864

4. Bjørn Olav Åsvold

   1. K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
   2. Department of Endocrinology, Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway

   Contribution

   Data curation, Funding acquisition, Writing – review and editing

   Competing interests

   No competing interests declared

5. George Davey Smith

   1. Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, United Kingdom
   2. Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, United Kingdom

   Contribution

   Funding acquisition, Methodology, Supervision, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1407-8314

6. Neil M Davies

   1. Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, United Kingdom
   2. Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, United Kingdom
   3. K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway

   Contribution

   Funding acquisition, Investigation, Methodology, Supervision, Writing – review and editing

   For correspondence

   neil.davies@bristol.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2460-0508

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