Next Article in Journal
Genomic Diversity of Listeria monocytogenes Isolated from Clinical and Non-Clinical Samples in Chile
Next Article in Special Issue
Pervasive Modulation of Obesity Risk by the Environment and Genomic Background
Previous Article in Journal
A Model Stacking Framework for Identifying DNA Binding Proteins by Orchestrating Multi-View Features and Classifiers
Previous Article in Special Issue
Evolutionary and Medical Consequences of Archaic Introgression into Modern Human Genomes
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Genes
Volume 9
Issue 8
10.3390/genes9080395
genes-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Mitochondrial DNA Haplogroups and Breast Cancer Risk Factors in the Avon Longitudinal Study of Parents and Children (ALSPAC)

by
Vivienne Riley
1,
A Mesut Erzurumluoglu
2,3,
Santiago Rodriguez
3 and
Carolina Bonilla
3,4,5,*
1
MSc Genomic Medicine Programme, G7, College House, St Luke’s Campus University of Exeter, Exeter, Devon EX2 4TE, UK
2
Genetic Epidemiology Group, Department of Health Sciences, University of Leicester, Leicester LE1 7RH, UK
3
MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
4
Integrative Cancer Epidemiology Program, Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
5
Departamento de Medicina Preventiva, Faculdade de Medicina, Universidade de São Paulo, São Paulo 01246-903, Brazil
*
Author to whom correspondence should be addressed.
Genes 2018, 9(8), 395; https://doi.org/10.3390/genes9080395
Submission received: 9 June 2018 / Revised: 25 July 2018 / Accepted: 27 July 2018 / Published: 1 August 2018
(This article belongs to the Special Issue Evolutionary Medicine)
Versions Notes

Abstract

:
The relationship between mitochondrial DNA (mtDNA) and breast cancer has been frequently examined, particularly in European populations. However, studies reporting associations between mtDNA haplogroups and breast cancer risk have had a few shortcomings including small sample sizes, failure to account for population stratification and performing inadequate statistical tests. In this study we investigated the association of mtDNA haplogroups of European origin with several breast cancer risk factors in mothers and children of the Avon Longitudinal Study of Parents and Children (ALSPAC), a birth cohort that enrolled over 14,000 pregnant women in the Southwest region of the UK. Risk factor data were obtained from questionnaires, clinic visits and blood measurements. Information on over 40 independent breast cancer risk factor-related variables was available for up to 7781 mothers and children with mtDNA haplogroup data in ALSPAC. Linear and logistic regression models adjusted for age, sex and population stratification principal components were evaluated. After correction for multiple testing we found no evidence of association of European mtDNA haplogroups with any of the breast cancer risk factors analysed. Mitochondrial DNA haplogroups are unlikely to underlie susceptibility to breast cancer that occurs via the risk factors examined in this study of a population of European ancestry.

1. Introduction

The relationship between mitochondrial DNA (mtDNA) and breast cancer has been frequently explored. Carcinogenesis has been associated with oxidative stress, with mitochondria acting as a major source of production of reactive oxygen species (ROS) [1]. Additionally, the lack of protective histones and a limited capacity for DNA repair [2,3] has meant that the mitochondrial genome is particularly susceptible to damage by ROS, which in turn could affect the mitochondrial role in energy metabolism, apoptosis and aging [4]. Most publications have focused on somatic mutations in mtDNA. However, germline variants that subtly affect mitochondrial functioning may also lead to a build-up of ROS, resulting in an elevated cancer risk [4], and their study has thus become more frequent [5].
Since mtDNA is maternally inherited, it has been suggested that it could underlie the observation that having a mother diagnosed with breast cancer increases a woman’s risk of the disease.
During evolution mtDNA mutations have segregated and clustered in groups of related haplotypes or haplogroups (also referred to as clades) that differ prominently in frequencies across continents [6,7]. The geographic patterning of mtDNA lineages was attributed to founder effects although natural selection has been recently postulated as a more probable cause. Adaptation to climate and nutrition may have been the environmental selective factors to drive clade differences by region given that haplogroups exhibit diverse metabolic capacities [6,8]. Thus, lineages that are advantageous in a particular environment could become maladaptive when the environment changes contributing to the development of disease.
Nine major mtDNA haplogroups have been identified in Europeans (namely H, I, J, K, T, U, V, W, X). Haplogroup H is the most frequent, though the frequencies of individual clades vary within Europe [9].
The association of mtDNA variants and haplogroups with cancer and other diseases has been extensively explored in the European population. For instance, there are reports on breast cancer [10,11], prostate cancer [12], meningococcal disease [13], neurological diseases [14], type 2 diabetes mellitus [15], infertility [16], obesity [17], acquired immunodeficiency syndrome (AIDS) progression [18], stroke [19], and osteoarthritis [20]. Results have been inconsistent across studies of the same disease. This has been attributed to a number of problems relating to study design and data analysis, such as population stratification, genotyping error, small sample sizes or inadequate statistical approaches [21,22,23,24]. The case of breast cancer studies is particularly noteworthy [23].
In order to elucidate the putative role of mtDNA in breast cancer we investigated the distribution of European mtDNA haplogroups across a range of well-known and possible risk factors for breast cancer in mothers and children of the Avon Longitudinal Study of Parents and Children (ALSPAC). If mtDNA variation plays a role in breast cancer susceptibility it may do so via risk factors that lie in the causal pathway to disease. Thus, we might be able to detect an association of mtDNA haplogroups with these exposures with less bias in a large and substantially homogeneous cohort like ALSPAC.

2. Materials and Methods

2.1. Avon Longitudinal Study of Parents and Children (ALSPAC)

The Avon Longitudinal Study of Parents and Children (ALSPAC) is a prospective, population-based birth cohort study that recruited 14,541 pregnant women residing in Avon County, United Kingdom, with an expected delivery date between 1 April 1991 and 31 December 1992. From these initial pregnancies, there were a total of 14,676 fetuses, resulting in 14,062 live births and 13,988 children who were alive at one year of age. When the oldest children were approximately seven years of age, an attempt was made to bolster the initial sample with eligible cases who had failed to join the study originally. The total sample size for analyses using any data collected after the age of seven is therefore 15,247 pregnancies, resulting in 15,458 fetuses, of which 14,775 were live births and 14,701 were alive at one year of age. Data were gathered through self-completed questionnaires or assessment at research clinics at regular intervals. The study is described in detail elsewhere [25,26] (http://www.bristol.ac.uk/alspac/). Hormone level measurements (i.e., circulating sex hormones, insulin growth factors and insulin growth factor binding proteins) were reported earlier [27,28,29,30,31]. Please note that the study website contains details of all the data that are available through a fully searchable data dictionary: http://www.bris.ac.uk/alspac/researchers/data-access/data-dictionary/.
Ethical approval for the study was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees (http://www.bristol.ac.uk/alspac/researchers/research-ethics/). Written informed consent was obtained from all participants in the study.

2.2. Breast Cancer Risk Factors

The selection of breast cancer risk factors was based on worldwide research data published by the World Cancer Research Fund [32] and Cancer Research UK [33].
Lifestyle factors showing robust evidence for increasing the risk of breast cancer have been identified in premenopausal women, postmenopausal women, or both. Risk factors for postmenopausal breast cancer are: overweight or obesity and a greater weight gain in adulthood. Having a greater birth weight is a risk factor for premenopausal breast cancer. Alcohol intake and a greater linear growth (marked by adult attained height) have been associated with both pre- and postmenopausal breast cancer. Conversely, factors that reduce the risk of breast cancer include increased physical activity, and breastfeeding, whereas being overweight or obese acts as a protective factor in premenopausal women.
Other established risk factors are older age, White ethnicity, family history of breast cancer, a prior diagnosis of cancer, early menarche, late natural menopause, nulliparity, first pregnancy after the age of 30, hormone replacement therapy, use of oral contraceptives, high bone mineral density, diabetes mellitus and exposure to radiation such as X-rays.
Possible breast cancer risk factors include smoking and night shift work. In contrast, a healthy diet and regularly taking aspirin or other non-steroidal anti-inflammatory drugs are considered protective factors.
We also tested women having had reproductive surgery (hysterectomy and/or oophorectomy), and using non-oral hormonal contraceptives, which may affect breast cancer risk by altering hormone concentrations, and serum levels of sex hormone binding globulin (SHBG), testosterone, androstenedione, dehydroepiandrosterone-sulfate (DHEAS), insulin like growth factor I (IGF-I), insulin-like growth factor II (IGF-II) and insulin-like growth factor binding protein 3 (IGFBP-3).
More detailed information on the association of the risk factors considered with breast cancer can be found in the literature [34,35,36,37,38].
Data on most risk and protective factors were available in ALSPAC from mothers, children or both.

2.3. Variables Analyzed

Details on the continuous and categorical variables examined in mothers and children are given in Tables S1 and S2 showing the complete set of ALSPAC participants of European ancestry. Data were obtained from questionnaires answered by the subject or by the subject’s mother during childhood, as well as from clinic visits at different time points. We limited the analysis to variables measured approximately every two years in the children.
In some cases, usually when numbers were low, we created new variables that reflected whether the individual had ever experienced the activity or shown the trait of interest by combining the different instances where it was assessed. We did this for smoking, having diabetes, having had cancer, having undergone a hysterectomy and/or an oophorectomy, doing night shift work, taking oral contraceptives, using non-oral hormonal contraceptives, receiving hormone replacement therapy, taking aspirin, being a biological parent, undertaking physical activity and getting X-rays. The variable age of menarche in the children was determined by integrating all the information provided by the mother and the child [39].

2.4. Mitochondrial DNA Genotyping

Genotyping methods for mitochondrial and nuclear DNA polymorphisms in ALSPAC have been previously described [40]. Haplogroup assignment was performed using HaploGrep [41]. Mitochondrial DNA haplogroups of mothers were inferred from those of their children in view of the maternal inheritance of mtDNA.
Samples with a quality score of more than 80% were included in the analysis (238 samples were excluded, Table S3). Additionally, we ran the analysis using a quality score cut-off point of >90%.
We grouped the clades as follows: European = H (H + V + subclade R0), J, K, T, U, other European (I + W + X + subclades N1, R1, R3); South Asian = M + subclade R5; East/Southeast Asian = A + C + D; and African = L. The ‘other European’ group consisted of haplogroups that were present in less than 3% of the sample.
We restricted our analysis to all individuals who were of European genomic ancestry (as detected by a multidimensional scaling analysis seeded with haplotype map (HapMap)2 individuals) or self-identified as White (if data on genomic ancestry was missing) and carried a European mtDNA haplogroup. There was information on 7781 mtDNA haplogroups in our working dataset.

2.5. Statistical Analysis

We used linear regression to investigate the association of continuous variables with mtDNA haplogroups and chi-squared tests to examine differences in categorical variables. Adjustment for confounders was carried out using linear and logistic regression models with continuous and categorical variables, respectively. When categorical variables exhibited more than two ordered categories ordinal logistic regression was run, also adjusted for confounders. Confounders introduced in the models were age, sex, gestational age and the top 10 principal components accounting for population stratification, where appropriate.
We checked that the residuals of the linear regression of each continuous variable on mtDNA haplogroups were normally distributed. Only residuals for DHEAS levels in children showed a markedly non-normal distribution, and therefore the variable was natural log-transformed [42].
Similarly to Howe et al. [40], we used pairwise correlation to determine the number of independent variables to account for when applying the Bonferroni correction for multiple testing [43]. Polychoric correlation was used with binary and ordinal variables. Variables showing a correlation coefficient of 0.8 and above were considered non-independent (data not shown). There were 43 independent variables (out of 59) in the mothers and 48 independent variables (out of 86) in the children, therefore the multiple testing adjusted p-value cut-off was 0.001 in both cases.
We tested whether there was residual population stratification due to mtDNA clustering within our working dataset of European/White individuals by plotting the top two principal components by mitochondrial lineage as reported by Erzurumluoglu et al. for Y chromosome haplogroups in ALSPAC [44].
All analyses were performed with the statistical package Stata 14 (StataCorp, College Station, TX, USA).
Statistical power was calculated with mitPower using binary variables, as this approach has not been developed for continuous or ordinal variables yet (http://bioinformatics.cesga.es/mitpower/) [45].

3. Results

Mitochondrial DNA haplogroups found in mothers and children of ALSPAC that were used in this study are shown in Table 1. For a more detailed haplogroup report see Table S3. The most frequent haplogroup was HV, representing almost 50% of the sample (49.3%), in agreement with the probable haplogroup composition of the UK estimated in a recent mtDNA study [46].
It is interesting to note, although not completely unexpected [47], that despite selecting individuals of European genomic ancestry and White ethnicity around 1% of them carried non-European mtDNA haplogroups (i.e., A, C, D, L, M, N) (Table S3), and were therefore excluded from the analysis. This can also be observed in individuals with an mtDNA quality score over 90% (Table S3). We uncovered no evidence of residual population stratification in the group of participants who carried a European mtDNA haplogroup (Figures S1 and S2).
No differences were identified in the distribution of mtDNA haplogroups by sex of the child (p = 0.15).
In the unadjusted analyses sample sizes ranged from 143 to 7629 in the mothers, and from 142 to 7373 in the children, whereas sample sizes for the adjusted analyses were between 100 and 4863, and between 137 and 6838, respectively.
Given the sample sizes available for the binary variables, the haplogroup frequencies and the number of haplogroups included in the analysis, we had 80% power to detect odds ratios (OR) of ~1.2 (ever smoked) to 1.7 (being a biological parent) at an α level of 0.05 if considering HV as the risk haplogroup. Under the same conditions, at the significance level corrected for multiple testing (p ≤ 0.001), those ORs become ~1.3 to 2.1. This calculation excludes the variable ‘having diabetes’ in children as there were only 26 subjects with the disease and mtDNA data in ALSPAC.

3.1. Association between Mitochondrial DNA Haplogroups and Breast Cancer Risk Factors in ALSPAC Mothers

In the unadjusted analysis 10 nominal associations of mtDNA haplogroups with breast cancer risk factors were found (p ≤ 0.05). Most of these associations involved body composition variables such as body mass index (BMI), weight, height and bone mineral density (Table S4). Seven associations were apparent after correction for age (or gestational age in the case of IGF and sex hormone measurements in pregnancy) and the top 10 principal components (Table 2). However, no strong evidence of association was present after applying a Bonferroni correction for multiple testing.

3.2. Association between Mitochondrial DNA Haplogroups and Breast Cancer Risk Factors in ALSPAC Children

Likewise, among the children no associations between mtDNA haplogroups and breast cancer risk factors were uncovered after multiple testing correction. All three associations showing a p ≤ 0.05 in unadjusted models were related to BMI and height (Table S5) and two of these were also detected after controlling for age, sex and the top 10 principal components (Table 3).
Similar results overall were obtained when using the more stringent quality score threshold of >90% (Table S6).

4. Discussion

In this study we have not found evidence that major mtDNA haplogroups underlie differences in breast cancer risk factor distribution. This finding in some way supports previous research showing that mtDNA lineages are not associated with breast cancer risk [23,48], although it is still possible that mtDNA variation directly affects cancer development without going through any of the risk factors investigated here. However, we did not observe any associations with cancer-specific traits available in the cohort, such as any cancer diagnosis in the mother or a breast cancer diagnosis in her biological mother. In addition, no association was evident with having had a mammogram either. Conversely, if mtDNA variation plays a role in breast cancer onset via any of the tested exposures it might represent a small increase in risk. Nevertheless, large scale case-control studies are needed to properly examine the association of mtDNA haplogroups with breast cancer.
We tried to reduce sources of bias such as small sample sizes and population stratification by using the ALSPAC cohort, where we had over 7700 individuals with mtDNA haplogroup data who were of European genomic ancestry or self-identified as being of White ethnicity. In addition, we ran regression models adjusted for principal components that reflect population structure in the South West region of the UK. Because ALSPAC has collected such a comprehensive set of phenotypes we were also able to examine most of the established and possible breast cancer risk factors.
Among the limitations of our study, the fact that a handful of traits examined had a low number of observations, in particular the hormone measures, decreased our confidence in these results. The minimum difference detectable with 80% power was an OR of 1.2 for the binary variables, as estimated using mitPower. In addition, some of the derived variables grouped all instances of a phenotype together, which may have prevented us from noticing an effect that depended on the frequency of such a phenotype.
Pre-Bonferroni correction, we detected associations of mtDNA haplogroups with body composition variables (mainly BMI, weight, height), which were seen in mothers as well as children and at various instances across the lifetime. A few earlier studies have shown an association of mtDNA haplogroups with obesity and obesity-related traits [17,49,50], while others reported no evidence of association [51,52]. In our analysis these associations have not survived multiple testing correction, so it is possible that they have arisen by chance or are the result of persisting population stratification (as different subsamples of mothers and children responded to questionnaires and were involved in the clinics), given that height and BMI are considerably structured across Europe [53]. On the other hand, we did not find any discernible stratification beyond what was accounted for by the use of genome-wide principal components, in agreement with a recent study that examined Y chromosome haplogroups in ALSPAC and showed that clustering by male lineage did not affect the association between autosomal single nucleotide polymorphisms (SNPs) and BMI [44].
Whilst we did not have a replication cohort to confirm our findings, the analysis was run in two groups of individuals, albeit related, each with a different set of phenotypes. Further investigation is needed to strengthen the results presented here; however, these could prove useful to generate hypotheses for future, more powerful studies.

5. Conclusions

Well-established and possible breast cancer risk factors were not found to be associated with mtDNA haplogroups in ALSPAC, a cohort of predominantly European ancestry. This study can serve as the basis for a further detailed analysis of the influence of mtDNA variation on nutritional, anthropometric and lifestyle exposures that underlie the susceptibility to breast cancer and other cancer types.

Supplementary Materials

The following are available online at https://www.mdpi.com/2073-4425/9/8/395/s1, Table S1: Breast cancer risk factors in ALSPAC mothers of European ancestry, Table S2: Breast cancer risk factors in ALSPAC children of European ancestry, Table S3: Distribution of major mitochondrial DNA haplogroups and subgroups in ALSPAC mothers and children of European descent based on HaploGrep quality scores, Table S4: Breast cancer risk factors and major mtDNA haplogroups in ALSPAC mothers of European ancestry, unadjusted analysis, Table S5: Breast cancer risk factors and major mtDNA haplogroups in ALSPAC children of European ancestry, unadjusted analysis, Table S6: p-values obtained in the adjusted analysis using a quality score threshold of >90% compared to >80%, Figure S1: Top two principal components (PCs) by mtDNA haplogroup in ALSPAC mothers, Figure S2: Top two principal components (PCs) by mtDNA haplogroup in ALSPAC children.

Author Contributions

C.B. and S.R. conceived the study. A.M.E. generated the mtDNA haplogroup data. V.R. and C.B. analyzed the data. C.B. wrote the paper. All authors interpreted the data, read and approved the final manuscript.

Funding

The UK Medical Research Council and the Wellcome Trust (grant 102215/2/13/2) and the University of Bristol provide core support for ALSPAC. A comprehensive list of grant funding is available on the ALSPAC website. This research was specifically funded by the Medical Research Council (grant MR/K002767/1, awarded to Santiago Rodriguez). GWAS data were generated by Sample Logistics and Genotyping Facilities at Wellcome Sanger Institute and LabCorp (Laboratory Corporation of America) using support from 23andMe. C.B. was supported by Cancer Research UK (grant C18281/A19169) and by the MSc/PgDip/PgCert Genomic Medicine program at the University of Exeter. The Integrative Epidemiology Unit (IEU) is supported by the Medical Research Council and the University of Bristol (G0600705, MC_UU_12013/19), and the Integrative Cancer Epidemiology Programme is supported by a Cancer Research UK program grant (C18281/A19169).

Acknowledgments

We are extremely grateful to all the families who took part in the ALSPAC study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses.

Conflicts of Interest

The authors declare that they have no conflict of interests.

Data Availability

Data used for this submission can be accessed after submitting an application to and receiving approval from the ALSPAC Executive ([email protected]).

References

  1. Grzybowska-Szatkowska, L.; Slaska, B. Mitochondrial DNA and carcinogenesis (Review). Mol. Med. Rep. 2012, 6, 923–930. [Google Scholar] [CrossRef] [PubMed]
  2. Mandavilli, B.S.; Santos, J.H.; Van Houten, B. Mitochondrial DNA repair and aging. Mutat. Res. 2002, 509, 127–151. [Google Scholar] [CrossRef]
  3. Croteau, D.L.; Stierum, R.H.; Bohr, V.A. Mitochondrial DNA repair pathways. Mutat. Res. 1999, 434, 137–148. [Google Scholar] [CrossRef]
  4. Rohan, T.E.; Wong, L.J.; Wang, T.; Haines, J.; Kabat, G.C. Do alterations in mitochondrial DNA play a role in breast carcinogenesis? J. Oncol. 2010, 2010, 604304. [Google Scholar] [CrossRef] [PubMed]
  5. Schon, E.A.; DiMauro, S.; Hirano, M. Human mitochondrial DNA: Roles of inherited and somatic mutations. Nat. Rev. Genet. 2012, 13, 878–890. [Google Scholar] [CrossRef] [PubMed]
  6. Mishmar, D.; Ruiz-Pesini, E.; Golik, P.; Macaulay, V.; Clark, A.G.; Hosseini, S.; Brandon, M.; Easley, K.; Chen, E.; Brown, M.D.; et al. Natural selection shaped regional mtDNA variation in humans. Proc. Natl. Acad. Sci. USA 2003, 100, 171–176. [Google Scholar] [CrossRef] [PubMed]
  7. Van Oven, M.; Kayser, M. Updated comprehensive phylogenetic tree of global human mitochondrial DNA variation. Hum. Mutat. 2009, 30, E386–E394. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  8. Kenney, M.C.; Chwa, M.; Atilano, S.R.; Falatoonzadeh, P.; Ramirez, C.; Malik, D.; Tarek, M.; del Carpio, J.C.; Nesburn, A.B.; Boyer, D.S.; et al. Molecular and bioenergetic differences between cells with African versus European inherited mitochondrial DNA haplogroups: Implications for population susceptibility to diseases. Biochim. Biophys. Acta Mol. Basis Dis. 2014, 1842, 208–219. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  9. Richards, M.; Macaulay, V.; Torroni, A.; Bandelt, H.-J. In search of geographical patterns in European mitochondrial DNA. Am. J. Hum. Genet. 2002, 71, 1168–1174. [Google Scholar] [CrossRef] [PubMed]
  10. Blein, S.; Bardel, C.; Danjean, V.; McGuffog, L.; Healey, S.; Barrowdale, D.; Lee, A.; Dennis, J.; Kuchenbaecker, K.B.; Soucy, P.; et al. An original phylogenetic approach identified mitochondrial haplogroup T1a1 as inversely associated with breast cancer risk in BRCA2 mutation carriers. Breast Cancer Res. 2015, 17, 61. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  11. Tommasi, S.; Favia, P.; Weigl, S.; Bianco, A.; Pilato, B.; Russo, L.; Paradiso, A.; Petruzzella, V. Mitochondrial DNA variants and risk of familial breast cancer: An exploratory study. Int. J. Oncol. 2014, 44, 1691–1698. [Google Scholar] [CrossRef] [PubMed]
  12. Fachal, L.; Gómez-Caamaño, A.; Álvarez Iglesias, V.; Gómez Carballa, A.; Calvo, P.; Salas, A.; Vega, A. No association between typical European mitochondrial variation and prostate cancer risk in a Spanish cohort. J. Hum. Genet. 2014, 59, 411–414. [Google Scholar] [CrossRef] [PubMed]
  13. Salas, A.; Fachal, L.; Marcos-Alonso, S.; Vega, A.; Martinón-Torres, F. Investigating the role of mitochondrial haplogroups in genetic predisposition to meningococcal disease. PLoS ONE 2009, 4, e8347. [Google Scholar] [CrossRef] [PubMed]
  14. Fachal, L.; Mosquera-Miguel, A.; Pastor, P.; Ortega-Cubero, S.; Lorenzo, E.; Oterino-Durán, A.; Toriello, M.; Quintáns, B.; Camiña-Tato, M.; Sesar, A.; et al. No evidence of association between common European mitochondrial DNA variants in Alzheimer, Parkinson, and migraine in the Spanish population. Am. J. Med. Genet. Part B Neuropsychiatr. Genet. 2014, 168, 54–65. [Google Scholar] [CrossRef] [PubMed]
  15. Chinnery, P.F.; Mowbray, C.; Patel, S.K.; Elson, J.L.; Sampson, M.; Hitman, G.A.; McCarthy, M.I.; Hattersley, A.T.; Walker, M. Mitochondrial DNA haplogroups and type 2 diabetes: A study of 897 cases and 1010 controls. J. Med. Genet. 2007, 44, e80. [Google Scholar] [CrossRef] [PubMed]
  16. Ruiz-Pesini, E.; Lapeña, A.C.; Díez-Sánchez, C.; Pérez-Martos, A.; Montoya, J.; Alvarez, E.; Díaz, M.; Urriés, A.; Montoro, L.; López-Pérez, M.J.; et al. Human mtDNA haplogroups associated with high or reduced spermatozoa motility. Am. J. Hum. Genet. 2000, 67, 682–696. [Google Scholar] [CrossRef] [PubMed]
  17. Nardelli, C.; Labruna, G.; Liguori, R.; Mazzaccara, C.; Ferrigno, M.; Capobianco, V.; Pezzuti, M.; Castaldo, G.; Farinaro, E.; Contaldo, F.; et al. Haplogroup T is an obesity risk factor: Mitochondrial DNA haplotyping in a morbid obese population from southern Italy. BioMed Res. Int. 2013, 2013, 631082. [Google Scholar] [CrossRef] [PubMed]
  18. Hendrickson, S.L.; Hutcheson, H.B.; Ruiz-Pesini, E.; Poole, J.C.; Lautenberger, J.; Sezgin, E.; Kingsley, L.; Goedert, J.J.; Vlahov, D.; Donfield, S.; et al. Mitochondrial DNA haplogroups influence AIDS progression. AIDS 2008, 22, 2429–2439. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  19. Chinnery, P.F.; Elliott, H.R.; Syed, A.; Rothwell, P.M. Mitochondrial DNA haplogroups and risk of transient ischaemic attack and ischaemic stroke: A genetic association study. Lancet Neurol. 2010, 9, 498–503. [Google Scholar] [CrossRef]
  20. Rego, I.; Fernández-Moreno, M.; Fernández-López, C.; Gómez-Reino, J.J.; González, A.; Arenas, J.; Blanco, F.J. Role of European mitochondrial DNA haplogroups in the prevalence of hip osteoarthritis in Galicia, Northern Spain. Ann. Rheum. Dis. 2010, 69, 210–213. [Google Scholar] [CrossRef] [PubMed]
  21. Pereira, L.; Gonçalves, J.; Goios, A.; Rocha, T.; Amorim, A. Human mtDNA haplogroups and reduced male fertility: Real association or hidden population substructuring. Int. J. Androl. 2005, 28, 241–247. [Google Scholar] [CrossRef] [PubMed]
  22. Bandelt, H.J. Misanalysis gave false association of mtDNA mutations with infertility. Int. J. Androl. 2008, 31, 450–453. [Google Scholar] [CrossRef] [PubMed]
  23. Salas, A.; García-Magariños, M.; Logan, I.; Bandelt, H.-J. The saga of the many studies wrongly associating mitochondrial DNA with breast cancer. BMC Cancer 2014, 14, 659. [Google Scholar] [CrossRef] [PubMed]
  24. Salas, A.; Elson, J.L. Mitochondrial DNA as a risk factor for false positives in case-control association studies. J. Genet. Genom. 2015, 42, 169–172. [Google Scholar] [CrossRef] [PubMed]
  25. Fraser, A.; Macdonald-Wallis, C.; Tilling, K.; Boyd, A.; Golding, J.; Davey Smith, G.; Henderson, J.; Macleod, J.; Molloy, L.; Ness, A.; et al. Cohort Profile: The avon longitudinal study of parents and children: ALSPAC mothers cohort. Int. J. Epidemiol. 2013, 42, 97–110. [Google Scholar] [CrossRef] [PubMed]
  26. Boyd, A.; Golding, J.; Macleod, J.; Lawlor, D.A.; Fraser, A.; Henderson, J.; Molloy, L.; Ness, A.; Ring, S.; Davey Smith, G. Cohort Profile: The ‘children of the 90s’—The index offspring of the Avon Longitudinal Study of Parents and Children. Int. J. Epidemiol. 2013, 42, 111–127. [Google Scholar] [CrossRef] [PubMed]
  27. Ong, K.; Kratzsch, J.; Kiess, W.; Costello, M.; Scott, C.; Dunger, D. Size at birth and cord blood levels of insulin, insulin-like growth factor I (IGF-I), IGF-II, IGF-Binding Protein-1 (IGFBP-1), IGFBP-3, and the soluble IGF-II/Mannose-6-Phosphate Receptor in term human infants. J. Cinical Endocrinol. Metab. 2000, 85, 4266–4269. [Google Scholar]
  28. Gunnell, D.; Oliver, S.E.; Donovan, J.L.; Peters, T.J.; Gillatt, D.; Persad, R.; Hamdy, F.C.; Neal, D.E.; Holly, J.M.P. Do height-related variations in insulin-like growth factors underlie the associations of stature with adult chronic disease? J. Clin. Endocrinol. Metab. 2004, 89, 213–218. [Google Scholar] [CrossRef] [PubMed]
  29. Jeffreys, M.; Northstone, K.; Holly, J.; Emmett, P.; Gunnell, D. Levels of insulin-like growth factor during pregnancy and maternal cancer risk: A nested case-control study. Cancer Causes Control 2011, 22, 945–953. [Google Scholar] [CrossRef] [PubMed]
  30. Shaheen, S.O.; Hines, M.; Newson, R.B.; Wheeler, M.; Herrick, D.R.M.; Strachan, D.P.; Jones, R.W.; Burney, P.G.J.; Henderson, A.J. Maternal testosterone in pregnancy and atopic outcomes in childhood. Allergy 2007, 62, 25–32. [Google Scholar] [CrossRef] [PubMed]
  31. Maisonet, M.; Calafat, A.M.; Marcus, M.; Jaakkola, J.J.K.; Lashen, H. Prenatal exposure to perfluoroalkyl acids and serum testosterone concentrations at 15 years of age in female ALSPAC study participants. Environ. Health Perspect. 2015, 123, 1325–1330. [Google Scholar] [CrossRef] [PubMed]
  32. World Cancer Research Fund; American Institute for Cancer Research. Diet, Nutrition, Physical Activity and Breast Cancer; World Cancer Research Fund: London, UK, 2017. [Google Scholar]
  33. Cancer Research UK, Breast Cancer. Available online: www.cancerresearchuk.org (accessed on 1 July 2017).
  34. De Bruijn, K.M.J.; Arends, L.R.; Hansen, B.E.; Leeflang, S.; Ruiter, R.; van Eijck, C.H.J. Systematic review and meta-analysis of the association between diabetes mellitus and incidence and mortality in breast and colorectal cancer. Br. J. Surg. 2013, 100, 1421–1429. [Google Scholar] [CrossRef] [PubMed]
  35. Rojas, K.; Stuckey, A. Breast cancer epidemiology and risk factors. Clin. Obstet. Gynecol. 2016, 59, 651–672. [Google Scholar] [CrossRef] [PubMed]
  36. Terry, M.B.; Gammon, M.D.; Zhang, F.F.; Tawfik, H.; Teitelbaum, S.L.; Britton, J.A.; Subbaramaiah, K.; Dannenberberg, A.J.; Neugut, A.I. Association of frequency and duration of aspirin use and hormone receptor status with breast cancer risk. JAMA 2004, 291, 2433–2440. [Google Scholar] [CrossRef] [PubMed]
  37. Wang, F.; Yeung, K.L.; Chan, W.C.; Kwok, C.C.H.; Leung, S.L.; Wu, C.; Chan, E.Y.Y.; Yu, I.T.S.; Yang, X.R.; Tse, L.A. A meta-analysis on dose–response relationship between night shift work and the risk of breast cancer. Ann. Oncol. 2013, 24, 2724–2732. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  38. Fraenkel, M.; Novack, V.; Liel, Y.; Koretz, M.; Siris, E.; Norton, L.; Shafat, T.; Shany, S.; Geffen, D.B. Association between bone mineral density and incidence of breast cancer. PLoS ONE 2013, 8, e70980. [Google Scholar] [CrossRef]
  39. Sequeira, M.-E.; Lewis, S.J.; Bonilla, C.; Smith, G.D.; Joinson, C. Association of timing of menarche with depressive symptoms and depression in adolescence: Mendelian randomisation study. Br. J. Psychiatry 2017, 210, 39–46. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  40. Howe, L.J.; Erzurumluoglu, A.M.; Davey Smith, G.; Rodriguez, S.; Stergiakouli, E. Y Chromosome, mitochondrial DNA and childhood behavioural traits. Sci. Rep. 2017, 7, 11655. [Google Scholar] [CrossRef] [PubMed]
  41. Kloss-Brandstätter, A.; Pacher, D.; Schönherr, S.; Weissensteiner, H.; Binna, R.; Specht, G.; Kronenberg, F. HaploGrep: A fast and reliable algorithm for automatic classification of mitochondrial DNA haplogroups. Hum. Mutat. 2011, 32, 25–32. [Google Scholar] [CrossRef] [PubMed]
  42. Kirkwood, B.R.; Sterne, J.A.C. Medical Statistics, 2nd ed.; Blackwell Science Ltd.: Malden, MA, USA, 2007; ISBN 978-0-86542-871-3. [Google Scholar]
  43. Holm, S. A simple sequentially rejective multiple test procedure. Scand. J. Stat. 1979, 6, 65–70. [Google Scholar] [CrossRef]
  44. Erzurumluoglu, A.; Baird, D.; Richardson, T.; Timpson, N.; Rodriguez, S. Using Y-chromosomal haplogroups in genetic association studies and suggested implications. Genes 2018, 9, 45. [Google Scholar] [CrossRef] [PubMed]
  45. Pardo-Seco, J.; Amigo, J.; González-Manteiga, W.; Salas, A. A generalized model to estimate the statistical power in mitochondrial disease studies involving 2×k tables. PLoS ONE 2013, 8, e73567. [Google Scholar] [CrossRef] [PubMed]
  46. Røyrvik, E.C.; Burgstaller, J.P.; Johnston, I.G. mtDNA diversity in human populations highlights the merit of haplotype matching in gene therapies. Mol. Hum. Reprod. 2016, 22, 809–817. [Google Scholar] [CrossRef] [PubMed]
  47. Emery, L.S.; Magnaye, K.M.; Bigham, A.W.; Akey, J.M.; Bamshad, M.J. Estimates of continental ancestry vary widely among individuals with the same mtDNA haplogroup. Am. J. Hum. Genet. 2015, 96, 183–193. [Google Scholar] [CrossRef] [PubMed]
  48. Mosquera-Miguel, A.; Alvarez-Iglesias, V.; Carracedo, A.; Salas, A.; Vega, A.; Milne, R.; de León, A.C.; Benitez, J. Is mitochondrial DNA variation associated with sporadic breast cancer risk? Cancer Res. 2008, 68, 623–625. [Google Scholar] [CrossRef] [PubMed]
  49. Yang, T.-L.; Guo, Y.; Shen, H.; Lei, S.-F.; Liu, Y.-J.; Li, J.; Liu, Y.-Z.; Yu, N.; Chen, J.; Xu, T.; et al. Genetic association study of common mitochondrial variants on body fat mass. PLoS ONE 2011, 6, e21595. [Google Scholar] [CrossRef] [PubMed]
  50. Ebner, S.; Mangge, H.; Langhof, H.; Halle, M.; Siegrist, M.; Aigner, E.; Paulmichl, K.; Paulweber, B.; Datz, C.; Sperl, W.; et al. Mitochondrial haplogroup T is associated with obesity in Austrian juveniles and adults. PLoS ONE 2015, 10, 1–13. [Google Scholar] [CrossRef] [PubMed]
  51. Knoll, N.; Jarick, I.; Volckmar, A.L.; Klingenspor, M.; Illig, T.; Grallert, H.; Gieger, C.; Wichmann, H.E.; Peters, A.; Wiegand, S.; et al. Mitochondrial DNA variants in obesity. PLoS ONE 2014, 9, e94882. [Google Scholar] [CrossRef] [PubMed]
  52. Grant, S.F.A.; Glessner, J.T.; Bradfield, J.P.; Zhao, J.; Tirone, J.E.; Berkowitz, R.I.; Hakonarson, H.; Sondheimer, N. Lack of relationship between mitochondrial heteroplasmy or variation and childhood obesity. Int. J. Obes. 2012, 36, 80–83. [Google Scholar] [CrossRef] [PubMed]
  53. Robinson, M.R.; Hemani, G.; Medina-Gomez, C.; Mezzavilla, M.; Esko, T.; Shakhbazov, K.; Powell, J.E.; Vinkhuyzen, A.; Berndt, S.I.; Gustafsson, S.; et al. Population genetic differentiation of height and body mass index across Europe. Nat. Genet. 2015, 47, 1357–1362. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Table
Table 1. European mitochondrial DNA haplogroups in mothers and children of the Avon Longitudinal Study of Parents and Children (ALSPAC) used in the analysis of breast cancer risk factors (quality score > 80%).
Table 1. European mitochondrial DNA haplogroups in mothers and children of the Avon Longitudinal Study of Parents and Children (ALSPAC) used in the analysis of breast cancer risk factors (quality score > 80%).
mtDNA HaplogroupsN%
HV383549.3
U106013.6
J87311.2
T81710.5
K6948.9
Other European5026.5
Total7781100.0
mtDNA: Mitochondrial DNA.
Table
Table 2. Major European mitochondrial DNA (mtDNA) haplogroups and breast cancer risk factors in ALSPAC mothers. Regression models adjusted for age and the top 10 genomic principal components. (A) Continuous variables; (B) categorical variables. Reference is haplogroup HV.
Table 2. Major European mitochondrial DNA (mtDNA) haplogroups and breast cancer risk factors in ALSPAC mothers. Regression models adjusted for age and the top 10 genomic principal components. (A) Continuous variables; (B) categorical variables. Reference is haplogroup HV.
A-VariableCladeBeta95% CIp-ValueNModel p-Value
SHBG(nmol/L) in pregnancy 1U−40.28(−136.56,56.01)0.4081010.338
J−79.04(−180.04,21.95)0.123101
T40.96(−48.11,130.02)0.363101
K21.84(−79.84,123.52)0.670101
Other European−64.39(−205.90,77.11)0.368101
Testosterone (nmol/L) in pregnancy 1U−0.32(−1.16,0.52)0.4441000.228
J−0.10(−0.98,0.77)0.813100
T0.15(−0.63,0.93)0.705100
K0.98(0.09,1.86)0.031100
Other European−0.02(−1.25,1.21)0.972100
IGF-I (ng/mL) in pregnancy 1U4.21(−19.23,27.66)0.7242310.709
J2.53(−21.74,26.81)0.837231
T−2.67(−25.40,20.05)0.817231
K−14.90(−42.78,12.99)0.294231
Other European16.08(−12.44,44.61)0.268231
IGF-II (ng/mL) in pregnancy 1U34.45(−51.06,119.97)0.4282240.232
J−2.32(−89.70,85.06)0.958224
T−32.26(−116.26,51.74)0.450224
K105.67(2.40,208.93)0.045224
Other European61.39(−41.29,164.06)0.240224
IGFBP-3 (ng/mL) in pregnancy 1U−371.92(−926.11,182.27)0.1872310.023
J67.20(−506.61,641.01)0.818231
T−474.38(−1011.50,62.74)0.083231
K772.03(112.93,1431.12)0.022231
Other European381.81(−292.51,1056.13)0.266231
Mother’s natural mother’s age at birth (years)U0.08(−0.44,0.60)0.76941640.985
J−0.04(−0.61,0.52)0.8784164
T0.14(−0.42,0.70)0.6264164
K0.18(−0.43,0.78)0.5624164
Other European0.11(−0.60,0.83)0.7614164
Age of mother at birth (years)U0.06(−0.35,0.46)0.79144410.591
J0.32(−0.13,0.76)0.1624441
T−0.16(−0.60,0.29)0.4934441
K0.15(−0.33,0.62)0.5494441
Other European−0.17(−0.73,0.39)0.5544441
Age at menarche (years)U−0.02(−0.16,0.12)0.80740730.043
J−0.17(−0.32,−0.01)0.0324073
T−0.19(−0.35,−0.04)0.0134073
K0.05(−0.11,0.22)0.5354073
Other European−0.11(−0.30,0.09)0.2714073
Age at menopause (years) (child ~20 years old)U0.95(0.07,1.82)0.0348770.329
J−0.09(−1.02,0.84)0.845877
T−0.16(−1.20,0.89)0.766877
K−0.15(−1.12,0.83)0.769877
Other European−0.16(−1.37,1.06)0.802877
Birth weight of mother (g)U−34.42(−101.56,32.72)0.31529060.760
J−33.85(−107.72,40.03)0.3692906
T−6.93(−78.36,64.50)0.8492906
K−26.06(−103.86,51.74)0.5112906
Other European31.84(−61.43,125.10)0.5032906
BMI (~12 weeks gestation)U0.20(−0.15,0.55)0.26042290.028
J0.60(0.22,0.98)0.0024229
T0.22(−0.16,0.60)0.2594229
K0.42(0.02,0.83)0.0404229
Other European0.02(−0.47,0.50)0.9404229
BMI (May–September 2010)U0.41(−0.36,1.19)0.29915030.713
J0.45(−0.38,1.27)0.2861503
T−0.02(−0.90,0.85)0.9561503
K−0.28(−1.16,0.60)0.5351503
Other European0.18(−0.83,1.19)0.7271503
BMI (FOM1: 2009–2011)U0.27(−0.35,0.90)0.38925650.175
J0.58(−0.08,1.25)0.0862565
T−0.44(−1.13,0.25)0.2132565
K0.32(−0.40,1.05)0.3812565
Other European0.54(−0.32,1.39)0.2182565
BMI (FOM2: 2011–2013)U0.68(−0.09,1.45)0.08416490.438
J0.54(−0.28,1.36)0.1951649
T−0.13(−1.02,0.75)0.7691649
K0.06(−0.84,0.95)0.9001649
Other European0.40(−0.61,1.42)0.4341649
Pre-pregnancy weight (kg)U1.07(0.07,2.07)0.03642720.035
J1.54(0.46,2.62)0.0054272
T0.95(−0.14,2.04)0.0884272
K1.01(−0.15,2.17)0.0874272
Other European0.50(−0.88,1.88)0.4784272
Weight (kg) (May–September 2010)U1.70(−0.43,3.84)0.11815120.417
J0.77(−1.51,3.05)0.5091512
T−0.20(−2.60,2.19)0.8691512
K−1.16(−3.59,1.27)0.3491512
Other European1.18(−1.62,3.98)0.4101512
Weight (kg) (FOM1: 2009–2011)U1.10(−0.63,2.83)0.21325650.288
J1.21(−0.63,3.06)0.1982565
T−0.50(−2.41,1.41)0.6092565
K0.61(−1.39,2.61)0.5502565
Other European2.18(−0.18,4.53)0.0712565
Weight (kg) (FOM2: 2011–2013)U2.53(0.39,4.66)0.02016490.294
J1.20(−1.05,3.45)0.2951649
T0.79(−1.65,3.22)0.5281649
K0.52(−1.95,3.00)0.6781649
Other European1.52(−1.28,4.32)0.2871649
Height (cm) (~12 weeks gestation)U0.66(0.05,1.27)0.03444340.134
J−0.26(−0.91,0.40)0.4384434
T0.36(−0.30,1.02)0.2884434
K−0.23(−0.93,0.48)0.5294434
Other European0.43(−0.41,1.27)0.3154434
Height (cm) (May–September 2010)U0.80(−0.15,1.74)0.09817450.241
J−0.60(−1.60,0.41)0.2441745
T−0.09(−1.14,0.97)0.8731745
K−0.48(−1.57,0.61)0.3891745
Other European0.46(−0.79,1.71)0.4671745
Height (cm) (FOM1: 2009–2011)U0.48(−0.25,1.21)0.19825650.060
J−0.40(−1.18,0.38)0.3202565
T0.91(0.10,1.72)0.0282565
K−0.29(−1.14,0.56)0.5102565
Other European0.68(−0.32,1.68)0.1812565
Sitting height (cm) (FOM1: 2009–2011)U0.36(−0.03,0.75)0.07325450.057
J0.10(−0.32,0.52)0.6372545
T0.63(0.20,1.06)0.0042545
K0.05(−0.40,0.50)0.8372545
Other European0.35(−0.18,0.88)0.1932545
Leg length (cm) (FOM1: 2009–2011)U0.11(−0.38,0.60)0.67025450.170
J−0.43(−0.95,0.10)0.1112545
T0.31(−0.24,0.85)0.2702545
K−0.34(−0.90,0.23)0.2452545
Other European0.36(−0.30,1.03)0.2862545
Height (cm) (FOM2: 2011–2013)U0.91(0.00,1.81)0.04916490.041
J−0.38(−1.33,0.58)0.4391649
T1.36(0.32,2.39)0.0101649
K0.42(−0.63,1.47)0.4341649
Other European0.46(−0.73,1.64)0.4491649
Sitting height (cm) (FOM2: 2011–2013)U0.54(0.09,1.00)0.02016480.016
J0.19(−0.29,0.68)0.4321648
T0.88(0.35,1.40)0.0011648
K0.26(−0.27,0.80)0.3321648
Other European0.35(−0.25,0.96)0.2521648
Leg length (cm) (FOM2: 2011–2013)U0.37(−0.24,0.98)0.23916480.166
J−0.57(−1.21,0.08)0.0841648
T0.49(−0.21,1.18)0.1731648
K0.16(−0.55,0.87)0.6601648
Other European0.11(−0.69,0.91)0.7881648
Total body bone mineral density (FOM1: 2009–2011)U0.01(−0.003,0.02)0.15625090.083
J−0.003(−0.01,0.01)0.5492509
T0.01(0.003,0.03)0.0122509
K−0.003(−0.01,0.01)0.6012509
Other European0.002(−0.01,0.02)0.7442509
Total body bone mineral density (FOM2: 2011–2013)U0.01(−0.003,0.02)0.14616140.421
J−0.002(−0.02,0.01)0.8201614
T0.01(−0.01,0.02)0.3781614
K−0.01(−0.02,0.01)0.2741614
Other European0.001(−0.02,0.02)0.9451614
Daily fat intake (g) (~32 weeks gestation)U−0.36(−1.13,0.41)0.35843860.230
J0.02(−0.82,0.85)0.9714386
T0.18(−0.66,1.01)0.6784386
K−0.08(−0.98,0.81)0.8564386
Other European−1.29(−2.35,−0.23)0.0184386
B—VariableCladeOR95% CIp-ValueNModel p-Value
parity (~18 weeks gestation)U1.01(0.85,1.20)0.90746170.741
J1.07(0.90,1.28)0.4474617
T0.95(0.79,1.15)0.6124617
K1.01(0.84,1.23)0.8854617
Other European0.87(0.68,1.09)0.2234617
Number of pregnancies after study child (child ~21 months old)U0.95(0.76,1.19)0.63940070.524
J0.82(0.64,1.05)0.1194007
T0.96(0.75,1.22)0.7224007
K0.80(0.61,1.06)0.1234007
Other European0.94(0.69,1.27)0.6874007
Alcohol consumption before this pregnancyU1.15(0.97,1.36)0.09846450.082
J1.02(0.85,1.22)0.8124645
T0.94(0.78,1.13)0.4954645
K0.81(0.66,0.98)0.0314645
Other European0.95(0.76,1.21)0.6954645
Alcohol consumption (child ~8 months old)U0.94(0.79,1.12)0.50043190.226
J0.88(0.73,1.06)0.1694319
T0.89(0.73,1.07)0.2164319
K0.79(0.65,0.98)0.0324319
Other European0.84(0.66,1.07)0.1764319
Quantity of alcohol mother drinks (child ~21 months old)U1.04(0.87,1.23)0.67939950.170
J0.84(0.70,1.03)0.0893995
T0.95(0.78,1.15)0.5843995
K0.79(0.64,0.98)0.0303995
Other European0.96(0.76,1.22)0.7673995
Quantity of alcohol mother drinks (child ~33 months old)U0.98(0.82,1.16)0.79638790.050
J0.86(0.70,1.04)0.1243879
T0.85(0.70,1.03)0.1073879
K0.73(0.59,0.91)0.0063879
Other European0.84(0.65,1.07)0.1543879
Quantity of alcohol mother drinks (child ~5 years old)U1.13(0.93,1.38)0.20634450.058
J0.96(0.79,1.17)0.6883445
T0.91(0.75,1.13)0.3993445
K0.73(0.59,0.92)0.0083445
Other European1.00(0.77,1.28)0.9723445
Years taking contraceptive pill (child ~8 years old)U0.87(0.69,1.09)0.22530560.170
J0.83(0.64,1.05)0.1213056
T1.12(0.86,1.45)0.4253056
K0.82(0.63,1.07)0.1543056
Other European1.16(0.83,1.62)0.3893056
Years taking contraceptive pill (child ~11 years old)U0.88(0.70,1.09)0.24333520.289
J0.95(0.74,1.21)0.6593352
T1.00(0.78,1.27)0.9743352
K0.79(0.61,1.03)0.0873352
Other European1.22(0.88,1.68)0.2333352
Frequency of alcohol consumption (May–September 2010)U1.22(0.93,1.60)0.15320360.425
J1.11(0.84,1.48)0.4672036
T0.90(0.68,1.20)0.4832036
K0.92(0.68,1.25)0.6102036
Other European1.22(0.84,1.75)0.2922036
Duration of breastfeeding last baby (~18 weeks gestation)U0.97(0.78,1.22)0.80525080.229
J1.00(0.79,1.28)0.9732508
T1.22(0.94,1.57)0.1272508
K0.95(0.73,1.23)0.7152508
Other European1.39(1.01,1.92)0.0442508
Average time spent exercising in past year (from ~18 weeks gestation to 2010)U1.00(0.85,1.19)0.95445450.392
J0.97(0.80,1.16)0.7124545
T1.00(0.83,1.21)0.9964545
K0.80(0.66,0.98)0.0284545
Other European0.94(0.75,1.19)0.6334545
Number of hours of activity per week (from ~18 weeks gestation to 2010)U0.93(0.79,1.09)0.37845960.792
J0.93(0.78,1.12)0.4524596
T0.93(0.78,1.13)0.4854596
K0.96(0.79,1.16)0.6484596
Other European1.09(0.86,1.38)0.4644596
Number of hours of vigorous activity per week (from ~18 weeks gestation to 2010)U1.14(0.95,1.36)0.14446700.684
J1.02(0.84,1.25)0.8534670
T1.08(0.90,1.32)0.3964670
K0.97(0.78,1.20)0.7534670
Other European0.98(0.76,1.26)0.8604670
Mother has reached menopause (child ~20 years old)U0.98(0.69,1.39)0.89620740.442
J0.95(0.65,1.38)0.7772074
T0.73(0.50,1.08)0.1122074
K1.26(0.85,1.88)0.2462074
Other European1.07(0.68,1.69)0.7612074
Mother’s natural mother had breast cancer (~12 weeks gestation)U0.66(0.38,1.14)0.13445090.679
J0.85(0.50,1.44)0.5424509
T0.83(0.48,1.43)0.4944509
K1.09(0.65,1.83)0.7484509
Other European1.04(0.54,1.97)0.9154509
Mother’s natural mother had breast cancer (child ~8 years old)U0.83(0.52,1.33)0.43931220.496
J1.07(0.67,1.71)0.7633122
T0.61(0.34,1.09)0.0943122
K0.95(0.56,1.60)0.8463122
Other European0.68(0.34,1.37)0.2823122
Mother ever had a mammogram (FOM1: 2009–2011)U1.01(0.66,1.55)0.95525770.408
J0.80(0.50,1.28)0.3482577
T0.73(0.44,1.20)0.2182577
K1.18(0.72,1.91)0.5162577
Other European0.63(0.34,1.16)0.1372577
Breastfed last baby (~18 weeks gestation)U1.04(0.77,1.39)0.81425090.537
J1.11(0.81,1.53)0.5192509
T1.3(0.93,1.83)0.1312509
K1.05(0.75,1.48)0.7672509
Other European1.39(0.90,2.14)0.1402509
Mother ever had diabetes (from ~12 weeks gestation to 2010)U0.98(0.58,1.67)0.94847720.428
J1.12(0.65,1.93)0.6864772
T1.67(1.03,2.72)0.0394772
K1.28(0.73,2.23)0.3914772
Other European1.03(0.51,2.09)0.9344772
Mother ever had any cancer (since child was ~6 years old to 2010)U0.60(0.34,1.05)0.07142510.243
J0.67(0.38,1.19)0.1714251
T0.83(0.49,1.41)0.4864251
K1.04(0.61,1.77)0.8824251
Other European1.30(0.74,2.29)0.3624251
Mother ever had an hysterectomy and/or oophorectomy (since child was ~8 years old to 2010)U0.93(0.60,1.43)0.73637280.955
J0.94(0.60,1.49)0.8083728
T1.15(0.74,1.79)0.5263728
K0.86(0.51,1.45)0.5693728
Other European0.96(0.54,1.70)0.8843728
Mother had ever done nightshift work (from ~18 weeks gestation to when child was ~10 years old)U1.05(0.88,1.25)0.59047710.947
J0.96(0.79,1.15)0.6364771
T0.95(0.79,1.16)0.6344771
K0.95(0.78,1.17)0.6484771
Other European1.03(0.81,1.31)0.8214771
Mother ever took oral contraceptives (from ~12 weeks gestation to 2013)U1.17(0.74,1.85)0.49148380.582
J1.07(0.67,1.72)0.7764838
T0.86(0.55,1.33)0.4974838
K0.77(0.49,1.21)0.2524838
Other European0.75(0.45,1.27)0.2854838
Mother ever used non-oral hormonal contraceptives (since child was ~21 months old to 2013)U1.04(0.85,1.28)0.68744740.817
J0.93(0.74,1.16)0.5284474
T1.14(0.92,1.42)0.2394474
K1.02(0.80,1.29)0.9014474
Other European1.02(0.77,1.35)0.8994474
Mother ever had hormone replacement therapy (since child was ~9 years old to 2010)U1.06(0.76,1.49)0.72430630.706
J0.99(0.69,1.43)0.9713063
T0.90(0.61,1.33)0.5873063
K1.32(0.91,1.91)0.1383063
Other European1.05(0.67,1.67)0.8223063
Mother ever taken aspirin (from ~18 weeks gestation to 2010)U1.02(0.86,1.22)0.80748480.355
J1.08(0.89,1.30)0.4454848
T0.95(0.78,1.15)0.6034848
K0.81(0.66,1.00)0.0514848
Other European1.03(0.81,1.30)0.8404848
Mother ever smoked (from ~18 weeks gestation to 2010)U0.99(0.83,1.17)0.87748630.739
J1.02(0.85,1.23)0.7984863
T0.93(0.77,1.13)0.4584863
K0.87(0.71,1.07)0.1784863
Other European0.92(0.72,1.16)0.4784863
Mother ever had an X-ray (from ~12 weeks gestation to when child was ~33 months old)U1.02(0.84,1.24)0.82246310.640
J0.90(0.73,1.11)0.3274631
T0.97(0.78,1.20)0.7734631
K1.15(0.93,1.44)0.2014631
Other European0.96(0.74,1.26)0.7774631
Mother exercised at least once a week (from ~18 weeks gestation to 2010)U1.27(0.95,1.69)0.10547230.071
J1.24(0.91,1.69)0.1674723
T1.01(0.76,1.36)0.9244723
K0.77(0.58,1.02)0.0664723
Other European1.19(0.81,1.76)0.3744723
1 Adjusted for gestational age and 10 principal components. FOM: Focus on Mothers clinic; SHBG: Sex hormone binding globulin; IGF:Insulin-like growth factor; IGFBP: Insulin-like growth factor binding protein; BMI: Body mass index; ALSPAC: Avon Longitudinal Study of Parents and Children.
Table
Table 3. Major European mtDNA haplogroups and breast cancer risk factors in ALSPAC children. Regression models adjusted for age, sex and the top 10 genomic principal components. (A) Continuous variables; (B) categorical variables. Reference is haplogroup HV.
Table 3. Major European mtDNA haplogroups and breast cancer risk factors in ALSPAC children. Regression models adjusted for age, sex and the top 10 genomic principal components. (A) Continuous variables; (B) categorical variables. Reference is haplogroup HV.
A—VariableCladeBeta95% CIp-ValueNModel p-Value
SHBG (nmol/L) (~8 years old)U−3.13(−10.91,4.65)0.4296350.435
J1.05(−6.84,8.94)0.794635
T−8.19(−16.27,−0.11)0.047635
K−0.32(−8.93,8.28)0.941635
Other European−2.52(−12.58,7.53)0.623635
SHBG, nmol/L) (~15 years old) 1U−0.71(−5.93,4.52)0.79012810.812
J−3.64(−9.15,1.87)0.1951281
T−1.67(−7.14,3.8)0.5501281
K0.85(−5.29,6.98)0.7861281
Other European−1.95(−8.86,4.96)0.5801281
Testosterone (nmol/L) (~15 years old) 1U0.01(−0.06,0.08)0.77513390.478
J−0.05(−0.13,0.02)0.1721339
T−0.004(−0.08,0.07)0.9111339
K0.05(−0.04,0.14)0.2421339
Other European−0.04(−0.14,0.06)0.4631339
Androstenedione (ng/dL) (~8 years old)U0.97(−6.49,8.42)0.7995830.948
J−0.64(−8.18,6.9)0.867583
T2.75(−5.10,10.61)0.492583
K−1.13(−9.24,6.97)0.783583
Other European−2.80(−12.70,7.09)0.578583
DHEAS(ug/dL) (~8 years old) 2U−0.02(−7.00,6.95)0.9955850.269
J−1.83(−8.91,5.26)0.613585
T6.29(−1.04,13.63)0.093585
K−3.89(−11.51,3.73)0.317585
Other European−5.05(−14.35,4.25)0.286585
IGF-I (ng/mL) (cord blood) 3U6.38(−4.09,16.85)0.2324420.125
J11.50(−0.87,23.87)0.068442
T−3.09(−14.72,8.54)0.602442
K6.36(−6.65,19.37)0.337442
Other European14.71(0.55,28.87)0.042442
IGF-I (~7 years old)U−6.19(−22.93,10.55)0.4683400.347
J8.42(−8.53,25.37)0.329340
T−13.79(−31.15,3.57)0.119340
K3.32(−16.52,23.16)0.742340
Other European−10.36(−32.65,11.92)0.361340
IGF-I (ng/mL) (~8 years old)U6.41(−13.53,26.34)0.5283170.499
J−19.44(−42.05,3.18)0.092317
T−2.37(−22.9,18.16)0.821317
K−5.54(−27.13,16.05)0.614317
Other European2.74(−25.34,30.81)0.848317
IGF-II (ng/mL) (cord blood) 3U−9.90(−32.12,12.32)0.3824430.032
J−24.76(−51.01,1.49)0.064443
T23.21(−1.46,47.89)0.065443
K−24.67(−52.29,2.95)0.080443
Other European−3.16(−33.22,26.89)0.836443
IGF-II (ng/mL) (~7 years old)U−33.08(−95.43,29.26)0.2961490.647
J−13.87(−69.00,41.26)0.620149
T−53.36(−120,12.9519)0.114149
K−8.39(−77.90,61.13)0.812149
Other European4.21(−74.08,82.51)0.915149
IGFBP-3 (ng/mL) (cord blood) 3U−60.05(−346.54,226.44)0.6791370.856
J−65.89(−413.24,281.45)0.708137
T132.24(−118.57,383.05)0.299137
K−37.07(−352.6,278.46)0.816137
Other European−26.71(−376.79,323.37)0.880137
IGFBP-3 (ng/mL) (~7 years old)U−77.05(−440,29.44)0.6793400.439
J76.89(−290.00,447.99)0.684340
T−320.00(−700,63.7442)0.103340
K−180.00(−620.00,251.58)0.408340
Other European−320.00(−810.00,169.81)0.201340
IGFBP-3 (ng/mL) (~8 years old)U22.75(−572.91,618.40)0.9403170.674
J77.72(−598.04,753.47)0.821317
T150.74(−462.65,764.13)0.629317
K404.59(−240.45,1049.63)0.218317
Other European−412.02(−1253.45,429.42)0.336317
Age at menarche (months) 1U1.30(0.29,2.89)0.10827280.254
J−0.85(−2.56,0.86)0.3322728
T0.08(−1.64,1.79)0.9312728
K−1.30(−3.30,0.69)0.2012728
Other European−0.56(−2.75,1.63)0.6162728
BMI (kg/m2) (~12 months old)U0.15(−0.14,0.44)0.3137730.014
J−0.13(−0.48,0.22)0.478773
T0.25(−0.05,0.54)0.099773
K−0.34(−0.67,0.00)0.047773
Other European0.40(0.02,0.79)0.041773
BMI (kg/m2) (~25 months old)U0.13(−0.19,0.44)0.4356750.507
J−0.16(−0.54,0.23)0.427675
T0.21(−0.12,0.53)0.213675
K−0.06(−0.44,0.31)0.746675
Other European0.22(−0.21,0.65)0.310675
BMI (kg/m2) (~37 months old)U0.12(−0.18,0.43)0.4296910.143
J−0.21(−0.58,0.15)0.254691
T0.13(−0.18,0.45)0.410691
K0.12(−0.24,0.48)0.518691
Other European0.48(0.06,0.89)0.024691
BMI (kg/m2) (~61 months old)U0.13(−0.23,0.48)0.4886640.103
J−0.08(−0.50,0.35)0.725664
T0.28(−0.07,0.64)0.118664
K−0.05(−0.45,0.36)0.827664
Other European0.61(0.14,1.08)0.012664
BMI (kg/m2) (~7 years old)U−0.04(−0.21,0.13)0.64753450.898
J−0.03(−0.21,0.15)0.7215345
T0.01(−0.18,0.19)0.9455345
K0.09(−0.11,0.29)0.3785345
Other European0.06(−0.17,0.28)0.6115345
BMI (kg/m2) (~9 years old)U0.01(−0.23,0.25)0.94351060.981
J0.03(−0.22,0.28)0.8225106
T−0.10(−0.36,0.16)0.4565106
K−0.01(−0.29,0.28)0.9705106
Other European0.01(−0.31,0.32)0.9685106
BMI (kg/m2) (~11 years old)U−0.09(−0.38,0.20)0.55147640.768
J0.01(−0.30,0.33)0.9274764
T0.01(−0.31,0.33)0.9404764
K−0.03(−0.38,0.32)0.8644764
Other European0.27(−0.12,0.66)0.1794764
BMI (kg/m2) (~13 years old)U0.004(−0.30,0.31)0.98145160.787
J−0.05(−0.39,0.28)0.7554516
T−0.02(−0.36,0.32)0.9054516
K−0.24(−0.61,0.13)0.2094516
Other European0.15(−0.27,0.56)0.4834516
BMI (kg/m2) (~15 years old)U−0.08(−0.43,0.27)0.65636940.219
J0.13(−0.24,0.49)0.4993694
T−0.18(−0.56,0.20)0.3543694
K−0.04(−0.44,0.36)0.8423694
Other European0.49(0.04,0.95)0.0333694
BMI (kg/m2) (~17 years old)U−0.15(−0.56,0.27)0.49033270.812
J0.17(−0.27,0.62)0.4453327
T−0.02(−0.47,0.43)0.9313327
K−0.10(−0.58,0.39)0.7003327
Other European0.24(−0.33,0.80)0.4173327
BMI (kg/m2) (~11 years old, puberty questionnaire)U−0.04(−0.40,0.31)0.81328740.583
J0.03(−0.34,0.39)0.8862874
T0.03(−0.35,0.41)0.8782874
K0.36(−0.07,0.79)0.0992874
Other European−0.17(−0.63,0.30)0.4792874
BMI (kg/m2) (~13 years old, puberty questionnaire)U−0.11(−0.49,0.26)0.55428890.604
J−0.22(−0.62,0.19)0.2922889
T−0.19(−0.60,0.22)0.3552889
K−0.36(−0.81,0.10)0.1262889
Other European0.05(−0.46,0.55)0.8612889
BMI (kg/m2) (~15 years old, puberty questionnaire)U−0.06(−0.44,0.33)0.76825050.456
J0.25(−0.16,0.66)0.2372505
T−0.05(−0.47,0.37)0.8252505
K−0.15(−0.62,0.32)0.5392505
Other European0.40(−0.12,0.92)0.1352505
BMI (kg/m2) (~17 years old, puberty questionnaire)U−0.24(−0.66,0.19)0.28123740.123
J0.38(−0.06,0.83)0.0932374
T0.21(−0.25,0.67)0.3732374
K−0.03(−0.54,0.48)0.9042374
Other European0.52(−0.05,1.09)0.0722374
Birth weight (g)U−7.46(−39.89,24.96)0.65268380.584
J−5.43(−40.22,29.36)0.7596838
T−25.25(−61.23,10.74)0.1696838
K−12.34(−50.81,26.12)0.5296838
Other European−33.86(−77.51,9.79)0.1286838
Weight (kg) (~12 months old)U0.10(−0.13,0.34)0.3967750.092
J−0.05(−0.34,0.23)0.718775
T0.22(−0.03,0.46)0.079775
K−0.24(−0.51,0.03)0.081775
Other European0.16(−0.15,0.48)0.309775
Weight (kg) (~25 months old)U0.15(−0.17,0.48)0.3587180.301
J0.0002(−0.39,0.39)0.999718
T0.31(−0.01,0.64)0.061718
K−0.19(−0.57,0.19)0.317718
Other European0.02(−0.42,0.45)0.938718
Weight (kg) (~37 months old)U0.20(−0.20,0.60)0.3306990.745
J−0.09(−0.58,0.39)0.704699
T0.22(−0.20,0.64)0.301699
K−0.01(−0.48,0.46)0.966699
Other European0.24(−0.31,0.78)0.395699
Weight (kg) (~61 months old)U0.40(−0.25,1.05)0.2296670.494
J0.18(−0.59,0.95)0.648667
T0.53(−0.13,1.18)0.113667
K−0.18(−0.93,0.56)0.626667
Other European0.31(−0.56,1.18)0.484667
Weight (kg) (~7 years old)U−0.11(−0.48,0.26)0.57353460.974
J0.03(−0.37,0.42)0.8965346
T−0.02(−0.42,0.39)0.9375346
K0.13(−0.32,0.57)0.5735346
Other European−0.05(−0.54,0.45)0.8465346
Weight (kg) (~9 years old)U0.01(−0.60,0.62)0.98151490.877
J0.28(−0.37,0.92)0.4025149
T0.03(−0.63,0.69)0.9375149
K0.04(−0.68,0.76)0.9095149
Other European−0.37(−1.18,0.44)0.3685149
Weight (kg, DEXA) (~9 years old)U0.04(−0.57,0.65)0.89249100.802
J0.36(−0.28,1.01)0.2734910
T0.03(−0.63,0.69)0.9314910
K−0.03(−0.76,0.69)0.9314910
Other European−0.36(−1.18,0.45)0.3864910
Weight (kg) (~11 years old)U−0.17(−1.02,0.67)0.69047700.972
J0.21(−0.70,1.13)0.6464770
T0.15(−0.78,1.09)0.7484770
K0.14(−0.88,1.16)0.7874770
Other European0.31(−0.83,1.45)0.5954770
Weight (kg) (~13 years old)U0.18(−0.77,1.13)0.71545160.931
J0.30(−0.73,1.33)0.5684516
T0.42(−0.62,1.46)0.4304516
K−0.28(−1.43,0.87)0.6314516
Other European0.08(−1.20,1.36)0.9054516
Weight (kg) (~15 years old)U−0.07(−1.20,1.07)0.91036940.242
J0.91(−0.29,2.12)0.1363694
T−0.41(−1.66,0.83)0.5133694
K0.04(−1.29,1.36)0.9573694
Other European1.45(−0.05,2.94)0.0573694
Weight (kg) (~17 years old)U−0.21(−1.52,1.11)0.75633290.902
J0.59(−0.82,2.00)0.4113329
T0.05(−1.38,1.48)0.9453329
K−0.05(−1.58,1.49)0.9533329
Other European0.77(−1.03,2.56)0.4023329
Weight (kg) (~11 years old, puberty questionnaire)U0.18(−0.71,1.07)0.69331530.757
J0.44(−0.49,1.36)0.3563153
T0.18(−0.77,1.13)0.7143153
K0.57(−0.51,1.65)0.3003153
Other European−0.42(−1.61,0.76)0.4843153
Weight (kg) (~13 years old, puberty questionnaire)U0.51(−0.62,1.64)0.37330530.959
J0.08(−1.11,1.28)0.8913053
T0.30(−0.92,1.52)0.6293053
K0.00(−1.37,1.36)0.9983053
Other European0.36(−1.17,1.89)0.6443053
Weight (kg) (~15 years old, puberty questionnaire)U−0.29(−1.52,0.95)0.64926590.567
J0.77(−0.55,2.09)0.2552659
T0.19(−1.17,1.55)0.7872659
K0.41(−1.10,1.92)0.5962659
Other European1.25(−0.43,2.93)0.1452659
Weight (kg) (~17 years old, puberty questionnaire)U−0.27(−1.64,1.10)0.69924760.092
J1.82(0.39,3.24)0.0122476
T0.45(−1.03,1.93)0.5492476
K0.20(−1.45,1.85)0.8112476
Other European1.60(−0.20,3.40)0.0812476
Height (cm) (~25 months old)U−0.05(−0.75,0.64)0.8816750.130
J0.22(−0.62,1.07)0.604675
T0.32(−0.39,1.03)0.384675
K−0.64(−1.47,0.18)0.125675
Other European−0.99(−1.93,−0.05)0.040675
Height (cm) (~37 months old)U0.23(−0.57,1.03)0.5796910.547
J0.28(−0.67,1.24)0.561691
T0.15(−0.68,0.98)0.724691
K−0.51(−1.45,0.43)0.286691
Other European−0.67(−1.76,0.41)0.222691
Height (cm) (~61 months old)U0.67(−0.37,1.71)0.2066650.134
J0.79(−0.44,2.02)0.207665
T0.44(−0.6,1.48)0.407665
K−0.46(−1.64,0.73)0.447665
Other European−1.18(−2.56,0.2)0.093665
Sitting height (kg) (~61 months old)U0.33(−0.25,0.9)0.2626670.722
J0.21(−0.47,0.89)0.545667
T0.15(−0.43,0.72)0.617667
K−0.13(−0.79,0.52)0.694667
Other European−0.28(−1.04,0.48)0.472667
Height (cm) (~7 years old)U−0.07(−0.50,0.36)0.75253500.885
J0.22(−0.25,0.68)0.3655350
T−0.03(−0.51,0.44)0.8905350
K−0.03(−0.55,0.49)0.9155350
Other European−0.21(−0.79,0.37)0.4845350
Sitting height (cm) (~7 years old)U−0.02(−0.24,0.21)0.87853510.949
J0.03(−0.21,0.27)0.8215351
T0.01(−0.24,0.25)0.9665351
K−0.02(−0.29,0.25)0.8845351
Other European−0.15(−0.45,0.15)0.3245351
Height (cm) (~9 years old)U0.07(−0.44,0.59)0.77851080.213
J0.49(−0.06,1.04)0.0815108
T0.33(−0.23,0.89)0.2475108
K0.19(−0.43,0.80)0.5515108
Other European−0.49(−1.18,0.20)0.1655108
Sitting height (cm) (~9 years old)U0.11(−0.15,0.37)0.39751450.178
J0.28(0.01,0.55)0.0425145
T0.17(−0.10,0.45)0.2215145
K0.06(−0.24,0.37)0.6805145
Other European−0.20(−0.54,0.14)0.2525145
Height (cm) (~11 years old)U−0.02(−0.63,0.59)0.94647650.614
J0.32(−0.34,0.98)0.3424765
T0.22(−0.45,0.90)0.5174765
K0.36(−0.38,1.10)0.3424765
Other European−0.43(−1.26,0.39)0.3044765
Sitting height (cm) (~11 years old)U−0.08(−0.40,0.23)0.60447690.784
J0.10(−0.24,0.44)0.5664769
T0.10(−0.25,0.45)0.5584769
K0.11(−0.28,0.49)0.5874769
Other European−0.20(−0.63,0.23)0.3624769
Height (cm) (~13 years old)U0.18(−0.49,0.84)0.60145590.162
J0.67(−0.05,1.39)0.0694559
T0.67(−0.06,1.39)0.0724559
K0.46(−0.34,1.26)0.2624559
Other European−0.39(−1.28,0.51)0.3944559
Sitting height (cm) (~13 years old)U0.07(−0.29,0.43)0.69445310.106
J0.34(−0.05,0.73)0.0874531
T0.35(−0.04,0.74)0.0804531
K0.29(−0.15,0.72)0.1954531
Other European−0.31(−0.79,0.17)0.2064531
Height (cm) (~15 years old)U0.21(−0.47,0.89)0.55137020.514
J0.75(0.02,1.47)0.0433702
T0.16(−0.58,0.90)0.6673702
K0.22(−0.57,1.01)0.5893702
Other European0.03(−0.86,0.93)0.9403702
Sitting height (cm) (~15 years old)U−0.07(−0.47,0.33)0.73931390.188
J0.52(0.09,0.94)0.0173139
T0.11(−0.33,0.54)0.6303139
K0.15(−0.32,0.62)0.5373139
Other European−0.17(−0.71,0.37)0.5343139
Height (cm) (~17 years old)U0.23(−0.44,0.91)0.49633300.975
J0.14(−0.58,0.86)0.7013330
T0.15(−0.59,0.88)0.6933330
K0.27(−0.52,1.05)0.5073330
Other European0.06(−0.86,0.98)0.9043330
Height (cm) (~11 years old, puberty questionnaire)U0.32(−0.45,1.10)0.41531670.656
J0.45(−0.35,1.26)0.2713167
T0.56(−0.28,1.39)0.1913167
K0.20(−0.74,1.14)0.6773167
Other European−0.21(−1.23,0.80)0.6803167
Height (cm) (~13 years old, puberty questionnaire)U1.09(0.19,1.98)0.01732770.020
J1.19(0.23,2.14)0.0153277
T1.22(0.26,2.19)0.0133277
K0.71(−0.37,1.79)0.2003277
Other European0.14(−1.06,1.33)0.8213277
Height (cm) (~15 years old, puberty questionnaire)U0.27(−0.63,1.16)0.55927970.502
J0.34(−0.62,1.30)0.4822797
T0.33(−0.66,1.31)0.5172797
K1.10(−0.02,2.23)0.0542797
Other European−0.15(−1.37,1.07)0.8092797
Height (cm) (~17 years old, puberty questionnaire)U0.11(−0.74,0.95)0.80225670.846
J0.61(−0.28,1.51)0.1792567
T−0.03(−0.94,0.88)0.9442567
K0.18(−0.84,1.19)0.7292567
Other European−0.06(−1.19,1.07)0.9182567
BMD (g/cm2) (~9 years old)U−0.002(−0.006,0.003)0.48249100.691
J−0.0005(−0.005,0.004)0.8454910
T0.003(−0.002,0.008)0.1824910
K0.001(−0.004,0.006)0.6224910
Other European0.0002(−0.006,0.006)0.9394910
BMD (g/cm2) (~11 years old)U−0.002(−0.007,0.003)0.46146990.498
J0.001(−0.005,0.007)0.7404699
T0.004(−0.002,0.010)0.1594699
K0.004(−0.003,0.010)0.2524699
Other European0.002(−0.006,0.009)0.6564699
BMD (g/cm2 (~13 years old)U−0.003(−0.010,0.005)0.48939030.635
J0.004(−0.004,0.012)0.3333903
T0.004(−0.004,0.013)0.2983903
K0.004(−0.005,0.013)0.3893903
Other European0.001(−0.009,0.011)0.8073903
BMD (g/cm2) (~15 years old)U−0.006(−0.015,0.002)0.14735500.206
J0.004(−0.005,0.012)0.4323550
T0.004(−0.005,0.013)0.3623550
K0.005(−0.005,0.015)0.3113550
Other European0.008(−0.004,0.019)0.1833550
BMD (g/cm2) (~17 years old)U−0.001(−0.001,0.008)0.88432190.866
J0.001(−0.009,0.010)0.8403219
T0.006(−0.004,0.016)0.2513219
K0.001(−0.010,0.011)0.8633219
Other European0.005(−0.008,0.017)0.4593219
B—VariableCladeOR95% CIp-ValueNmodel p-Value
YYP was breastfed as a babyU1.08(0.94,1.26)0.27663570.630
J1.13(0.96,1.31)0.1396357
T1.02(0.87,1.20)0.7836357
K1.01(0.85,1.20)0.8956357
Other European0.96(0.79,1.17)0.6726357
Frequency YP has a drink containing alcohol (~17 years old, questionnaire)U0.99(0.81,1.21)0.89731260.893
J1.08(0.88,1.35)0.4323126
T1.04(0.83,1.30)0.7543126
K1.08(0.85,1.38)0.5243126
Other European1.14(0.87,1.48)0.3593126
Frequency YP has a drink containing alcohol (~17 years old, clinic visit)U1.12(0.90,1.39)0.31426550.629
J1.21(0.96,1.52)0.1072655
T1.04(0.82,1.32)0.7612655
K1.00(0.78,1.28)0.9962655
Other European1.13(0.83,1.52)0.4542655
Frequency respondent has a drink containing alcohol (~19 years old)U0.94(0.74,1.20)0.61521360.862
J1.07(0.84,1.39)0.5722136
T0.96(0.73,1.26)0.7702136
K1.15(0.86,1.55)0.3372136
Other European1.04(0.76,1.45)0.8062136
Over the past year frequency YP had a drink containing alcohol (~21 years old)U1.06(0.86,1.30)0.57827010.568
J1.11(0.89,1.38)0.3852701
T1.11(0.87,1.40)0.4302701
K0.91(0.70,1.17)0.4772701
Other European1.23(0.91,1.67)0.1662701
Over the past year frequency YP had a drink containing alcohol (~23 years old)U0.87(0.70,1.08)0.20224990.710
J1.03(0.82,1.30)0.8042499
T0.98(0.77,1.25)0.8532499
K0.86(0.66,1.12)0.2572499
other European0.99(0.72,1.35)0.9352499
Frequency of physical activity during the past month (8–12 years old)U0.94(0.80,1.12)0.50755560.679
J0.97(0.81,1.16)0.7525556
T1.06(0.89,1.28)0.5005556
K1.12(0.91,1.36)0.2625556
Other European1.08(0.87,1.35)0.4745556
Frequency of physical activity during the past month (13–18 years old)U0.94(0.80,1.11)0.46055730.851
J0.92(0.78,1.08)0.3295573
T0.94(0.79,1.12)0.5055573
K1.04(0.86,1.26)0.6695573
Other European0.99(0.80,1.22)0.9245573
Frequency of physical activity during the past year (13–18 years old)U1.02(0.86,1.21)0.85445400.906
J1.03(0.86,1.23)0.7724540
T1.01(0.84,1.22)0.9044540
K1.14(0.92,1.39)0.2284540
Other European1.05(0.84,1.32)0.6464540
YP was breastfed as a babyU0.91(0.75,1.10)0.32463830.820
J0.92(0.75,1.12)0.3936383
T0.93(0.75,1.15)0.4986383
K1.03(0.81,1.30)0.8266383
Other European1.05(0.80,1.36)0.7406383
YP is a biological parent (21–23 years old)U0.55(0.31,0.98)0.04335630.205
J0.92(0.55,1.54)0.7603563
T0.94(0.56,1.59)0.8303563
K0.60(0.30,1.21)0.1553563
Other European1.31(0.73,2.35)0.3743563
YP ever took oral contraceptives (from 8 to 22 years old)U1.05(0.85,1.29)0.66534830.512
J1.13(0.91,1.41)0.2733483
T1.02(0.81,1.27)0.8863483
K1.16(0.90,1.49)0.2543483
Other European0.85(0.64,1.13)0.2523483
YP ever used non-oral hormonal contraceptives (from 17 to 22 years old)U1.32(0.95,1.82)0.09627090.529
J1.11(0.78,1.58)0.5722709
T0.94(0.64,1.38)0.7492709
K1.09(0.73,1.64)0.6592709
Other European0.85(0.51,1.42)0.5292709
Teenager has drunk alcohol (~13 years old)U1.09(0.88,1.35)0.43631170.817
J1.09(0.86,1.37)0.4843117
T0.96(0.75,1.22)0.7293117
K1.01(0.78,1.31)0.9453117
Other European0.88(0.65,1.19)0.4083117
YP has ever drunk alcohol (~17 years old)U1.00(0.64,1.57)0.99833160.962
J1.17(0.70,1.95)0.5493316
T1.21(0.71,2.07)0.4793316
K1.05(0.60,1.81)0.8713316
Other European0.92(0.52,1.62)0.7623316
YP ever smoked (from 14 to 23 years old)U1.16(0.98,1.38)0.08856150.262
J1.06(0.88,1.27)0.5645615
T1.06(0.88,1.28)0.5245615
K1.14(0.93,1.40)0.2105615
Other European0.88(0.70,1.10)0.2475615
YP ever had diabetes (from 11 to 23 years old)U3.50(1.22,10.06)0.02038950.257
J3.05(0.96,9.70)0.0583895
T1.92(0.49,7.48)0.3453895
K1.50(0.31,7.28)0.6143895
Other European2.07(0.42,10.08)0.3693895
YP ever had an X-ray (from 6 months to 15 years old)U1.04(0.83,1.30)0.72064640.158
J1.08(0.86,1.37)0.5076464
T1.00(0.78,1.28)0.9956464
K0.78(0.58,1.04)0.0936464
Other European1.31(0.99,1.73)0.0566464
1 Only girls in the analysis. 2 Natural logarithm of DHEAS levels. 3 Adjusted for gestational age, sex and 10 principal components. DHEAS: Dehydroepiandrosterone; BMD: Total bone mineral density; YP: Young person; DEXA: dual-energy X-ray absorptiometry.

Share and Cite

MDPI and ACS Style

Riley, V.; Erzurumluoglu, A.M.; Rodriguez, S.; Bonilla, C. Mitochondrial DNA Haplogroups and Breast Cancer Risk Factors in the Avon Longitudinal Study of Parents and Children (ALSPAC). Genes 2018, 9, 395. https://doi.org/10.3390/genes9080395

AMA Style

Riley V, Erzurumluoglu AM, Rodriguez S, Bonilla C. Mitochondrial DNA Haplogroups and Breast Cancer Risk Factors in the Avon Longitudinal Study of Parents and Children (ALSPAC). Genes. 2018; 9(8):395. https://doi.org/10.3390/genes9080395

Chicago/Turabian Style

Riley, Vivienne, A Mesut Erzurumluoglu, Santiago Rodriguez, and Carolina Bonilla. 2018. "Mitochondrial DNA Haplogroups and Breast Cancer Risk Factors in the Avon Longitudinal Study of Parents and Children (ALSPAC)" Genes 9, no. 8: 395. https://doi.org/10.3390/genes9080395

APA Style

Riley, V., Erzurumluoglu, A. M., Rodriguez, S., & Bonilla, C. (2018). Mitochondrial DNA Haplogroups and Breast Cancer Risk Factors in the Avon Longitudinal Study of Parents and Children (ALSPAC). Genes, 9(8), 395. https://doi.org/10.3390/genes9080395

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop