@article{10702,
  abstract     = {Background: Blood-based markers of cognitive functioning might provide an accessible way to track neurodegeneration years prior to clinical manifestation of cognitive impairment and dementia. Results: Using blood-based epigenome-wide analyses of general cognitive function, we show that individual differences in DNA methylation (DNAm) explain 35.0% of the variance in general cognitive function (g). A DNAm predictor explains ~4% of the variance, independently of a polygenic score, in two external cohorts. It also associates with circulating levels of neurology- and inflammation-related proteins, global brain imaging metrics, and regional cortical volumes. Conclusions: As sample sizes increase, the ability to assess cognitive function from DNAm data may be informative in settings where cognitive testing is unreliable or unavailable.},
  author       = {McCartney, Daniel L. and Hillary, Robert F. and Conole, Eleanor L.S. and Banos, Daniel Trejo and Gadd, Danni A. and Walker, Rosie M. and Nangle, Cliff and Flaig, Robin and Campbell, Archie and Murray, Alison D. and Maniega, Susana Muñoz and Valdés-Hernández, María Del C. and Harris, Mathew A. and Bastin, Mark E. and Wardlaw, Joanna M. and Harris, Sarah E. and Porteous, David J. and Tucker-Drob, Elliot M. and McIntosh, Andrew M. and Evans, Kathryn L. and Deary, Ian J. and Cox, Simon R. and Robinson, Matthew Richard and Marioni, Riccardo E.},
  issn         = {1474-760X},
  journal      = {Genome Biology},
  number       = {1},
  publisher    = {Springer Nature},
  title        = {{Blood-based epigenome-wide analyses of cognitive abilities}},
  doi          = {10.1186/s13059-021-02596-5},
  volume       = {23},
  year         = {2022},
}

@article{12142,
  abstract     = {Theory for liability-scale models of the underlying genetic basis of complex disease provides an important way to interpret, compare, and understand results generated from biological studies. In particular, through estimation of the liability-scale heritability (LSH), liability models facilitate an understanding and comparison of the relative importance of genetic and environmental risk factors that shape different clinically important disease outcomes. Increasingly, large-scale biobank studies that link genetic information to electronic health records, containing hundreds of disease diagnosis indicators that mostly occur infrequently within the sample, are becoming available. Here, we propose an extension of the existing liability-scale model theory suitable for estimating LSH in biobank studies of low-prevalence disease. In a simulation study, we find that our derived expression yields lower mean square error (MSE) and is less sensitive to prevalence misspecification as compared to previous transformations for diseases with  =< 2% population prevalence and LSH of =< 0.45, especially if the biobank sample prevalence is less than that of the wider population. Applying our expression to 13 diagnostic outcomes of  =< 3% prevalence in the UK Biobank study revealed important differences in LSH obtained from the different theoretical expressions that impact the conclusions made when comparing LSH across disease outcomes. This demonstrates the importance of careful consideration for estimation and prediction of low-prevalence disease outcomes and facilitates improved inference of the underlying genetic basis of  =< 2% population prevalence diseases, especially where biobank sample ascertainment results in a healthier sample population.},
  author       = {Ojavee, Sven E. and Kutalik, Zoltan and Robinson, Matthew Richard},
  issn         = {0002-9297},
  journal      = {The American Journal of Human Genetics},
  keywords     = {Genetics (clinical), Genetics},
  number       = {11},
  pages        = {2009--2017},
  publisher    = {Elsevier},
  title        = {{Liability-scale heritability estimation for biobank studies of low-prevalence disease}},
  doi          = {10.1016/j.ajhg.2022.09.011},
  volume       = {109},
  year         = {2022},
}

@article{8430,
  abstract     = {While recent advancements in computation and modelling have improved the analysis of complex traits, our understanding of the genetic basis of the time at symptom onset remains limited. Here, we develop a Bayesian approach (BayesW) that provides probabilistic inference of the genetic architecture of age-at-onset phenotypes in a sampling scheme that facilitates biobank-scale time-to-event analyses. We show in extensive simulation work the benefits BayesW provides in terms of number of discoveries, model performance and genomic prediction. In the UK Biobank, we find many thousands of common genomic regions underlying the age-at-onset of high blood pressure (HBP), cardiac disease (CAD), and type-2 diabetes (T2D), and for the genetic basis of onset reflecting the underlying genetic liability to disease. Age-at-menopause and age-at-menarche are also highly polygenic, but with higher variance contributed by low frequency variants. Genomic prediction into the Estonian Biobank data shows that BayesW gives higher prediction accuracy than other approaches.},
  author       = {Ojavee, Sven E and Kousathanas, Athanasios and Trejo Banos, Daniel and Orliac, Etienne J and Patxot, Marion and Lall, Kristi and Magi, Reedik and Fischer, Krista and Kutalik, Zoltan and Robinson, Matthew Richard},
  issn         = {20411723},
  journal      = {Nature Communications},
  number       = {1},
  publisher    = {Nature Research},
  title        = {{Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis}},
  doi          = {10.1038/s41467-021-22538-w},
  volume       = {12},
  year         = {2021},
}