Nick Martin was a doctoral student of mine at the University of Birmingham in the mid 1970s. In this review, I discuss two of Nick’s earliest and most seminal contributions to the field of behavior genetics. First, Martin and Eaves’ (1977) extension of the model-fitting approach to multivariate data, which laid the theoretical groundwork for a generation of multivariate behavior genetic studies. Second, the Martin et al.’s (1978) manuscript on the power of the classical twin design, which showed that thousands of twin pairs would be required in order to reliably estimate components of variance, and has served as impetus for the formation of large-scale twin registries across the world. I discuss these contributions against the historical backdrop of a time when we and others were struggling with the challenge of figuring out how to incorporate gene-by-environment interaction, gene–environment correlation, mate selection and cultural transmission into more complex genetic models of human behavior.

We performed a candidate single-nucleotide polymorphism association study of cleft lip and palate in 1992 which earned more citations than it had subjects (N = 230).

In the course of twin studies whose main focus was elucidation of genetic and environmental factors on behavioral traits, many twin researchers became aware of the strong tendency for dizygotic (DZ) twinning to run in families. Over four decades, Nick Martin and others initiated hormone and ultrasound studies, performed segregation and pedigree analyses, tested candidate genes, carried out linkage projects in sister pairs and formed large collaborations to illuminate the genetics of DZ twinning by genome-wide association studies and meta-analysis. This article summarizes the early work on hormone and genetic studies and describes the meta-analyses that have at last met with success in finding the first genes that predispose to DZ twinning, which also appear to influence many other female reproductive traits.

Biomarkers diagnose, predict or assess the risk of disease, and studies of the effects of genetic variation on biomarker phenotypes in the general population complement studies on patients diagnosed with disease. This paper traces the evolution of studies on biomarker genetics over the past 40 years through examples drawn from the work of Professor Martin and his colleagues.

The author provides a personal perspective on Nick Martin’s contributions to behavioral genetics and his role in the workshops on statistical genetics held annually in Boulder. Highlighted are Prof. Martin’s seminal work on multivariate behavioral genetics, his career-long commitment to the value of the study of twins, and his enthusiastic support of the didactic mission of the ‘Boulder workshops’. These contributions and activities continue unabated as we celebrate Prof. Martin’s 70th birthday.

This short essay recounts the author’s interactions with Nick Martin in the years they both worked with Lindon Eaves at Virginia Commonwealth University. Although coming from very different academic traditions, they became close colleagues building their young careers together. Nick generously shared his statistical genetics expertise and the author taught Nick a thing or two about psychiatric illness.

The extended twin model is a unique design in the genetic epidemiology toolbox that allows to simultaneously estimate multiple causes of variation such as genetic and cultural transmission, genotype–environment covariance and assortative mating, among others. Nick Martin has played a key role in the conception of the model, the collection of substantially large data sets to test the model, the application of the model to a range of phenotypes, the publication of the results including cross-cultural comparisons, the evaluation of bias and power of the design and the further elaborations of the model, such as the children-of-twins design.

Dr Nick Martin has made enormous contributions to the field of behavior genetics over the past 50 years. Of his many seminal papers that have had a profound impact, we focus on his early work on the power of twin studies. He was among the first to recognize the importance of sample size calculation before conducting a study to ensure sufficient power to detect the effects of interest. The elegant approach he developed, based on the noncentral chi-squared distribution, has been adopted by subsequent researchers for other genetic study designs, and today remains a standard tool for power calculations in structural equation modeling and other areas of statistical analysis. The present brief article discusses the main aspects of his seminal paper, and how it led to subsequent developments, by him and others, as the field of behavior genetics evolved into the present era.

One of Nick’s key early achievements at QIMR was to establish a twin study on melanoma risk factors. The Brisbane Twin Nevus Study (BTNS) had an initial focus on nevus (mole) count in adolescents but, reflecting Nick’s broad interests, expanded in scope enormously over the decades. In the skin cancer arena, BTNS was essential to genetic discoveries in melanoma, eye color and pigmentation. Later studies amassed data on thousands of phenotypes, ranging from molecular phenotypes such as gene expression to studies where gene mapping findings in adolescents turned out to have translational potential in late-onset diseases. Nick’s twin data have formed the basis for an enormous range of discoveries, with Nick and his colleagues continuing to capitalize on these data.

This article describes Dr Nathan Gillespie’s PhD training and supervision under Professor Nick Martin and their ongoing collaborations. Drs Gillespie and Martin have collaborated on numerous biometrical genetic analyses applied to cross-sectional and longitudinal twin data, combined molecular and phenotypic modeling, as well as genomewide meta-analyses of psychoactive substance use and misuse. Dr Gillespie remains an active collaborator with Professor Martin, including ongoing data collection, analysis and publications related to the Brisbane Longitudinal Twin Study.

Blood cell concentrations for most cell types are highly heritable. Data from Nick Martin’s twin registry provided much of the data for the early heritability and linkage studies of blood cell related traits and have contributed significantly to more recent genomewide association studies that have successfully identified individual genetic loci.

This letter reflects on my collaborations with Nick Martin over the past 18 years. Working together we have applied twin-family and statistical genetics methods to examine the genetic architecture and identify genetic variants influencing a range of physical, psychological and social traits. The common thread across much of this work has been the empirical questions: Why are we the way we are and how can this knowledge help us when things go wrong?

Twins, data and emails. Some of the words that first come to mind when I think of Nick. Lots of twins. With lots of data. And short single-finger-typed emails. And great wine. Well, it works, there is no doubt. That’s how I ended up in Australia, working on asthma genetics.

Recounts how our collaboration with Nick Martin was shaped over two decades, leading to the first studies of predictions from the ‘Dual Route Cascaded’ computational model of reading in twins, and extending into the molecular work, first linkage, fine mapping of genes identified in pedigree studies, into now the genomewide association study era and the first polygenic risk scores for reading and their potential in early clarifying causality and validating interventions, as well as for future global collaborations in improving these predictors and identifying causal variants. We highlight Nick’s warm, future-focused optimism, support and inclusive approach without which none of this would have been possible. The circle of Nick asking, over half a century ago, ‘What genes do you think make some kids get better grades?’ has built a diverse scientific legacy involving thousands of papers and collaborations. The (heritable) traits of curiosity, boldness, warmth, interest in societally important questions, openness to new methods, ambition and collaborative skill to bring into being the infrastructure and samples needed for this research are rare, and we are grateful.

Mapping genetic risk factors for endometriosis continues from early studies on women’s health initiated by Nick Martin and Susan Treloar. Their initial recruitment of endometriosis cases and family members received a major boost and became a flagship project within the Cooperative Research Centre (CRC) for the Discovery of Common Human Disease. We extended the study through a formal collaboration with Professor Stephen Kennedy and his group in Oxford. Our first joint scientific meeting was held in Brisbane and was sadly memorable as the day the planes were flown into the Twin Towers in New York. Our initial collaboration expanded into the International Endometriosis Genetics Consortium (IEGC). The IEGC now has 15 groups around the world, and the most recent meta-analysis will be published this year.

This note reflects on my collaborations with Nick Martin and the GenEpi group over the past 20 years. Over the past two decades, our work together has focused on gene mapping and understanding the genetic architecture of a wide range of traits with particular foci on migraine and common baldness. Our migraine research has included latent class and twin analyses cumulating in genome-wide association analyses which had identified 44 (34 new) risk variants for migraine. Leveraging these results through polygenic risk score analyses identified subgroups of patients likely to respond to triptans (an acute migraine drug), providing the first step toward precision medicine in migraine [Kogelman et al. (2019) Neurology Genetics, 5, e364].

The classical twin design relies on a number of strong number of assumptions in order to yield unbiased estimates of heritability. This includes the equal environments assumption — that monozygotic and dizygotic twins experience similar degrees of environmental similarity — an assumption that is likely to be violated in practice for many traits of interest. An alternative method of estimating heritability that does not suffer from many of these limitations is to model trait similarity between sibling pairs as a function of their empirical genome-wide identity by descent sharing, estimated from genetic markers. In this review, I recount the story behind Nick Martin’s and my development of this method, our first attempts at applying it in a human population and more recent studies using the original and related methods to estimate trait heritability.

Nick Martin is a pioneer in recognizing the need for large sample size to study the complex, heterogeneous and polygenic disorders of common mental disorders. In the predigital era, questionnaires were mailed to thousands of twin pairs around Australia. Always quick to adopt new technology, Nick’s studies progressed to phone interviews and then online. Moreover, Nick was early to recognize the value of collecting DNA samples. As genotyping technologies improved over the years, these twin and family cohorts were used for linkage, candidate gene and genome-wide association studies. These cohorts have underpinned many analyses to disentangle the complex web of genetic and lifestyle factors associated with mental health. With characteristic foresight, Nick is chief investigator of our Australian Genetics of Depression Study, which has recruited 16,000 people with self-reported depression (plus DNA samples) over a time frame of a few months — analyses are currently ongoing. The mantra of sample size, sample size, sample size has guided Nick’s research over the last 30 years and continues to do so.

Nick Martin has had an outsized influence on the field of behavioral genetics. Much of this influence stems from his mentorship of young scientists. I describe Nick’s mentorship, and what makes it special, from a personal perspective.