Augustine Kong

Our Future Health is a prospective study aiming to recruit 5 million UK-resident adults to enable discovery and translation of disease prevention, detection and treatment approaches. So far, more than 2.5 million have enrolled, and baseline phenotypic data are available for >1.9 million participants. Here we provide an assessment of phenotypes—self-reported health-related behaviors, geolocation, diagnoses and medication, in- and outpatient visits, cancer registry and cause of death—and comparison of disease patterns against national estimates and the UK Biobank cohort. Sociodemographic, lifestyle and health-related characteristics reflected UK population patterns; all but one minority ethnic group and the most socioeconomically deprived groups were underrepresented. The prevalence of several major self-reported conditions, particularly mental health conditions such as depression and anxiety, was higher than national estimates and directionally concordant with the UK Biobank ( r = 0.78). Associations with known clinical correlates replicated across both cohorts ( r = 0.80). Medication-use patterns and cancer prevalence followed expected age-related gradients, with lower lung cancer rates than national data. As recruitment progresses, electronic health records can help specify disease patterns and systematically assess biases.

It is increasingly recognized that participation bias can pose problems for genetic studies. Recently, to overcome the challenge that genetic information of nonparticipants is unavailable, it is shown that by comparing the IBD (identity by descent) shared and not-shared segments between participating relative pairs, one can estimate the genetic component underlying participation. That, however, does not directly address how to adjust estimates of heritability and genetic correlation for phenotypes correlated with participation. Here, we demonstrate a way to do so by adopting a statistical framework that separates the genetic and nongenetic correlations between participation and these phenotypes. Crucially, our method avoids making the assumption that the effect of the genetic component underlying participation is manifested entirely through these other phenotypes. Applying the method to 12 UK Biobank phenotypes, we found eight that have significant genetic correlations with participation, including body mass index, educational attainment, and smoking status. For most of these phenotypes, without adjustments, estimates of heritability and the absolute value of genetic correlation would have underestimation biases.

Substantial shifts in reproductive behaviors have recently taken place in many high-income countries including earlier age at menarche, advanced age at childbearing, rising childlessness and a lower number of children. As reproduction shifts to later ages, genetic factors may become increasingly important. Although monogenic genetic effects are known, the genetics underlying human reproductive traits are complex, with both causal effects and statistical bias often confounded by socioeconomic factors. Here, we review genome-wide association studies (GWASs) of 44 reproductive traits of both female and male individuals from 2007 to early 2024, examining reproductive behavior, reproductive lifespan and aging, infertility and hormonal concentration. Using the GWAS Catalog as a basis, from 159 relevant studies, we isolate 37 genes that harbor association signals for four or more reproductive traits, more than half of which are linked to rare Mendelian disorders, including ten genes linked to reproductive-related disorders: FSHB, MCM8, DNAH2, WNT4, ESR1, IGSF1, THRB, BRWD1, CYP19A1 and PTPRF. We also review the relationship of reproductive genetics to related health and behavioral traits, aging and longevity and the effect of parental age on offspring outcomes as well as reflecting on limitations, open questions and challenges in this fast-moving field.

The trait of participating in a genetic study probably has a genetic component. Identifying this component is difficult as we cannot compare genetic information of participants with nonparticipants directly, the latter being unavailable. Here, we show that alleles that are more common in participants than nonparticipants would be further enriched in genetic segments shared by two related participants. Genome-wide analysis was performed by comparing allele frequencies in shared and not-shared genetic segments of first-degree relative pairs of the UK Biobank. In nonoverlapping samples, a polygenic score constructed from that analysis is significantly associated with educational attainment, body mass index and being invited to a dietary study. The estimated correlation between the genetic components underlying participation in UK Biobank and educational attainment is estimated to be 36.6%—substantial but far from total. Taking participation behaviour into account would improve the analyses of the study data, including those of health traits.

Effects estimated by genome-wide association studies (GWASs) include effects of alleles in an individual on that individual (direct genetic effects), indirect genetic effects (for example, effects of alleles in parents on offspring through the environment) and bias from confounding. Within-family genetic variation is random, enabling unbiased estimation of direct genetic effects when parents are genotyped. However, parental genotypes are often missing. We introduce a method that imputes missing parental genotypes and estimates direct genetic effects. Our method, implemented in the software package snipar (single-nucleotide imputation of parents), gives more precise estimates of direct genetic effects than existing approaches. Using 39,614 individuals from the UK Biobank with at least one genotyped sibling/parent, we estimate the correlation between direct genetic effects and effects from standard GWASs for nine phenotypes, including educational attainment (r = 0.739, standard error (s.e.) = 0.086) and cognitive ability (r = 0.490, s.e. = 0.086). Our results demonstrate substantial confounding bias in standard GWASs for some phenotypes.

Participation in a genetic study likely has a genetic component. Identifying such component is difficult as we cannot compare genetic information of participants with non-participants directly, the latter being unavailable. Here, we show that alleles that are more common in participants than non-participants would be further enriched in genetic segments shared by two related participants. Genome-wide analysis was performed by comparing allele frequencies in shared and not-shared genetic segments of first-degree relative pairs of the UK Biobank. A polygenic score constructed from that analysis, in non-overlapping samples, is associated with educational attainment ( P = 2.1 × 10 ⁻⁵² ), body mass index ( P = 1.5 × 10 ⁻¹⁹ ), and participation in a dietary study ( P = 6.9 × 10 ⁻²¹ ). Further analysis shows that inclination to participate is a behavioural trait in its own right, and not simply a consequence of other established phenotypes. Understanding the basis of this trait is important for data analyses and the design of future surveys, genetic or otherwise.