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Low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG) are modifiable risk factors for cardiovascular disease. Several genetic loci for predisposition to abnormal LDL-C, HDL-C and TG have been identified. However, it remains unclear whether these loci are consistently associated with serum lipid levels at each age or with unique developmental trajectories. Therefore, we assessed the association between genome wide association studies (GWAS) derived polygenic genetic risk scores and LDL-C, HDL-C, and triglyceride trajectories from childhood to adulthood using data available from the 27-year European 'Cardiovascular Risk in Young Finns' Study. For 2,442 participants, three weighted genetic risk scores (wGRSs) for HDL-C (38 SNPs), LDL-C (14 SNPs) and triglycerides (24 SNPs) were computed and tested for association with serum lipoprotein levels measured up to 8 times between 1980 and 2011. The categorical analyses revealed no clear divergence of blood lipid trajectories over time between wGRSs categories, with participants in the lower wGRS quartiles tending to have average lipoprotein concentrations 30 to 45% lower than those in the upper-quartile wGRS beginning at age 3 years and continuing through to age 49 years (where the upper-quartile wGRS have 4-7 more risk alleles than the lower wGRS group). Continuous analyses, however, revealed a significant but moderate time-dependent genetic interaction for HDL-C levels, with the association between HDL-C and the continuous HDL-C risk score weakening slightly with age. Conversely, in males, the association between the continuous TG genetic risk score and triglycerides levels tended to be lower in childhood and become more pronounced after the age of 25 years. Although the influence of genetic factors on age-specific lipoprotein values and developmental trajectories is complex, our data show that wGRSs are highly predictive of HDL-C, LDL-C, and triglyceride levels at all ages.

Generally, sigmoid curves are used to describe the growth of animals over their lifetime. However, because growth rates often differ over an animal's lifetime a single curve may not accurately capture the growth. Broken-stick models constrained to pass through a common point have been proposed to describe the different growth phases, but these are often unsatisfactory because essentially there are still two functions that describe the lifetime growth. To provide a single, converged model to age animals with disparate growth phases we developed a smoothly joining two-phase nonlinear function (SJ2P), tailored to provide a more accurate description of lifetime growth of the macropod, the Tasmanian pademelon Thylogale billardierii. The model consists of the Verhulst logistic function, which describes pouch-phase growth--joining smoothly to the Brody function, which describes post-pouch growth. Results from the model demonstrate that male pademelons grew faster and bigger than females. Our approach provides a practical means of ageing wild pademelons for life history studies but given the high variability of the data used to parametrise the second growth phase of the model, the accuracy of ageing of post-weaned animals is low: accuracy might be improved with collection of longitudinal growth data. This study provides a unique, first robust method that can be used to characterise growth over the lifespan of pademelons. The development of this method is relevant to collecting age-specific vital rates from commonly used wildlife management practices to provide crucial insights into the demographic behaviour of animal populations.