No abstract

Background: Pre-analytical conditions are key factors in maintaining the high quality of biospecimens. They are necessary for accurate reproducibility of experiments in the field of biomarker discovery as well as achieving optimal specificity of laboratory tests for clinical diagnosis. In research at the National Biobank of Korea, we evaluated the impact of pre-analytical conditions on the stability of biobanked blood samples by measuring biochemical analytes commonly used in clinical laboratory tests. Methods: We measured 10 routine laboratory analytes in serum and plasma samples from healthy donors (n = 50) with a chemistry autoanalyzer (Hitachi 7600-110). The analyte measurements were made at different time courses based on delay of blood fractionation, freezing delay of fractionated serum and plasma samples, and at different cycles (0, 1, 3, 6, 9) of freeze-thawing. Statistically significant changes from the reference sample mean were determined using the repeated-measures ANOVA and the significant change limit (SCL). Results: The serum levels of GGT and LDH were changed significantly depending on both the time interval between blood collection and fractionation and the time interval between fractionation and freezing of serum and plasma samples. The glucose level was most sensitive only to the elapsed time between blood collection and centrifugation for blood fractionation. Based on these findings, a simple formula (glucose decrease by 1.387 mg/ dL per hour) was derived to estimate the length of time delay after blood collection. In addition, AST, BUN, GGT, and LDH showed sensitive responses to repeated freeze-thaw cycles of serum and plasma samples. Conclusion: These results suggest that GGT and LDH measurements can be used as quality control markers for certain pre-analytical conditions (eg, delayed processing or repeated freeze-thawing) of blood samples which are either directly used in the laboratory tests or stored for future research in the biobank.

Many biobanks were established as biorepositories for biomedical research, and a number of biobanks were founded in the 1990s. The main aim of the biobank is to store and to maintain biomaterials for studying chronic disease, identifying risk factors of specific diseases, and applying personalized drug therapies. This report provides a review of biobanks, including Korean biobanks and an analysis of sample volumes, regulations, policies, and ethical issues of the biobank. Until now, the top 6 countries according to the number of large-scale biobanks are the United Kingdom, United States, Sweden, France, the Netherlands, and Italy, and there is one major National Biobank of Korea (NBK) and 17 regional biobanks in Korea. Many countries have regulations and guidelines for the biobanks, and the importance of good management of biobanks is increasing. Meanwhile, according to a first survey of 456 biobank managers in the United States, biobankers are concerned with the underuse of the samples in their repositories, which need to be advertised for researchers. Korea Biobank Network (KBN) project phase II (2013-2015) was also planned for the promotion to use biospecimens in the KBN. The KBN is continuously introducing for researchers to use biospecimens in the biobank. An accreditation process can also be introduced for biobanks to harmonize collections and encourage use of biospecimens in the biobanks. KBN is preparing an on-line application system for the distribution of biospecimens and a biobank accreditation program and is trying to harmonize the biobanks.

For a family-based sample, the phenotypic variance-covariance matrix can be parameterized to include the variance of a polygenic effect that has then been estimated using a variance component analysis. However, with the advent of large-scale genomic data, the genetic relationship matrix (GRM) can be estimated and can be utilized to parameterize the variance of a polygenic effect for population-based samples. Therefore narrow sense heritability, which is both population and trait specific, can be estimated with both population- and family-based samples. In this study we estimate heritability from both family-based and population-based samples, collected in Korea, and the heritability estimates from the pooled samples were, for height, 0.60; body mass index (BMI), 0.32; log-transformed triglycerides (log TG), 0.24; total cholesterol (TCHL), 0.30; high-density lipoprotein (HDL), 0.38; low-density lipoprotein (LDL), 0.29; systolic blood pressure (SBP), 0.23; and diastolic blood pressure (DBP), 0.24. Furthermore, we found differences in how heritability is estimated—in particular the amount of variance attributable to common environment in twins can be substantial—which indicates heritability estimates should be interpreted with caution.