The Early Growth Genetics (EGG) and EArly Genetics and Lifecourse Epidemiology (EAGLE) consortia : design, results and future prospects

The impact of many unfavorable childhood traits or diseases, such as low birth weight and mental disorders, is not limited to childhood and adolescence, as they are also associated with poor outcomes in adulthood, such as cardiovascular disease. Insight into the genetic etiology of childhood and adolescent traits and disorders may therefore provide new perspectives, not only on how to improve wellbeing during childhood, but also how to prevent later adverse outcomes. To achieve the sample sizes required for genetic research, the Early Growth Genetics (EGG) and EArly Genetics and Lifecourse Epidemiology (EAGLE) consortia were established. The majority of the participating cohorts are longitudinal population-based samples, but other cohorts with data on early childhood phenotypes are also involved. Cohorts often have a broad focus and collect(ed) data on various somatic and psychiatric traits as well as environmental factors. Genetic variants have been successfully identified for multiple traits, for example, birth weight, atopic dermatitis, childhood BMI, allergic sensitization, and pubertal growth. Furthermore, the results have shown that genetic factors also partly underlie the association with adult traits. As sample sizes are still increasing, it is expected that future analyses will identify additional variants. This, in combination with the development of innovative statistical methods, will provide detailed insight on the mechanisms underlying the transition from childhood to adult disorders. Both consortia welcome new collaborations. Policies and contact details are available from the corresponding authors of this manuscript and/or the consortium websites.Peer reviewe

Confounder adjusted multivariable and instrumental variable associations of alcohol with biomarkers of liver function in those who report some alcohol consumption (i.e. those reporting no consumption have been removed from these analyses).

<p>CI: confidence interval; ALT: alanine aminotransferase; γ-GT: γ-glutamyl-transferase; ALP: alkaline phosphatase; Prothrombin: Prothrombin action.</p><p>In the multivariable analysis all results are adjusted for age, gender, smoking, physical activity, education and income.</p><p>In the instrumental variable analysis the control function method was used with <i>ADH1B</i> and <i>ADH1C</i> used jointly as categorical (indicator) instrumental variables. The first stage F-statistic for all instrumental variable analyses = 34.</p>a<p>Test of null hypothesis that there is no difference in association of alcohol with each outcome between the confounder adjusted multivariable association (row 1) and the instrumental variable association using the control function (row 2); p-value obtained from the bootstrap distribution.</p><p>Confounder adjusted multivariable and instrumental variable associations of alcohol with biomarkers of liver function in those who report some alcohol consumption (i.e. those reporting no consumption have been removed from these analyses).</p

Association of <i>ADH1B</i> and <i>ADH1C</i> with alcohol consumption.

<p>OR: Odds ratio; CI: confidence intervals.</p>a<p>From a model of the mean risk difference of not drinking.</p><p>Association of <i>ADH1B</i> and <i>ADH1C</i> with alcohol consumption.</p

Confounder adjusted multivariable and instrumental variable associations of drinking versus not drinking alcohol with biomarkers for liver function.

<p>CI: confidence interval; ALT: alanine aminotransferase; γ-GT: γ-glutamyl-transferase; ALP: alkaline phosphatase; Prothrombin: Prothrombin action.</p><p>In the multivariable analysis all results are adjusted for age, gender, smoking, physical activity, education and income.</p><p>In the instrumental variable analysis the control function method was used with <i>ADH1B</i> and <i>ADH1C</i> used jointly as categorical (indicator) instrumental variables. The first stage F-statistic for all instrumental variable analyses = 21.</p>a<p>Test of null hypothesis that there is no difference in association of alcohol with each outcome between the confounder adjusted multivariable association (row 1) and the instrumental variable association using the control function (row 2); p-value obtained from the bootstrap distribution.</p><p>Confounder adjusted multivariable and instrumental variable associations of drinking versus not drinking alcohol with biomarkers for liver function.</p

Associations of observed confounders with <i>ADH1B</i> and <i>ADH1C</i> genotype.

<p><b>N = 58,313</b>.</p>a<p>F-statistic for continuous variables and chi-square for categorical variables testing the null hypothesis that distributions of the confounders do not differ by genotype (1 degree of freedom for <i>ADH1B</i> and 2 degrees of freedom for <i>ADH1C</i>).</p><p>MVPA = Moderate or vigorous physical activity. Kr = Danish kroner.</p><p>Associations of observed confounders with <i>ADH1B</i> and <i>ADH1C</i> genotype.</p

Evaluation of Common Type 2 Diabetes Risk Variants in a South Asian Population of Sri Lankan Descent

<div><p>Introduction</p><p>Most studies seeking common variant associations with type 2 diabetes (T2D) have focused on individuals of European ancestry. These discoveries need to be evaluated in other major ancestral groups, to understand ethnic differences in predisposition, and establish whether these contribute to variation in T2D prevalence and presentation. This study aims to establish whether common variants conferring T2D-risk in Europeans contribute to T2D-susceptibility in the South Asian population of Sri Lanka.</p><p>Methodology</p><p>Lead single nucleotide polymorphism (SNPs) at 37 T2D-risk loci attaining genome-wide significance in Europeans were genotyped in 878 T2D cases and 1523 normoglycaemic controls from Sri Lanka. Association testing was performed by logistic regression adjusting for age and sex and by the Cochran-Mantel-Haenszel test after stratifying according to self-identified ethnolinguistic subgroup. A weighted genetic risk score was generated to examine the combined effect of these SNPs on T2D-risk in the Sri Lankan population.</p><p>Results</p><p>Of the 36 SNPs passing quality control, sixteen showed nominal (p<0.05) association in Sri Lankan samples, fifteen of those directionally-consistent with the original signal. Overall, these association findings were robust to analyses that accounted for membership of ethnolinguistic subgroups. Overall, the odds ratios for 31 of the 36 SNPs were directionally-consistent with those observed in Europeans (p = 3.2×10⁻⁶). Allelic odds ratios and risk allele frequencies in Sri Lankan subjects were not systematically different to those reported in Europeans. Genetic risk score and risk of T2D were strongly related in Sri Lankans (per allele OR 1.10 [95%CI 1.08–1.13], p = 1.2×10⁻¹⁷).</p><p>Conclusion</p><p>Our data indicate that most T2D-risk variants identified in Europeans have similar effects in South Asians from Sri Lanka, and that systematic difference in common variant associations are unlikely to explain inter-ethnic differences in prevalence or presentation of T2D.</p></div

Individual SNP association results for T2D risk in the Sri Lankan case control samples.

<p>Chr: chromosome.</p

Comparison of risk allele frequencies (RAF) between South Asians from Sri Lanka samples included in this study (cases and controls combined) and previously reported RAF for Europeans (from HapMap).

<p>Comparison of risk allele frequencies (RAF) between South Asians from Sri Lanka samples included in this study (cases and controls combined) and previously reported RAF for Europeans (from HapMap).</p