Genome-wide association analysis on monozygotic twin-pairs offers a route to discovery of gene–environment interactions through testing for variability loci associated with sensitivity to individual environment/lifestyle. We present a genome-wide scan of loci associated with intra-pair differences in serum lipid and apolipoprotein levels. We report data for 1,720 monozygotic female twin-pairs from GenomEUtwin project with 2.5 million SNPs, imputed or genotyped, and measured serum lipid fractions for both twins. We found one locus associated with intra-pair differences in high-density lipoprotein cholesterol, rs2483058 in an intron of SRGAP2, where twins carrying the C allele are more sensitive to environmental factors (P = 3.98 × 10−8). We followed up the association in further genotyped monozygotic twins (N = 1,261), which showed a moderate association for the variant (P = 0.200, same direction of an effect). In addition, we report a new association on the level of apolipoprotein A-II (P = 4.03 × 10−8).

In our three-stage questionnaire study we investigated patterns of twin and familial aggregation of osteoarthritis (OA) for commonly affected joints. The baseline questionnaire study of the Finnish Twin Cohort was performed in 1975. In 1990, 4095 twin pairs of the same gender born 1930–1957 responded to a questionnaire and reported whether they had OA diagnosed by a physician. In 1996 both twins of 266 pairs of which at least one had reported OA in 1990 responded to a detailed questionnaire on joint-specific OA, including family history of OA. In male pairs shared (non-genetic) familial effects accounted for 37% of the total variance in liability to OA and unshared environmental effects for 63%. In female pairs additive gene effects explained 44% of the variance in liability to OA, and unshared environmental effects for 36%. Familial aggregation of finger and knee OA was clearly higher than that of hip OA. Twin-pair discordance for OA was, to some extent, associated with body-mass index, occupational loading and trauma. Our results indicate that genetic effects may be modulated by sex or by environmental factors distributed differently between men and women. Based on our joint-specific data finger and knee joints are the most optimal targets for studies of genetic factors predisposing to the development of OA.

Genome-wide linkage analysis using multiple traits and statistical software packages is a tedious process which requires a significant amount of manual file manipulation. Different linkage analysis programs require different input file formats, making the task of analyzing data with multiple methods even more time-consuming. We have developed a software tool, AUTOGSCAN, that automates file formatting, the running of statistical analyses, and the summarizing of resulting statistics for whole genome scans with a push of a button, using several independent, and often idiosyncratic, statistical software packages such as MERLIN, SOLAR and GENEHUNTER. We also describe a program, ANALYZE, designed to run qualitative linkage analysis with several different statistical strategies and programs to efficiently screen for linkage and linkage disequilibrium for a given discrete trait. The ANALYZE program can also be used by AUTOGSCAN in a genome-wide sense.

The amount of available DNA is often a limiting factor in pursuing genetic analyses of large-scale population cohorts. An association between higher DNA yield from blood and several phenotypes associated with inflammatory states has recently been demonstrated, suggesting that exclusion of samples with very low DNA yield may lead to biased results in statistical analyses. Whole genome amplification (WGA) could present a solution to the DNA concentration-dependent sample selection. The aim was to thoroughly assess WGA for samples with low DNA yield, using the multiply-primed rolling circle amplification method. Fifty-nine samples were selected with the lowest DNA yield (less than 7.5µg) among 799 samples obtained for one population cohort. The genotypes obtained from two replicate WGA samples and the original genomic DNA were compared by typing 24 single nucleotide polymorphisms (SNPs). Multiple genotype discrepancies were identified for 13 of the 59 samples. The largest portion of discrepancies was due to allele dropout in heterozygous genotypes in WGA samples. Pooling the WGA DNA replicates prior to genotyping markedly improved genotyping reproducibility for the samples, with only 7 discrepancies identified in 4 samples. The nature of discrepancies was mostly homozygote genotypes in the genomic DNA and heterozygote genotypes in the WGA sample, suggesting possible allele dropout in the genomic DNA sample due to very low amounts of DNA template. Thus, WGA is applicable for low DNA yield samples, especially if using pooled WGA samples. A higher rate of genotyping errors requires that increased attention be paid to genotyping quality control, and caution when interpreting results.

In this issue of Twin Research, we describe different facets of a European Community funded effort, GenomEUtwin, which capitalises on eight of the world's largest and best characterised twin registers and a multi-national population cohort, MORGAM. This international study, reaching beyond the geographical borders of Europe, is based on linkage and association strategies designed to identify genetic contributors to health and disease using integrated expertise of participating groups in genetics, epidemiology and biostatistics. By merging information from numerous epidemiological and genetic databases, GenomEUtwin will create an intellectual and technical infrastructure for future genetic epidemiological studies aiming to define genetic and life style risks for common human diseases.