single-nucleotide polymorphism  protein kinase A  health risk factor  cardiac dysfunction   morbidity

ast efforts to identify susceptibility genes for common diseases have considered association studies using markers of candidate genes in samples of cases and controls or linkage analyses using markers of genome variation in samples of pedigrees. Both of these approaches have critical limitations. Candidate gene studies are generally limited to a small number of genes already suggested to be involved in the disease pathway. They provide little opportunity for novel gene discovery in a disease field. Linkage analyses to study common diseases have proven to be very expensive, and in very few cases have they resulted in the identification of a disease suscepti- bility gene. The shared public and private efforts to identify and validate single-nucleotide polymorphisms (SNPs) genome-wide (1) and improvements in SNP measurement technologies have en- hanced the feasibility of genome-wide association studies. Such studies are expected to be much more powerful than traditional linkage analysis methods for identifying genes involved in common diseases (2).

Rather than searching for genes based on their influence in a specific disease, our aim is to screen for susceptibility genes that have a significant influence on late-onset diseases in the general population. Instead of considering clinical samples for a conven- tional case-control study, we chose to sample healthy individuals ascertained through blood donation centers. Our hypothesis is that older individuals carrying disease susceptibility alleles are more

A follow-up study of the SNPs significantly associated with age in the genome scan was carried out in a sample of 417 white twin

Abbreviations:D-AKAP2,dual-specificAkinase-anchoringprotein2;SNP,single-nucleotide polymorphism; AKB, A kinase binding.

Table 1. Summary of the blood donor collection stratified by gender

Table 2. Composition of age-, gender-, and ethnicity-stratified groups

Group Gender n Age range, years Mean age (SD) EA-YF Female 276 18–39 27.0 (6.68) EA-YM Male 276 18–39 27.1 (6.65) EA-OF Female 184 60–69 64.1 (2.84) EA-OM Male 367 60–79 66.7 (4.78) HI-YF Female 359 18–39 29.0 (6.42) HI-YM Male 173 18–39 28.9 (6.67) HI-OF Female 61 50–78 55.6 (5.66) HI-OM Male 64 50–89 57.9 (7.49) AF Female(cid:1)male 97(cid:1)97 18–76 37.4 (11.49) AS Female(cid:1)male 62(cid:1)64 18–65 34.4 (12.18)

Female (n   6,202) Male (n  4,766) Age, years 39.8 (13.7)* 43.2 (14.5)* 18–39 0.53 (3,256)† 0.44 (2115) 40–59 0.39 (2,394) 0.41 (1,953) (cid:2)60 0.09 (550) 0.15 (698) Ethnicity African 0.03 (167) 0.02 (108) Asian 0.01 (92) 0.02 (94) European 0.74 (4,582) 0.77 (3,687) Hispanic 0.12 (770) 0.10 (470) Other 0.10 (591) 0.09 (407) Donor disease 0.06 (364) 0.06 (292)

EA, European American; HI, Hispanic American; AF, African American; AS, Asian American; YF, young female; YM, young male; OF, old female; OM, old

*Age is summarized by the mean and standard deviation (in parentheses). †All categorical variables are summarized as the column proportion and count (in parentheses).

collection. The analysis was conducted by using a quantitative transmission-disequilibrium test as described by Abecasis and col- leagues (10) to take advantage of the twin-based sample and to controlforadmixtureandothernongeneticsourcesofvariation.We implemented the form of the test that does not require the estimation of variance components. In our experience with this method, the inclusion of variance components usually has little effect on the resulting inferences. We did not use formal statistical procedures to account for multiple testing, but report the distribu- tion of the resulting P values.

pairs from the adult twin registry at St. Thomas Hospital (London). Participantsinthiscollectionwereenrolledwithoutregardtohealth status as described (6). For this study, 97 traits were selected to explore possible disease associations. The selected traits have connections to many disease areas, including cardiovascular dis- eases, diabetes, hypertension, obesity, and osteoporosis.

DNA Extraction and Genotyping. DNA from 10 ml of blood from each blood donor was extracted by using a desalting method (Gentra Systems), and quantitated fluorimetrically (Fluoroskan Ascent CF, Labsystems, Chicago) by using Pico green. Age-, gender-, and ethnicity-specific pools were generated by pooling equimolar amounts of each DNA sample to a final concentration g/l. of 5 ng l. All PCR and MassEXTEND reactions were conducted by using standard conditions, and both alleles were analyzed to calculate allele frequencies as described elsewhere (7–9).

Specific A-Kinase-Anchoring Protein 2 (D-AKAP2). Orthologous se- quences were identified with PSI-BLAST (11) against the NCBI nonredundant database by using mouse D-AKAP2 (SwissProt O88845) as query sequence and a BLOSUM62 weight matrix (12). Sequences unequivocally identified as orthologues from Homo sapiens (SwissProt O43572), Sus scrofa (SwissProt P57770), Drosophila melanogaster (SW:Q9V3I3), and Caenorhabditis elegans (SwissProt Q10955) were obtained from SwissProt. Partial sequences from Anopheles gambiae (GenBank accession no. AJ281189) and Gallus gallus (GenBank accession no. AJ398916) were derived from GenBank EST sequences.

Statistical Analysis. Estimates of allele frequencies derived from pooled DNA were based on independent mass spectrometry mea- surements of four analyte aliquots derived from a single PCR reaction (8, 9). The median standard deviation for these values was awe 0.01. For comparing allele frequencies between the young and old pools, females and males were analyzed separately. The statistic used to test the difference in allele frequencies between pools was of the form