Genome-Wide SNP Analysis for Morbidity Gene Discovery. The

tations for conducting genome-wide association studies by geno- typing each subject with a dense panel of genetic markers include high costs, use of relatively large amounts of DNA and the extensive time requirements for such an endeavor. We have pursued a strategy that utilizes estimates of allele frequencies in DNA pools tobeabletoscreenlargenumbersofSNPsinaquickandaffordable manner (8, 9, 15). To apply this to disease susceptibility gene discovery, we divided the European-American individuals by age (under 40 and over 60 years) and by sex (Table 2). The fraction of included subjects reporting any health problem was only 1.8% and 2.9%inyoungfemalesandmales,and3.8%and3.5%inoldfemales and males, respectively. In this study, we used a collection of 6,500   exonic SNPs located in 5,000 genes or EST clusters. The majority ofassaysfortheseSNPs,originallyidentifiedinan insilicodiscovery project, were developed in collaboration with the National Cancer Institute (7).

D-AKAP2 codes for dual-specific A-kinase anchor protein 2, which is part of a family of scaffold proteins known as A-kinase anchoring proteins (AKAPs) (13, 18). AKAPs bind the regulatory subunit of cAMP-dependent PKA, and target the kinase to various intracellular locations, localizing cAMP-mediated activation of the kinase (19). PKA is a broad specificity kinase and phosphorylates numerousproteinsthatfunctioninmanyessentialcellularprocesses such as metabolism, gene transcription, cell division, and neuronal transmission. In the inactive state, PKA is a tetramer consisting of two catalytic (C) and two regulatory (R) subunits. The dual specificity of D-AKAP2 is defined by its ability to bind both the RI

There is some concern with the strategy of using samples collected through blood donation centers in a rapidly growing, highly mobile and ethnically diverse population such as Southern California to compare allele frequencies between young and old groups. One concern is potential inaccuracies in the self-reporting of ethnic or geographic background, particularly age-dependent biases. Another concern is that the older and younger generations may represent different compositions of geographic heritage. Such confounders could lead to differences in allele frequencies for reasons unrelated to health and lead to false inferences. To address these concerns, we obtained estimates of allele frequencies from a subset of 271 markers randomly selected from the larger SNP panel in the four European-American pools, as well as pools of subjects of reported African-American, Asian-American, and Hispanic- American ethnicity (described in Table 2). We used these estimates to calculate Nei’s genetic distance between pools (16), which is illustrated in the neighbor-joining tree (17) in Fig. 1. The lengths of the branches are proportional to the pairwise genetic distances between pools. The distances between the old and young groups of European Americans are roughly equal and relatively small com- pared with the distances between the three other ethnic pools. This argues against the role of age-dependent population admixture as the explanation for the differences in allele frequencies observed between young and old groups.

 An A 3 G polymorphism in the 3 UTR of D-AKAP2 showed   num>< ( a significant decrease of 8% (P 0.01) of the G allele in the older sample of both genders (Table 3). The marker was individually genotyped and the frequency differences between young and old individuals calculated from the genotypes were very similar to the pool results. There was a slight skewing of frequencies in the pools likely resulting from uneven PCR amplification of the two alleles. This led to an underestimation of the G allele frequency in all pools butdidnotimpactthesignificanceofthedifferencesbetweenyoung and old (Table 3).

To identify common polymorphisms in the D-AKAP2 gene, we sequenced the 15 exons and 100–200 bp of the flanking sequence (Fig. 4, which is published as supporting information on the PNAS website, www.pnas.org). The analysis of 36 chromosomes revealed only two additional polymorphisms: An A 3 G SNP in intron 10, six nucleotides downstream from exon 10 (In10), and an A 3 G SNP in exon 14, leading to an amino acid substitution Ile to Val at position 646 (I646V). Individual genotyping of the European- American samples showed that the intron 10 SNP exhibits no morbidity-association. The I646V polymorphism, however, was found to be significantly different between young and old in both   ait tM> males (P 0.03) and females (P 0.009) (Table 3). There was no significant difference between young males and females and be-

Table 3. Comparison of allele frequencies between young and old groups

Mouse ChickenDrosophilaA. gambiaeG. elegansAKB inVQOGNTDEAQEELAWKTAKMIVSIXMOQAHHDOPLEKSTKI, © MOUSE-V24 wane nnennennnnennnnene ne 2g OST Mov Mot Hut MeyostProteins   Cc Tle Val136nM+420 48nM481.7nM+04  22nM+0.413 nM +3 13nM+3Fraction Bound 40066405 400 4205 10 15 20 25 30Log Protein (nM)

Young Old Difference P value Pool data 3(cid:8) UTR European female 0.322 0.240 0.082 0.007 European male 0.362 0.286 0.076 0.002 Genotype data 3(cid:8) UTR European female 0.274 0.212 0.062 0.034 European male 0.304 0.232 0.072 0.004 I646V European female 0.402 0.318 0.084 0.009 European male 0.429 0.369 0.060 0.030 Hispanic female 0.445 0.316 0.129 0.008 Hispanic male 0.436 0.375 0.061 0.229 In10 European female 0.877 0.871 0.006 0.147 European male 0.880 0.865 0.015 0.360

Sample sizes are the same as those of the corresponding groups in Table frequencies for each SNP are given for the G allele.