AZFc deletions and spermatogenic failure: a population-based survey of 20,000 Y chromosomes.

Deletions involving the Y chromosome's AZFc region are the most common known genetic cause of severe spermatogenic failure (SSF). Six recurrent interstitial deletions affecting the region have been reported, but their population genetics are largely unexplored. We assessed the deletions' prevalence in 20,884 men in five populations and found four of the six deletions (presented here in descending order of prevalence): gr/gr, b2/b3, b1/b3, and b2/b4. One of every 27 men carried one of these four deletions. The 1.6 Mb gr/gr deletion, found in one of every 41 men, almost doubles the risk of SSF and accounts for ∼2% of SSF, although <2% of men with the deletion are affected. The 1.8 Mb b2/b3 deletion, found in one of every 90 men, does not appear to be a risk factor for SSF. The 1.6 Mb b1/b3 deletion, found in one of every 994 men, appears to increase the risk of SSF by a factor of 2.5, although <2% of men with the deletion are affected, and it accounts for only 0.15% of SSF. The 3.5 Mb b2/b4 deletion, found in one of every 2,320 men, increases the risk of SSF 145 times and accounts for ∼6% of SSF; the observed prevalence should approximate the rate at which the deletion arises anew in each generation. We conclude that a single rare variant of major effect (the b2/b4 deletion) and a single common variant of modest effect (the gr/gr deletion) are largely responsible for the AZFc region's contribution to SSF in the population.

Estrogen receptor alpha gene variation is associated with risk of myocardial infarction in more than seven thousand men from five cohorts.

Understanding the mechanisms by which estrogens affect cardiovascular disease risk, including the role of variation in the gene for estrogen receptor alpha (ESR1), may be key to new treatment strategies. We investigated whether the CC genotype at ESR1 c.454-397T>C is associated with increased risk among men. Study of more than 7000 whites in 5 cohorts from 4 countries provided evidence that genotype CC, present in roughly 20% of individuals, is a risk factor for nonfatal acute myocardial infarction (odds ratio=1.44; P<0.0001), after adjustment for established cardiovascular risk factors. After exclusion of younger subjects from 2 cohorts, because of age interaction, the odds ratio increased (to 1.63).

A 100K genome-wide association scan for diabetes and related traits in the Framingham Heart Study: replication and integration with other genome-wide datasets.

OBJECTIVE: To use genome-wide fixed marker arrays and improved analytical tools to detect genetic associations with type 2 diabetes in a carefully phenotyped human sample. RESEARCH DESIGN AND METHODS: A total of 1,087 Framingham Heart Study (FHS) family members were genotyped on the Affymetrix 100K single nucleotide polymorphism (SNP) array and examined for association with incident diabetes and six diabetes-related quantitative traits. Quality control filters yielded 66,543 SNPs for association testing. We used two complementary SNP selection strategies (a "lowest P value" strategy and a "multiple related trait" strategy) to prioritize 763 SNPs for replication. We genotyped a subset of 150 SNPs in a nonoverlapping sample of 1,465 FHS unrelated subjects and examined all 763 SNPs for in silico replication in three other 100K and one 500K genome-wide association (GWA) datasets. RESULTS: We replicated associations of 13 SNPs with one or more traits in the FHS unrelated sample (16 expected under the null); none of them showed convincing in silico replication in 100K scans. Seventy-eight SNPs were nominally associated with diabetes in one other 100K GWA scan, and two (rs2863389 and rs7935082) in more than one. Twenty-five SNPs showed promising associations with diabetes-related traits in 500K GWA data; one of them (rs952635) replicated in FHS. Five previously reported associations were confirmed in our initial dataset. CONCLUSIONS: The FHS 100K GWA resource is useful for follow-up of genetic associations with diabetes-related quantitative traits. Discovery of new diabetes genes will require larger samples and a denser array combined with well-powered replication strategies.