While optimizing the analysis strategy for a 500,000 SNP Affymetrix array set, I found 6 autosomal SNPs that show highly significant sex-dependent allele differences: rs2809868, rs4862188, rs2880301, rs3883011, rs3883013 and rs3883014.
Sure, there could be autosomal marker that influences male/female outcome but there is a more likely explanation: All SNPs have paralogue sequence stretches on the Y chromosome that are co-amplified during PCR. From the initial genotyping results it is most likely that only the Y chromosomal stretch is being mutated in SNP 4, 13 and 15.2.
These SNPs are perfect sex marker, as they include an autosomal control allele (in comparison to pure Y markers like SNPs in SRY). They are always unambiguous (in contrast to pure X marker where only heterocygotes are informative).
They even offer advantage to commercial STR kits of the Amelogenin/Amely gene situated (in the Y parautosomal region) as they would not be affected by excess homologous X chromosomal material as often found in forensic situations. In addition, they might overcome some other weakness of the Amelogenin test where a second assay is usually recommended.
If you will ever see a case-control study that is highlighting any of these SNPs, you can be sure that this study had a distorted male-female ratio between case and controls.
The AJHG preprint server has an important paper about the effect of rare missense alleles. By combining information from HGMD, human – chimpanzee divergence and 4 other datasets (NIEHS-EGP, Seattle SNPs, JSNP and a resequencing approach of 58 genes in >1,500 chromosomes) they attack the Chakravarti hypothesis of “common diseases – commmon variants”
It remains uncertain why such polymorphisms can persist without being eliminated by purifying selection. Currently, two major lines of reasoning exist that explain this apparent paradox. The first considers various complex evolutionary scenarios and treats positive or balancing selection as a major force that can drive medically detrimental mutations to high frequencies. The second line of reasoning postulates a high mutation rate as a major factor that determines the cumulative frequency of detrimental polymorphisms in the population.
Anyway, here is the main outcome
The November AJHG has an excellent re-analysis of the dysbindin-schizophrenia association using new methodology that surpasses all previous meta-analysis techniques. As the single SNP association results from the previous 6 studies cannot be directly compared, they construct a European super-hap map from all tag SNPs in that region, place them in a phylogenetic tree before finally mapping all single associations on these haplotypes. Their Fig.1B show the main results; as the circles in Fig.1B are somewhat confusing, I have withdrawn their results – adding the haplotype frequencies and ordering the studies by year of publication.
We may think of a triple-blind study – neither patients, nor PIs, nor we did know anything before. The results are alarming. I do not understand how the Kirov set could have included all haplotypes and why the Schwab/Williams set is in opposition to the Straub/Bogaert/Funke set.
What could have gone wrong? The authors of the current re-analysis believe that population differences are an unlikely reason for the inconsistency as the allele frequencies match between studies. Good news that genotyping errors may be largely excluded.
Unfortunately the authors remain vague why there is no common causal variant. Have there been different sampling schemes, different diagnostic thresholds, different environmental exposures in the previous studies? Is dysbindin at all a schizophrenia gene, or only under a certain genetic background? It seems possible that studies of one branch are false positives. Or is the haplotype reconstruction in the re-analysis erroneous for whatever reasons?
Von Münchhausen is well know for escaping from a swamp by pulling himself up by his own hair. I would like I could do that too.
A new study of 12 Mb DNA sequence in 927 individuals representing 52 populations now finds good portability of of tag SNPs between the 4 hapmap groups and any of the 52 populations (except some African populations like the Mandenka, Bantu, Yoruba, Biaka Pygmy, Mbuti Pygmy and San). The paper has some exceptional well done graphics – and I am quite happy that the resolution of European nations leaves some gaps for our forthcoming ECRHS papers (a poster had already been on display at the 3rd Annual International HapMap Project in Cambridge, Massachusetts).
“Die Botschaft hörâ€™ ich wohl, allein mir fehlt der Glaube” (Goethe, “I hear the message well…”). The usefulness of tagSNPs in disease association studies still remains to be shown (I still renember comments like cr.. map). At present I neither believe in rare variants nor in common common variants but a permanent reshuffling of rare, frequent and highly abundant variants. Yea, yea.