Smoking behavior is genetically determined?

Drug addiction is usually genetically determined by peculiar single nucleotide polymorphism. These mutations are clustered and associated to more frequent form of dependence. Given the status of smoking behavior that is often considered as a drug, scientists from the University of Missouri analyzed SNPs in large Caucasian population. They identified nine loci where the mutations of SNP were located. Furthermore, in African population they observed other two SNPs different from the first none above.

All these single nucleotide polymorphism’s were identified near to IL15 gene and were correlated with high parameters currently used to evaluate nicotine dependence. Male specificity was observed in both populations analyzed. IL15 is an important cytokine that controls the immune system and, in this case, seems strongly associated also with smoking habit. The molecular mechanism that SNPs use seemed to be related to gene regulation and expression because the most of mutations were identified in the binding site for transcription factors. Smoking is one of principal risk factor for lung cancer, so the determination of SNPs that could influence this behavior could allow to identify potential smokers into the population and, maybe, avoid that they start to smoke. This approach could decrease the number of smokers and, potentially, of lung cancer patients.

Genome-wide study on cardiovascular diseases

We have already talked about the importance of genome-wide analysis to study diseases and we have described some method for SNPs identification. In the current number of Nature Genetics an interesting study on cardiovascular diseases has been published. Co-authors belong to 95 institutions (hospitals, universities and research centers) in Europe and USA.

They analyzed the genotype of thousands patients and identified a correlation between high systolic pressure and mutations in limited region of the genome. In particular, they observed an association with the following eight genes: CYP17A1, CYP1A2, FGF5, SH2B3, MTHFR, c10orf107, ZNF652 and PLCD3.. This study has been confirmed by another work, always available in the same volume of Nature Genetics in which scientists identified four loci for systolic blood pressure (ATP2B1, CYP17A1, PLEKHA7, SH2B3), six for diastolic blood pressure (ATP2B1, CACNB2, CSK-ULK3, SH2B3, TBX3-TBX5, ULK4) and one for hypertension (ATP2B1). Why are so important these works? Firstly, these genes could be validated in further studies in order to understand if they could be considered as a target to prevent hypertension and develop new therapies for. Secondarily, this collaboration between so many institutions has to be a model for other studies about world-wide diseases.

References:
Nature Genetics 41, 667 – 676 (2009)
Nature Genetics 41, 677 – 687 (2009)

New method to quantify rare single-nucleotide polymorphism

Recent studies correlate rare allelic variants to many complex traits and combined effects of this deleterious mutations could explain susceptibility to many common diseases. To identify rare variants it’s necessary to genotype high number of individuals, by sequencing, or a pooled sample to minimize costs.

Druley and co-workers proposed a combination of molecular biology and computational analysis to achieve targeted resequencing and rare-variant detection. Procedure required PCR-amplification and sequencing with Illumina. They found that the first 12 bases of each Illumina read contained significantly fewer errors than later and, so, they used only this portion for their analysis.
They developed a new algorithm based on large deviation theory, named SNPSeeker. This program uses a second-order dependency error model for single-nucleotide polymorphism identification and considers the position of sequencing read (PCR cycle number) and the identity of two upstream bases. Thus, they improved the specificity of SNP calling and obtained results comparable to Sanger sequencing data, consistently reducing costs.
An important application of this method, is the combination of pooled-sample sequencing with genomic selection strategy to perform a more systematic survey of protein-coding DNA. This knowledge would be an important achievement for disease screening and tailoring risk-appropriate therapy.