Tobacco smoking continues to be a leading cause of preventable death. Recent research has underscored the important role of specific cholinergic nicotinic receptor subunit (CHRN) genes in risk for nicotine dependence and smoking. To detect and characterize the influence of genetic variation on vulnerability to nicotine dependence, we analyzed 226 SNPs covering the complete family of 16 CHRN genes, which encode the nicotinic acetylcholine receptor (nAChR) subunits, in a sample of 1,050 nicotine-dependent cases and 879 non-dependent controls of European descent. This expanded SNP coverage has extended and refined the findings of our previous largescale genome-wide association and candidate gene study. After correcting for the multiple tests across this gene family, we found significant association for two distinct loci in the CHRNA5-CHRNA3-CHRNB4 gene cluster, one locus in the CHRNB3-CHRNA6 gene cluster, and a fourth, novel locus in the CHRND-CHRNG gene cluster. The two distinct loci in CHRNA5-CHRNA3-CHRNB4 are represented by the nonsynonymous SNP rs16969968 in CHRNA5 and by rs578776 in CHRNA3, respectively, and joint analyses show that the associations at these two SNPs are statistically independent. Nominally significant single-SNP association was detected in CHRNA4 and CHRNB1. In summary, this is the most comprehensive study of the CHRN genes for involvement with nicotine dependence to date. Our analysis reveals significant 453Neuropsychiatric Genetics evidence for at least four distinct loci in the nicotinic receptor subunit genes that each influence the transition from smoking to nicotine dependence and may inform the development of improved smoking cessation treatments and prevention initiatives.

Genetic association studies have shown the importance of variants in the CHRNA5-CHRNA3-CHRNB4 cholinergic nicotinic receptor subunit gene cluster on chromosome 15q24-25.1 for the risk of nicotine dependence, smoking, and lung cancer in populations of European descent. We have carried out a detailed study of this region using dense genotyping in both European-Americans and African-Americans. We genotyped 75 known single nucleotide polymorphisms (SNPs) and one sequencing-discovered SNP in an African-American sample (N = 710) and in a European-American sample (N = 2,062). Cases were nicotine-dependent and controls were nondependent smokers. The nonsynonymous CHRNA5 SNP rs16969968 is the most significant SNP associated with nicotine dependence in the full sample of 2,772 subjects [P = 4.49 Â 10 À8

Nicotine dependence risk and lung cancer risk are associated with variants in a region of chromosome 15 encompassing genes encoding the nicotinic receptor subunits CHRNA5, CHRNA3 and CHRNB4. To identify potential biological mechanisms that underlie this risk, we tested for cis-acting eQTLs for CHRNA5, CHRNA3 and CHRNB4 in human brain. Using gene expression and disease association studies, we provide evidence that both nicotine-dependence risk and lung cancer risk are influenced by functional variation in CHRNA5. We demonstrated that the risk allele of rs16969968 primarily occurs on the low mRNA expression allele of CHRNA5. The non-risk allele at rs16969968 occurs on both high and low expression alleles tagged by rs588765 within CHRNA5. When the non-risk allele occurs on the background of low mRNA expression of CHRNA5, the risk for nicotine dependence and lung cancer is significantly lower compared to those with the higher mRNA expression. Together, these variants identify three levels of risk associated with CHRNA5. We conclude that there are at least two distinct mechanisms conferring risk for nicotine dependence and lung cancer: altered receptor function caused by a D398N amino acid variant in CHRNA5 (rs16969968) and variability in CHRNA5 mRNA expression.

Abstract.Conditional nonlinear optimal perturbation (CNOP) is proposed to study the predictability of numerical weather and climate prediction. A simple coupled oceanatmosphere model for ENSO is adopted as an example to show its applicability. In the case of climatological mean state being the basic state, it is shown that CNOP tends to evolve into El Niño or La Niña event more probably than linear singular vector (LSV) on the condition that CNOP and LSV are of the same magnitude of norm. CNOP is also employed to study the prediction error of El Niño and La Niña events. Comparisons between CNOP and LSV demonstrate that CNOP is more applicable in studying the predictability of the models governing the nonlinear motions of oceans and atmospheres.

[1] We used the approach of conditional nonlinear optimal perturbation (CNOP) to investigate optimal precursors for El Nino-Southern Oscillation (ENSO) events with a theoretical coupled ocean-atmosphere model. The CNOPs of the annual cycle of the coupled system were computed for different time periods, and the derived CNOPs were compared with the linear singular vectors (LSVs). The results show the existence of the CNOPs of annual cycle and local CNOPs. These CNOPs have the robust optimal patterns, which have opposite polarities in sea surface temperature and thermocline depth anomalies in the eastern equatorial Pacific. We demonstrate that the CNOP (local CNOP), rather than LSVs, has the highest likelihood to develop into an El Nino (La Nina) event; thus the CNOPs (local CNOPs) can be regarded as the optimal precursors for El Nino (La Nina) events. These optimal precursors agrees qualitatively well with the observations of period of 1980-2002. On the basis of the nonlinear oscillation described by the model, the physical mechanism of the optimal precursors for ENSO is discussed.

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Seasonal dependence of initial error growth for El Niño‐Southern Oscillation (ENSO) in Zebiak‐Cane model is investigated by using a new approach, i.e. conditional nonlinear optimal perturbation (CNOP). It is found that CNOP‐type error tends to have a significant season‐dependent evolution, and produces most considerable negative effects on the forecast results. Therefore, CNOPs are closely related to spring predictability barrier (SPB). On the other hand, some other kinds of initial errors, whose patterns are different from those of CNOPs, have also been found. Although the magnitudes of such initial errors are the same as those of CNOPs in terms of the chosen norm, they either show less prominent season‐dependent evolutions, or have trivial effect on the forecast results, and consequently do not yield SPB for El Niño events. The results of this investigation suggest that the CNOP‐type errors can be considered as one of candidate errors that cause the SPB. If data assimilation or (and) targeting observation approaches possess the function of filtering the CNOP‐type or (and) other similar errors, it is hopeful to improve the prediction skill of ENSO.

[1] Most state-of-the-art climate models have difficulty in the prediction of El Niño-Southern Oscillation (ENSO) starting from preboreal spring seasons. The causes of this spring predictability barrier (SPB) remain elusive. With a theoretical ENSO system model, we investigate this controversial issue by tracing the evolution of conditional nonlinear optimal perturbation (CNOP) and by analyzing the behavior of initial error growth. The CNOPs are the errors in the initial states of ENSO events, which have the biggest impact on the uncertainties at the prediction time under proper physical constraints. We show that the evolution of CNOP-type errors associated with El Niño episodes depends remarkably on season with the fastest growth occurring during boreal spring in the onset phase. There also exist other kinds of initial errors, which have either somewhat smaller growth rates or neutral ones during spring. However, for La Niña events, even if initial errors are of CNOP-type, the errors grow without significant seasonal dependence. These findings suggest that the SPB in this model results from combined effects of three factors: the annual cycle of the mean state, the structure of El Niño, and the pattern of the initial errors. On the basis of the error tendency equations derived from the model, we addressed how the combination of the three factors causes the SPB and proposed a mechanism responsible for the error growth in the model ENSO events. Our results help in clarifying the role of the initial error pattern in SPB, which may provide a clue for explaining why SPB can be eliminated by improving initial conditions. The results also illustrate a theoretical basis for improving data assimilation in ENSO prediction.