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This paper proposes a rough-fuzzy inference system (RFIS) for detect the most susceptibility area to land degradation.
Most susceptibility loci confer only a small increase in risk, with their effects acting multiplicatively.
Despite such progress, the functions of the products of most susceptibility genes have not been fully elucidated.
Most susceptibility alleles probably have only a small direct effect [ 6], instead acting to determine future disease risk mainly by their interaction with environmental exposures [ 7].
Most susceptibility variants identified by GWAS in different cancers have low effect size (per-allele relative risks of 1.1 1.3) (Chung et al, 2010) and so the clinical utility of the individual variants in predicting future risk is limited.
An alternative view is that the frequency of most susceptibility SNPs in epilepsy could be so rare, or individually could make such small contributions toward raising the seizure threshold, that GWASs are not sensitive enough to detect them.
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But the Cavendish unfortunately has its own weaknesses – most prominently susceptibility to a disease called Black Sigatoka.
The quantitative and independent evaluation of the predictive capability of the models reveals that: (1) The most reliable susceptibility models are those derived from the nearest neighbour distance and sinkhole density.
Despite this progress, most psoriasis susceptibility variants have been located in intergenic or intronic regions, suggesting that such variants confer increased disease risk through their effects on gene regulation [26,28].
The most important susceptibility genes are PTPN2 and HLADR shared epitope (SE).
The imputation of genotypes in the most interesting susceptibility loci provided an opportunity to identify more strongly associated variants.
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