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Background One of the kallikrein genes ( KLK3) encodes prostate-specific antigen, a key biomarker for prostate cancer. A number of factors, both genetic and non-genetic, determine variation of serum prostate-specific antigen concentrations in the population. We have recently found three KLK3 deletions in individuals with very low prostate-specific antigen concentrations, suggesting a link between abnormally reduced KLK3 expression and deletions of KLK3. Here, we aim to determine the frequency of kallikrein gene 3 deletions in the general population. Methods The frequency of KLK3 deletions in the general population was estimated from the 1958 Birth Cohort sample ( n = 3815) using amplification ratiometry control system. In silico analyses using PennCNV were carried out in the same cohort and in NBS-WTCCC2 in order to provide an independent estimation of the frequency of KLK3 deletions in the general population. Results Amplification ratiometry control system results from the 1958 cohort indicated a frequency of KLK3 deletions of 0.81% (3.98% following a less stringent calling criterion). From in silico analyses, we found that potential deletions harbouring the KLK3 gene occurred at rates of 2.13% (1958 Cohort, n = 2867) and 0.99% (NBS-WTCCC2, n = 2737), respectively. These results are in good agreement with our in vitro experiments. All deletions found were in heterozygosis. Conclusions We conclude that a number of individuals from the general population present KLK3 deletions in heterozygosis. Further studies are required in order to know if interpretation of low serum prostate-specific antigen concentrations in individuals with KLK3 deletions may offer false-negative assurances with consequences for prostate cancer screening, diagnosis and monitoring. [ABSTRACT FROM AUTHOR]

Consanguineous offspring have elevated levels of homozygosity. Autozygous stretches within their genome are likely to harbour loss of function (LoF) mutations which will lead to complete inactivation or dysfunction of genes. Studying consanguineous offspring with clinical phenotypes has been very useful for identifying disease causal mutations. However, at present, most of the genes in the human genome have no disorder associated with them or have unknown function. This is presumably mostly due to the fact that homozygous LoF variants are not observed in outbred populations which are the main focus of large sequencing projects. However, another reason may be that many genes in the genome-even when completely 'knocked out,' do not cause a distinct or defined phenotype. Here, we discuss the benefits and implications of studying consanguineous populations, as opposed to the traditional approach of analysing a subset of consanguineous families or individuals with disease. We suggest that studying consanguineous populations 'as a whole' can speed up the characterisation of novel gene functions as well as indicating nonessential genes and/or regions in the human genome. We also suggest designing a single nucleotide variant (SNV) array to make the process more efficient. [ABSTRACT FROM AUTHOR]

Recent technological advances have created challenges for geneticists and a need to adapt to a wide range of new bioinformatics tools and an expanding wealth of publicly available data (e.g., mutation databases, and software). This wide range of methods and a diversity of file formats used in sequence analysis is a significant issue, with a considerable amount of time spent before anyone can even attempt to analyse the genetic basis of human disorders. Another point to consider that is although many possess “just enough” knowledge to analyse their data, they do not make full use of the tools and databases that are available and also do not fully understand how their data was created. The primary aim of this review is to document some of the key approaches and provide an analysis schema to make the analysis process more efficient and reliable in the context of discovering highly penetrant causal mutations/genes. This review will also compare the methods used to identify highly penetrant variants when data is obtained from consanguineous individuals as opposed to nonconsanguineous; and when Mendelian disorders are analysed as opposed to common-complex disorders. [ABSTRACT FROM AUTHOR]

Summary: We present FATHMM-XF, a method for predicting pathogenic point mutations in the human genome. Drawing on an extensive feature set, FATHMM-XF outperforms competitors on benchmark tests, particularly in non-coding regions where the majority of pathogenic mutations are likely to be found. Availability and implementation: The FATHMM-XF web server is available at http://fathmm.bio compute.org.uk/fathmm-xf/, and as tracks on the Genome Tolerance Browser: http://gtb.biocom pute.org.uk. Predictions are provided for human genome version GRCh37/hg19. The data used for this project can be downloaded from: http://fathmm.biocompute.org.uk/fathmm-xf/. [ABSTRACT FROM AUTHOR]