The analysis of cffNA may allow NIPD for a variety of genetic conditions and may in future form part of national antenatal screening programmes for DS and other common genetic disorders.

Array-based comparative genomic hybridization is being increasingly used in patients with learning disability (mental retardation) and congenital anomalies. In this article, we update our previous meta-analysis evaluating the diagnostic and false-positive yields of this technology. An updated systematic review and meta-analysis was conducted investigating patients with learning disability and congenital anomalies in whom conventional cytogenetic analyses have proven negative. Nineteen studies (13,926 patients) were included of which 12 studies (13,464 patients) were published since our previous analysis. The overall diagnostic yield of causal abnormalities was 10% (95% confidence interval: 8 -12%). The overall number needed to test to identify an extra causal abnormality was 10 (95% confidence interval: 8 -13). The overall false-positive yield of noncausal abnormalities was 7% (95% confidence interval: 5-10%). This updated meta-analysis provides new evidence to support the use of array-based comparative genomic hybridization in investigating patients with learning disability and congenital anomalies in whom conventional cytogenetic tests have proven negative. However, given that this technology also identifies false positives at a similar rate to causal variants, caution in clinical practice should be advised. Genet Med 2009:11(3):139 -146.

Background:We modelled the efficiency of a personalised approach to screening for prostate and breast cancer based on age and polygenic risk-profile compared with the standard approach based on age alone.Methods:We compared the number of cases potentially detectable by screening in a population undergoing personalised screening with a population undergoing screening based on age alone. Polygenic disease risk was assumed to have a log-normal relative risk distribution predicted for the currently known prostate or breast cancer susceptibility variants (N=31 and N=18, respectively).Results:Compared with screening men based on age alone (aged 55–79: 10-year absolute risk ⩾2%), personalised screening of men age 45–79 at the same risk threshold would result in 16% fewer men being eligible for screening at a cost of 3% fewer screen-detectable cases, but with added benefit of detecting additional cases in younger men at high risk. Similarly, compared with screening women based on age alone (aged 47–79: 10-year absolute risk ⩾2.5%), personalised screening of women age 35–79 at the same risk threshold would result in 24% fewer women being eligible for screening at a cost of 14% fewer screen-detectable cases.Conclusion:Personalised screening approach could improve the efficiency of screening programmes. This has potential implications on informing public health policy on cancer screening.

These results are similar to those of the comparison studies, although the overall birth prevalence is higher in this study. This is probably due to the effects of ethnicity and consanguinity and increasing ascertainment. This study provides useful epidemiological information for those planning and providing services for patients with IMDs, including newborn screening, in the UK and similar populations.

Individual risk prediction and stratification based on polygenic profiling may be useful in disease prevention. Risk-stratified population screening based on multiple factors including a polygenic risk profile has the potential to be more efficient than age-stratified screening. In this article, we summarize the implications of personalized screening for breast and prostate cancers. We report the opinions of multidisciplinary international experts who have explored the scientific, ethical, and logistical aspects of stratified screening. We have identified (i) the need to recognize the benefits and harms of personalized screening as compared with existing screening methods, (ii) that the use of genetic data highlights complex ethical issues including discrimination against high-risk individuals by insurers and employers and patient autonomy in relation to genetic testing of minors, (iii) the need for transparency and clear communication about risk scores, about harms and benefits, and about reasons for inclusion and exclusion from the risk-based screening process, and (iv) the need to develop new professional competences and to assess cost-effectiveness and acceptability of stratified screening programs before implementation. We conclude that health professionals and stakeholders need to consider the implications of incorporating genetic information in intervention strategies for health-care planning in the future.Genet Med 2013:15(6):423–432