Medical genetics was revolutionized during the 1980s by the application of genetic mapping to locate the genes responsible for simple Mendelian diseases. Most diseases and traits, however, do not follow simple inheritance patterns. Geneticists have thus begun taking up the even greater challenge of the genetic dissection of complex traits. Four major approaches have been developed: linkage analysis, allele-sharing methods, association studies, and polygenic analysis of experimental crosses. This article synthesizes the current state of the genetic dissection of complex traits—describing the methods, limitations, and recent applications to biological problems.

Genetic Dissection of Complex Traits

Advances in whole-genome and whole-transcriptome amplification have permitted the sequencing of the minute amounts of DNA and RNA present in a single cell, offering a window into the extent and nature of genomic and transcriptomic heterogeneity which occurs in both normal development and disease. Single-cell approaches stand poised to revolutionise our capacity to understand the scale of genomic, epigenomic, and transcriptomic diversity that occurs during the lifetime of an individual organism. Here, we review the major technological and biological breakthroughs achieved, describe the remaining challenges to overcome, and provide a glimpse into the promise of recent and future developments.

/pdf/single-cell-genomics-advances-and-future-perspectives-v434p8yy4k.pdf

Single Cell Genomics: Advances and Future Perspectives

Cystic fibrosis

Objective
The objective of this study was to review the published literature on pregnancy termination following a prenatal diagnosis of Down syndrome in the United States

Method
A systematic search of US English-language articles (1995–2011) was conducted to identify primary research studies that reported data for pregnancies with definitive prenatal diagnosis of Down syndrome with subsequent pregnancy termination Studies that provided indirect estimates of pregnancy termination, such as mathematical models, were excluded The weighted mean termination rate was calculated across studies

Results
Twenty-four studies were accepted The weighted mean termination rate was 67% (range: 61%–93%) among seven population-based studies, 85% (range: 60%–90%) among nine hospital-based studies, and 50% (range: 0%–100%) among eight anomaly-based studies Evidence suggests that termination rates have decreased in recent years Termination rates also varied with maternal age, gestational age, and maternal race/ethnicity

Conclusion
This systematic review presents the largest synthesis of United States data on termination rates following a prenatal diagnosis of Down syndrome Evidence suggests that termination rates are lower than noted in a previous review that was based on less contemporary studies and had an international focus Heterogeneity across studies suggests that a summary termination rate may not be applicable to the entire US population © 2012 John Wiley & Sons, Ltd

https://obgyn.onlinelibrary.wiley.com/doi/pdf/10.1002/pd.2910

Prenatal diagnosis of Down syndrome: a systematic review of termination rates (1995-2011).

A 37-year-old woman, gravida 1, seeks prenatal care at 8 weeks' gestation. Her family history and medical history are unremarkable. What would you advise regarding her risk of fetal chromosomal abnormalities such as Down's syndrome and her options for prenatal screening and diagnosis?

Clinical practice. Prenatal screening for aneuploidy.

Personalized Genetic Medicine

Diseases such as congenital birth defects, myocardial infarction, cancer, mental illness, diabetes, and Alzheimer disease cause morbidity and premature mortality in nearly two of every three individuals during their lifetimes (Table 8-1). Many of these diseases “run in families”—they seem to recur in the relatives of affected individuals more frequently than in the general population. And yet their inheritance generally does not follow one of the mendelian patterns seen in the single-gene disorders (described in Chapter 7). Instead, they are thought to result from complex interactions between a number of genetic and environmental factors and therefore are said to follow a multifactorial (or complex) inheritance pattern. The familial clustering can be explained by recognizing that family members share a greater proportion of their genetic information and environmental exposures than do individuals chosen at random in the population. Thus, the relatives of an affected individual are more likely to experience the same gene-gene and gene-environment interactions that led to disease in the proband in the fi rst place than are individuals who are unrelated to the proband. The multifactorial inheritance pattern that results represents an interaction between the collective effect of the genotype at one or, more commonly, multiple loci (polygenic or multigenic effects) either to raise or to lower susceptibility to disease, combined with a variety of environmental exposures that may trigger, accelerate, exacerbate, or protect against the disease process. The gene-gene interactions in polygenic inheritance may be simply additive or much more complicated. For example, there may be synergistic amplifi cation of susceptibility by the genotypes at multiple loci or dampening of the effect of genotype at one locus by the genotypes at other loci. Gene-environment interactions, including systematic exposures or chance encounters with environmental factors in one’s surroundings, add even more complexity to individual disease risk and the pattern of disease inheritance. In this chapter, we fi rst address the question of how we determine that genes predispose to common diseases and, therefore, that these diseases are, at least in part, “genetic.” We describe how studies of familial aggregation, twin studies, and estimates of heritability are used by geneticists to quantify the relative contributions of genes and environment to diseases and clinically important physiological measures with complex inheritance. Second, we illustrate the general concept of gene-gene interaction, starting with one of the simplest examples, one in which modifi er genes affect the occurrence or severity of a mendelian disorder. We then give a few examples of more complicated multifactorial diseases in which knowledge of the alleles and loci that confer disease susceptibility is leading to an increased understanding of the mechanisms by which these alleles interact with each other or the environment to cause disease. Unfortunately, we do not understand the underlying mechanisms of the gene-gene and geneenvironment interactions for the majority of complex disorders. Geneticists must therefore continue to rely on empirically derived risk fi gures to give our patients and their relatives some answers to basic questions about disease risk and approaches to reducing that risk. We provide such risk fi gures here but expect that, with

Genetics of Common Disorders with Complex Inheritance

Patterns of Single-Gene Inheritance

Principles of Clinical Cytogenetics

The Treatment of Genetic Disease

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