We consider statistics for analyzing a variety of family-based and nonfamily-based designs for detecting linkage disequilibrium of a marker with a disease susceptibility locus. These designs include sibships with parents, sibships without parents, and use of unrelated controls. We also provide formulas for and evaluate the relative power of different study designs using these statistics. In this first paper in the series, we derive statistical tests based on data derived from DNA pooling experiments and describe their characteristics. Although designs based on affected and unaffected sibs without parents are usually robust to population stratification, they suffer a loss of power compared with designs using parents or unrelateds as controls. Although increasing the number of unaffected sibs improves power, the increase is generally not substantial. Designs including sibships with multiple affected sibs are typically the most powerful, with any of these control groups, when the disease allele frequency is low. When the allele frequency is high, however, designs with unaffected sibs as controls do not retain this advantage. In designs with parents, having an affected parent has little impact on the power, except for rare dominant alleles, where the power is increased compared with families with no affected parents. Finally, we also demonstrate that for sibships with parents, only the parents require individual genotyping to derive the TDT statistic, whereas all the offspring can be pooled. This can potentially lead to considerable savings in genotyping, especially for multiplex sibships. The formulas and tables we derive should provide some guidance to investigators designing nuclear family-based linkage disequilibrium studies for complex diseases.

Haplotype analysis of disease chromosomes can help identify probable historical recombination events and localize disease mutations. Most available analyses use only marginal and pairwise allele frequency information. We have developed a Bayesian framework that utilizes full haplotype information to overcome various complications such as multiple founders, unphased chromosomes, data contamination, and incomplete marker data. A stochastic model is used to describe the dependence structure among several variables characterizing the observed haplotypes, for example, the ancestral haplotypes and their ages, mutation rate, recombination events, and the location of the disease mutation. An efficient Markov chain Monte Carlo algorithm was developed for computing the estimates of the quantities of interest. The method is shown to perform well in both real data sets (cystic fibrosis data and Friedreich ataxia data) and simulated data sets. The program that implements the proposed method, BLADE, as well as the two real datasets, can be obtained from http://www.fas.harvard.edu/ ∼junliu/TechRept/01folder/diseq_prog.tar.gz.In the quest to identify genes responsible for specific illnesses, it has been observed in many cases that a large portion of the carriers of the disease gene in the current population are descendant from a small number of "founders" in whose genomes the deleterious mutation appeared some generations ago. This translates into inhomogeneity between the allele frequencies in the general population and those with the disease for genetic markers close to the location of the disease gene(s). The reason is that the allele frequencies of these markers in the disease population still reflect those originally carried by the founder chromosome(s), with modifications introduced by recombinations and mutations. This phenomenon, known as linkage disequilibrium (LD), can be exploited to identify the location of a disease gene by measuring the dependence between disease status and allele distributions among a set of markers.Simply looking at the marginal dependency between each marker and disease status in a case/control sample of chromosomes is clearly inefficient. For an LD mapping strategy to be optimal in fine mapping, it is essential to consider the information observed in a set of contiguous markers (i.e., haplotypes). The primary goal of our Bayesian analysis is the localization of a gene responsible for the disease within the considered set of markers. Secondary goals are the determination of ancestral haplotypes, the separation of distinct founders of the disease, the construction of haplotypes from unphased chromosomes, and inference on the ages of the mutations causing the disease. Our method, like any others based on LD, is appropriate when there are reasons to assume the existence of a founder effect in at least a significant proportion of the diseased individuals. We note that several attempts along the lines of our approach have been discussed in the literature, and we compare these methods with our approa...

IntroductionHypoparathyroidism in pregnancy is rare, but important, as it is associated with maternal morbidity and foetal loss. There are limited case reports and no established management guidelines. Optimal maintenance of calcium levels during pregnancy is required to minimise the risk of related complications. This study aims to identify causes and examine outcomes of hypoparathyroidism in pregnancy in a cohort of women delivering at a large referral centre.Design and methodThe Monash Health maternity service database captures pregnancy and birthing outcomes in over 9000 women each year. We audited this database between 2000 and 2014 to examine the clinical course, treatment and outcomes of pregnant women with hypoparathyroidism.ResultsWe identified 10 pregnancies from 6 women with pre-existing hypoparathyroidism secondary to idiopathic hypoparathyroidism (n = 3), autosomal dominant branchial arch disorder with hypoparathyroidism (n = 3) and autosomal dominant hypocalcaemia (n = 1), surgery for thyroid cancer (n = 2) and Graves' disease (n = 1). Maternal calcium levels were monitored through pregnancy and management adjusted to maintain normocalcaemia. One woman was delivered by caesarean section at 34 weeks' gestation because of intrauterine growth restriction, and oligohydramnios complicated two other pregnancies. The postpartum period was complicated by severe hypercalcaemia in one woman and by symptomatic, labile serum calcium levels during lactation in another woman, requiring close monitoring over a 6 month period.ConclusionAlthough rare, hypoparathyroidism in pregnancy poses a management challenge for clinicians, and co-ordinated care is required by obstetricians and endocrinologists to ensure optimal outcomes for both mother and baby. Continued monitoring of maternal calcium levels during lactation and weaning is essential to avoid the potential complications of either hypercalcaemia or hypocalcaemia.

The authors describe a rapid and low-cost approach for multiplex microRNA(miRNA) assay on lateral flow nucleic acid biosensor (LFNAB). The principle of assay is based on sandwich-type nucleic acid hybridization reactions to produce gold nanoparticle (GNP)-attached complexes (ssDNA-microRNA-ssDNA/GNPs), which are captured and visualized on the test zone of LFNAB. By designing three different test zones on LFNAB, simultaneous detection of microRNA-21, microRNA-155 and microRNA-210 was achieved with an adding-measuring model by using GNP as visual tag. The method was challenged by testing the microRNAs in spiked serum samples with satisfied results. In our perception, the test is a particularly valuable tool for clinical application and biomedical diagnosis, particularly in limited resource settings.

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