Likelihood methods have been developed to partition individuals in a sample into sibling clusters using genetic marker data without parental information. Most of these methods assume either both sexes are monogamous to infer full sibships only or only one sex is polygamous to infer full sibships and paternal or maternal (but not both) half sibships. We extend our previous method to the more general case of both sexes being polygamous to infer full sibships, paternal half sibships, and maternal half sibships and to the case of a two-generation sample of individuals to infer parentage jointly with sibships. The extension not only expands enormously the scope of application of the method, but also increases its statistical power. The method is implemented for both diploid and haplodiploid species and for codominant and dominant markers, with mutations and genotyping errors accommodated. The performance and robustness of the method are evaluated by analyzing both simulated and empirical data sets. Our method is shown to be much more powerful than pairwise methods in both parentage and sibship assignments because of the more efficient use of marker information. It is little affected by inbreeding in parents and is moderately robust to nonrandom mating and linkage of markers. We also show that individually much less informative markers, such as SNPs or AFLPs, can reach the same power for parentage and sibship inferences as the highly informative marker simple sequence repeats (SSRs), as long as a sufficient number of loci are employed in the analysis.

https://www.genetics.org/content/genetics/181/4/1579.full.pdf

Parentage and Sibship Inference From Multilocus Genotype Data Under Polygamy

Background: Heritable mutations of the breast cancer gene BRCA1 are rare, occurring in fewer than 1% of women in the general population, and therefore account for a small proportion of cases of breast and ovarian cancers. Nevertheless, the presence of such mutations is highly predictive of the development of these cancers. Purpose: We developed and applied a mathematic model for calculating the probability that a woman with a family history of breast and/or ovarian cancer carries a mutation of BRCA1. Methods and Results: As a basis for the model, we use Mendelian genetics and apply Bayes’ theorem to information on the family history of these diseases. Of importance are the exact relationships of all family members, including both affected and unaffected members, and ages at diagnosis of the affected members and current ages of the unaffected members. We used available estimates of BRCA1 mutation frequencies in the general population and age-specific incidence rates of breast and ovarian cancers in carriers and noncarriers of mutations to estimate the probability that a particular member of the family carries a mutation. This probability is based on cancer statuses of all first- and second-degree relatives. We first describe the model by considering single individuals: a woman diagnosed with breast and/or ovarian cancer and also a woman free of cancer. We next considered two artificial and two actual family histories and addressed the sensitivity of our calculations to various assumptions. Particular relationships of family members with and without cancer can have a substantial impact on the probability of carrying a susceptibility gene. Ages at diagnosis of affected family members and their types of cancer are also important. A woman with two primary cancers can have a probability of carrying a mutation in excess of 80%, even with no other information about family history. The number and relationships of unaffected members, along with their current ages or ages at death, are critical determinants of one’s carrier probability. An affected woman with several cancers in her family can have a probability of carrying a mutation that ranges from close to 100% to less than 5%. Conclusion: Our model gives informative and specific probabilities that a particular woman carries a mutation. Implications: This model focuses on mutations in BRCA1 and assumes that all other breast cancer is sporadic. With the cloning of BRCA2, we now know that this assumption is incorrect. We have adjusted the model to include BRCA2, but the use of this version must await publication of penetrance data for BRCA2, including those for male breast cancer that are apparently associated with BRCA2 but not with BRCA1. The current model is, nevertheless, appropriate and useful. Of principal importance is its potential and that of improved versions for aiding women and their health care providers in assessing the need for genetic testing. [J Natl Cancer Inst 1997;89:227-37] There are approximately 180 000 new cases of breast cancer diagnosed each year in the United States. Approximately 45 000 women die of the disease annually. Although only about 26 000 cases of ovarian cancer are diagnosed each year, proportionately more women (14 000 annually) die of ovarian cancer (1). It is estimated that 2% of all breast cancers and 10% of ovarian cancers occur in women who carry a mutation in the BRCA1 susceptibility gene for breast cancer (2). The prevalence of mutations at BRCA1 has been estimated to be 0.04%-0.20% in the general population (3), but prevalence may vary, depending on racial or ethnic group (4). Approximately 85% of female carriers will develop breast cancer and 63% will develop ovarian cancer by the age of 70 years (2). Moreover, these two cancers seem to be statistically independent (2). In particular, based on these estimates, for female carriers who live to age 70 years, about 95% [o r1˛( 1 ˛0.85) (1 ˛ 0.63)] have developed one or the other cancer and about 54% (or 0.85 ◊ 0.63) have both cancers. The high penetrances of breast and ovarian cancers among women with BRCA1 mutations mean that family history of these diseases is a strong indicator of whether a mutation is present in the family. This article presents a method for finding the probability that a particular family member carries a mutation at BRCA1 on the basis of her family’s history of these two diseases.

/pdf/probability-of-carrying-a-mutation-of-breast-ovarian-cancer-51ud5fa96h.pdf

Probability of Carrying a Mutation of Breast-Ovarian Cancer Gene BRCA1 Based on Family History

Genomic selection (GS) using high-density single-nucleotide polymorphisms (SNPs) is promising to improve response to selection in populations that are under artificial selection. High-density SNP genotyping of all selection candidates each generation, however, may not be cost effective. Smaller panels with SNPs that show strong associations with phenotype can be used, but this may require separate SNPs for each trait and each population. As an alternative, we propose to use a panel of evenly spaced low-density SNPs across the genome to estimate genome-assisted breeding values of selection candidates in pedigreed populations. The principle of this approach is to utilize cosegregation information from low-density SNPs to track effects of high-density SNP alleles within families. Simulations were used to analyze the loss of accuracy of estimated breeding values from using evenly spaced and selected SNP panels compared to using all high-density SNPs in a Bayesian analysis. Forward stepwise selection and a Bayesian approach were used to select SNPs. Loss of accuracy was nearly independent of the number of simulated quantitative trait loci (QTL) with evenly spaced SNPs, but increased with number of QTL for the selected SNP panels. Loss of accuracy with evenly spaced SNPs increased steadily over generations but was constant when the smaller number individuals that are selected for breeding each generation were also genotyped using the high-density SNP panel. With equal numbers of low-density SNPs, panels with SNPs selected on the basis of the Bayesian approach had the smallest loss in accuracy for a single trait, but a panel with evenly spaced SNPs at 10 cM was only slightly worse, whereas a panel with SNPs selected by forward stepwise selection was inferior. Panels with evenly spaced SNPs can, however, be used across traits and populations and their performance is independent of the number of QTL affecting the trait and of the methods used to estimate effects in the training data and are, therefore, preferred for broad applications in pedigreed populations under artificial selection.

https://www.genetics.org/content/genetics/182/1/343.full.pdf

Genomic Selection Using Low-Density Marker Panels

There is a growing need for the development of statistical techniques capable of mapping quantitative trait loci (QTL) in general outbred animal populations. Presently used variance component methods, which correctly account for the complex relationships that may exist between individuals, are challenged by the difficulties incurred through unknown marker genotypes, inbred individuals, partially or unknown marker phases, and multigenerational data. In this article, a two-step variance component approach that enables practitioners to routinely map QTL in populations with the aforementioned difficulties is explored. The performance of the QTL mapping methodology is assessed via its application to simulated data. The capacity of the technique to accurately estimate parameters is examined for a range of scenarios.

/pdf/mapping-quantitative-trait-loci-in-complex-pedigrees-a-two-3zyijqmppm.pdf

Mapping Quantitative Trait Loci in Complex Pedigrees: A Two-Step Variance Component Approach

Among the hereditary systemic amyloidosis, transthyretin (TTR) neuropathies (OMIM #176300) are devastating disorders with an autosomal dominant transmission, expressed mainly as a progressive fibre length dependent sensorimotor polyneuropathy and life threatening autonomic dysfunction.1 Initially, the condition was recognised in northern Portugal, in the area of Povoa de Varzim, where the prevalence has been estimated at 1/1000.2,3 Other important clusters of families were subsequently reported in Japan, initially in two limited geographic areas (the districts of Arao and Ogawa),4 and in the north of Sweden.5 The condition is now known throughout the world, including various European countries.6 Among the 75 pathogenic TTR variants identified so far, Val30Met is the most frequent, and is virtually the only one described in Portugal and Sweden.7,8 By contrast, in Japan, the TTR point mutations are highly heterogeneous, with more than 20 different substitutions, including Val30Met, described.9 In France, a previous study found a similar heterogeneity.10 Depending on the geographic origin, the mean age of onset ranges from 30 to 70 years. A variable clinical expression is also observed, with restrictive cardiomyopathy, carpal tunnel syndrome, and vitreous opacities often encountered, whereas renal involvement is less common. These manifestations, along with the discrepancy in age of onset, account for phenotypic variations. However, virtually no genotype–phenotype correlation seems to exist. To supply the main source of mutant TTR, liver transplantation has been proposed as a treatment for the disease for the last 10 years, with promising results. In the Val30Met patients, it virtually suppresses the production of the mutant TTR in the serum and slows the progression of the neurological symptoms that otherwise have a fatal outcome in less than 10 years.11 Another important aspect of the management of affected families is the recent availability of predictive …

https://jmg.bmj.com/content/jmedgenet/40/11/e120.full.pdf

Genetic study of transthyretin amyloid neuropathies: carrier risks among French and Portuguese families

Contents Vol. 21, 2001

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Contents Vol. 21, 2001